Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Departamento de Salud Pública, Facultad de Medicina, Universidad Nacional de Colombia, Bogotá, D.C., Colombia.
Sci Adv. 2022 Jul 15;8(28):eabo0173. doi: 10.1126/sciadv.abo0173. Epub 2022 Jul 13.
Genomics is fundamentally changing epidemiological research. However, systematically exploring hypotheses in pathogen evolution requires new modeling tools. Models intertwining pathogen epidemiology and genomic evolution can help understand processes such as the emergence of novel pathogen genotypes with higher transmissibility or resistance to treatment. In this work, we present Opqua, a flexible simulation framework that explicitly links epidemiology to sequence evolution and selection. We use Opqua to study determinants of evolution across fitness valleys. We confirm that competition can limit evolution in high-transmission environments and find that low transmission, host mobility, and complex pathogen life cycles facilitate reaching new adaptive peaks through population bottlenecks and decoupling of selective pressures. The results show the potential of genomic epidemiological modeling as a tool in infectious disease research.
基因组学正在从根本上改变流行病学研究。然而，系统地探索病原体进化中的假说需要新的建模工具。将病原体流行病学和基因组进化联系起来的模型可以帮助我们理解新的病原体基因型的出现等过程，这些新的病原体基因型具有更高的传染性或对治疗的抵抗力。在这项工作中，我们提出了 Opqua，这是一个灵活的模拟框架，可以明确地将流行病学与序列进化和选择联系起来。我们使用 Opqua 来研究跨越适应度低谷的进化决定因素。我们证实，竞争可以限制高传播环境中的进化，并且发现低传播、宿主迁移和复杂的病原体生命周期通过种群瓶颈和选择压力的解耦，促进了新的适应性峰的出现。结果表明，基因组流行病学建模作为传染病研究的工具具有潜力。
