Understanding the limits to generalizability of experimental evolutionary models

Forde, Samantha E.; Beardmore, Robert; Gudelj, Ivana; Arkin, Sinan Sadi; Thompson, John N.; Hurst, Laurence D.

doi:10.1038/nature07152

Cited by 49 publications

(60 citation statements)

References 20 publications

(20 reference statements)

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“…In addition, the frequent occurrence of local adaptation indicates that there may be multiple routes to generalism (Buckling and Rainey 2002b;Morgan et al 2005;Vos et al 2009;Koskella et al 2011). These data indicate that the GFG framework may only be capturing part of the genetic interactions between bacteria and phages, which has led others to propose more complex specificities (Agrawal andLively 2002, 2003;Weitz et al 2005;Forde et al 2008;Fenton et al 2012). …”

Section: Discussionmentioning

confidence: 99%

“…Under the GFG framework, parasites must match or exceed the host's resistance alleles at each locus to have a high probability of causing an infection, which naturally leads to the evolution of generalism in the form of broader resistance and infectivity ranges. These dynamics have been observed in a variety of real host-parasite relationships, including bacterium-phage (Bohannan and Lenski 2000;Buckling and Rainey 2002a;Mizoguchi et al 2003;Brockhurst et al 2006;Forde et al 2008;, plant-pathogen (Flor 1956;Thompson and Burdon 1992;Thrall and Burdon 2003) and nematode-bacterium systems (Schulte et al 2010). Recent studies of bacterium-phage coevolution have found that infectivity range is correlated with the number of amino acid changes in tail fibers relative to the ancestral genotype , providing further support for the GFG framework.…”

Section: Introductionmentioning

confidence: 91%

“…While there is considerable evidence for coevolutionary arms races taking place among bacteria and phages (Bohannan and Lenski 2000;Buckling and Rainey 2002a;Mizoguchi et al 2003;Brockhurst et al 2006;Forde et al 2008;Scanlan et al 2011) and various other host-parasite systems (Little et al 2006;Schulte et al 2010), there is limited evidence of fluctuations in range except for some plantfungus interactions (e.g., Thrall and Burdon 2003). Recent work with Pseudomonas fluorescens and lytic phages has shown that fluctuating selection between genotypes with similar ranges is possible, either following or in the absence of a coevolutionary arms race (Gomez and Buckling 2011).…”

Section: Discussionmentioning

confidence: 99%

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Spatial Structure Mitigates Fitness Costs in Host-Parasite Coevolution

Ashby

Gupta

Buckling

2014

The American Naturalist

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Online enhancements: appendixes, code files. abstract:The extent of population mixing is known to influence the coevolutionary outcomes of many host and parasite traits, including the evolution of generalism (the ability to resist or infect a broad range of genotypes). While the segregation of populations into interconnected demes has been shown to influence the evolution of generalism, the role of local interactions between individuals is unclear. Here, we combine an individual-based model of microbial communities with a well-established framework of genetic specificity that matches empirical observations of bacterium-phage interactions. We find the evolution of generalism in well-mixed populations to be highly sensitive to the severity of associated fitness costs, but the constraining effect of costs on the evolution of generalism is lessened in spatially structured populations. The contrasting outcomes between the two environments can be explained by different scales of competition (i.e., global vs. local). These findings suggest that local interactions may have important effects on the evolution of generalism in host-parasite interactions, particularly in the presence of high fitness costs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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Spatial Structure Mitigates Fitness Costs in Host-Parasite Coevolution

Ashby

Gupta

Buckling

2014

The American Naturalist

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“…If phage-bacterial molecular interactions are dominated by single traits and variation in these traits is constrained along a single hierarchical dimension such as LPS, then one should expect the nested pattern to arise. There are other examples of traits with physical characteristics that behave similarly: bacteria that evolve a thicker and thicker protective coating (74), phages that evolve increased host range by continually reducing tail length (73), bacteria that reduce their number of receptors, and phages that target fewer receptors (75). Although there are many examples of this type of one-dimensional interaction, the problem with this finding being a universal explanation for the form of bacterial-phage interactions are that host-phage interactions are governed by hundreds of other genes (76), bacteria can use multiple strategies for resistance (74), and phages have complex mechanism to evade bacteria defenses (74,77).…”

Section: Discussionmentioning

confidence: 99%

“…Phages infect bacteria by using specialized proteins that target and bind to molecules on the outer membranes of bacteria (receptor molecules). Nested infection matrices have been shown for T-phages, which infect strains of E. coli, to be the result of the interactions of the phage proteins and receptor molecules (73). T-phages bind to the lipopolysaccharide (LPS) chains on the cell surface.…”

Section: Discussionmentioning

confidence: 99%

Statistical structure of host–phage interactions

Flores

Meyer

Valverde

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

289

356

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Interactions between bacteria and the viruses that infect them (i.e., phages) have profound effects on biological processes, but despite their importance, little is known on the general structure of infection and resistance between most phages and bacteria. For example, are bacteria-phage communities characterized by complex patterns of overlapping exploitation networks, do they conform to a more ordered general pattern across all communities, or are they idiosyncratic and hard to predict from one ecosystem to the next? To answer these questions, we collect and present a detailed metaanalysis of 38 laboratory-verified studies of hostphage interactions representing almost 12,000 distinct experimental infection assays across a broad spectrum of taxa, habitat, and mode of selection. In so doing, we present evidence that currently available host-phage infection networks are statistically different from random networks and that they possess a characteristic nested structure. This nested structure is typified by the finding that hard to infect bacteria are infected by generalist phages (and not specialist phages) and that easy to infect bacteria are infected by generalist and specialist phages. Moreover, we find that currently available host-phage infection networks do not typically possess a modular structure. We explore possible underlying mechanisms and significance of the observed nested host-phage interaction structure. In addition, given that most of the available hostphage infection networks examined here are composed of taxa separated by short phylogenetic distances, we propose that the lack of modularity is a scale-dependent effect, and then, we describe experimental studies to test whether modular patterns exist at macroevolutionary scales.bacteriophage | complex networks | ecology | evolution | nestedness

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A model of algal‐virus population dynamics reveals underlying controls on material transfer

Hinson

Papoulis

Fiet

et al. 2022

Limnology & Oceanography

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The influence of viruses on nutrient cycles and energy transfer in aquatic systems is important, yet still being determined. We developed a dynamic model to capture the population dynamics of algal hosts and their viruses. The model was fitted to literature data of population dynamics during laboratory one‐step infection experiments. Model parameters that underlie population dynamics were quantified in diverse algal groups, covering 7 different algal classes, 14 different host genera, and 32 different virus genotypes. The hypothesis that trade‐offs exist between traits that underlie population dynamics was evaluated. We report a possible virus size‐dependent trade‐off between the rate at which viruses can initiate infection, and the efficiency of carbon transfer from hosts to viruses upon lysis. We hypothesize that slower rates of infection in large viruses, due to slower rates of molecular diffusion, are compensated by enhanced utilization of host resources. Our approach provides a quantitative trait‐framework for understanding and quantifying virus activity in diverse natural communities.

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Understanding the limits to generalizability of experimental evolutionary models

Cited by 49 publications

References 20 publications

Spatial Structure Mitigates Fitness Costs in Host-Parasite Coevolution

Spatial Structure Mitigates Fitness Costs in Host-Parasite Coevolution

Statistical structure of host–phage interactions

A model of algal‐virus population dynamics reveals underlying controls on material transfer

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