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A general framework for modelling the impact of co-infections on pathogen evolution.

ABSTRACT: Theoretical models suggest that mixed-strain infections, or co-infections, are an important driver of pathogen evolution. However, the within-host dynamics of co-infections vary enormously, which complicates efforts to develop a general understanding of how co-infections affect evolution. Here, we develop a general framework which condenses the within-host dynamics of co-infections into a few key outcomes, the most important of which is the overall R₀ of the co-infection. Similar to how fitness is determined by two different alleles in a heterozygote, the R₀ of a co-infection is a product of the R₀ values of the co-infecting strains, shaped by the interaction of those strains at the within-host level. Extending the analogy, we propose that the overall R₀ reflects the dominance of the co-infecting strains, and that the ability of a mutant strain to invade a population is a function of its dominance in co-infections. To illustrate the utility of these concepts, we use a within-host model to show how dominance arises from the within-host dynamics of a co-infection, and then use an epidemiological model to demonstrate that dominance is a robust predictor of the ability of a mutant strain to save a maladapted wild-type strain from extinction (evolutionary emergence).

SUBMITTER: Bushman M

PROVIDER: S-EPMC6597765 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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A general framework for modelling the impact of co-infections on pathogen evolution.

Bushman Mary M Antia Rustom R

Journal of the Royal Society, Interface 20190626 155

Theoretical models suggest that mixed-strain infections, or co-infections, are an important driver of pathogen evolution. However, the within-host dynamics of co-infections vary enormously, which complicates efforts to develop a general understanding of how co-infections affect evolution. Here, we develop a general framework which condenses the within-host dynamics of co-infections into a few key outcomes, the most important of which is the overall R<sub>0</sub> of the co-infection. Similar to h ...[more]

PMID: 31238835

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Project description:When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of IGEs and variability, as the regression coefficient can respond to selection. Our simulations show that the model results in increased variability of body weight with increasing competition. When competition decreases, i.e., cooperation evolves, variability becomes significantly smaller. Hence, our model facilitates quantitative genetic studies on the relationship between IGEs and inherited variability. Moreover, our findings suggest that we may have been overlooking an entire level of genetic variation in variability, the one due to IGEs.

Project description:BackgroundOnly a few studies have examined the presence of Anaplasma marginale and Anaplasma centrale in South Africa, and no studies have comprehensively examined these species across the whole country. To undertake this country-wide study we adapted a duplex quantitative real-time PCR (qPCR) assay for use in South Africa but found that one of the genes on which the assay was based was variable. Therefore, we sequenced a variety of field samples and tested the assay on the variants detected. We used the assay to screen 517 cattle samples sourced from all nine provinces of South Africa, and subsequently examined A. marginale positive samples for msp1α genotype to gauge strain diversity.ResultsAlthough the A. marginale msp1β gene is variable, the qPCR functions at an acceptable efficiency. The A. centrale groEL gene was not variable within the qPCR assay region. Of the cattle samples screened using the assay, 57% and 17% were found to be positive for A. marginale and A. centrale, respectively. Approximately 15% of the cattle were co-infected. Msp1α genotyping revealed 36 novel repeat sequences. Together with data from previous studies, we analysed the Msp1a repeats from South Africa where a total of 99 repeats have been described that can be attributed to 190 msp1α genotypes. While 22% of these repeats are also found in other countries, only two South African genotypes are also found in other countries; otherwise, the genotypes are unique to South Africa.ConclusionsAnaplasma marginale was prevalent in the Western Cape, KwaZulu-Natal and Mpumalanga and absent in the Northern Cape. Anaplasma centrale was prevalent in the Western Cape and KwaZulu-Natal and absent in the Northern Cape and Eastern Cape. None of the cattle in the study were known to be vaccinated with A. centrale, so finding positive cattle indicates that this organism appears to be naturally circulating in cattle. A diverse population of A. marginale strains are found in South Africa, with some msp1α genotypes widely distributed across the country, and others appearing only once in one province. This diversity should be taken into account in future vaccine development studies.

Dataset Information

A general framework for modelling the impact of co-infections on pathogen evolution.

Publications

A general framework for modelling the impact of co-infections on pathogen evolution.

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