基因驱动因素的空间传播和持久性受到人口反馈、密度依赖性和阿利效应的影响

bioRxiv Pub Date : 2024-08-08 DOI:10.1101/2024.08.08.607064
Léna Kläy, Léo Girardin, Vincent Calvez, F. Débarre
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引用次数: 0

摘要

基因驱动等位基因会将野生型等位基因转化为驱动等位基因,从而使其自身的传播产生偏差。如果在自然种群中引入这些等位基因,即使它们是有害的,也可能在相对较少的世代内固定下来。迄今为止,还没有工程化的同源基因驱动生物被释放到野外,因此建立模型对于清楚地了解此类释放的潜在结果至关重要。我们使用确定性模型来研究不同的人口特征如何影响基因驱动的空间传播。在以往工作的基础上,我们首先考虑了内在种群增长率对基因驱动传播的影响。我们证实,与忽略种群规模变化的模型相比,加入人口动态因素会改变结果,因为种群密度的变化会影响驱动基因的空间传播。其次,我们研究了加入阿利效应的后果,发现阿利效应会使种群在驱动力扩散后更容易被消灭。最后,我们研究了密度依赖性所影响的适应性成分(繁殖力或存活率),发现它会影响驱动力在空间入侵的速度,并可能加剧阿利效应的后果。这些结果证实了检查模型结果对基本假设变化的稳健性的重要性,特别是如果模型要用于基因驱动风险评估的话。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The spatial spread and the persistence of gene drives are affected by demographic feedbacks, density dependence and Allee effects
Homing gene drive alleles bias their own transmission by converting wild-type alleles into drive alleles. If introduced in a natural population, they might fix within a relatively small number of generations, even if they are deleterious. No engineered homing gene drive organisms have been released in the wild so far, and modelling is essential to develop a clear understanding of the potential outcomes of such releases. We use deterministic models to investigate how different demographic features affect the spatial spread of a gene drive. Building on previous work, we first consider the effect of the intrinsic population growth rate on drive spread. We confirm that including demographic dynamics can change outcomes compared to a model ignoring changes in population sizes, because changes in population density can oppose the spatial spread of a drive. Secondly, we study the consequences of including an Allee effect, and find that it makes a population more prone to eradication following drive spread. Finally, we investigate the effects of the fitness component on which density dependence operates (fecundity or survival), and find that it affects the speed of drive invasion in space, and can accentuate the consequences of an Allee effect. These results confirm the importance of checking the robustness of model outcomes to changes in the underlying assumptions, especially if models are to be used for gene drive risk assessment.
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