The evolutionary cost of homophily: Social stratification facilitates stable variant coexistence and increased rates of evolution in host-associated pathogens.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Shuanger Li, Davorka Gulisija, Oana Carja
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引用次数: 0

Abstract

Coexistence of multiple strains of a pathogen in a host population can present significant challenges to vaccine development or treatment efficacy. Here we discuss a novel mechanism that can increase rates of long-lived strain polymorphism, rooted in the presence of social structure in a host population. We show that social preference of interaction, in conjunction with differences in immunity between host subgroups, can exert varying selection pressure on pathogen strains, creating a balancing mechanism that supports stable viral coexistence, independent of other known mechanisms. We use population genetic models to study rates of pathogen heterozygosity as a function of population size, host population composition, mutant strain fitness differences and host social preferences of interaction. We also show that even small periodic epochs of host population stratification can lead to elevated strain coexistence. These results are robust to varying social preferences of interaction, overall differences in strain fitnesses, and spatial heterogeneity in host population composition. Our results highlight the role of host population social stratification in increasing rates of pathogen strain diversity, with effects that should be considered when designing policies or treatments with a long-term view of curbing pathogen evolution.

嗜同性的进化代价:社会分层有利于宿主相关病原体的稳定变异共存和进化速度加快。
病原体的多种菌株在宿主群体中共存会给疫苗开发或治疗效果带来巨大挑战。在这里,我们讨论了一种可提高长效菌株多态性发生率的新机制,其根源在于宿主种群中存在社会结构。我们的研究表明,互动的社会偏好与宿主亚群之间免疫力的差异可对病原体毒株施加不同的选择压力,从而形成一种支持病毒稳定共存的平衡机制,而不受其他已知机制的影响。我们利用种群遗传模型研究了病原体杂合率与种群规模、宿主种群组成、突变株适合度差异和宿主社会互动偏好的函数关系。我们还表明,即使宿主种群分层的周期性小时间段也会导致菌株共存率升高。这些结果对不同的相互作用社会偏好、菌株体能的总体差异以及宿主种群组成的空间异质性都是稳健的。我们的研究结果凸显了宿主种群社会分层在提高病原体菌株多样性方面的作用,在设计长期遏制病原体进化的政策或疗法时应考虑到这种作用。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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