Numminen Elina, Gutmann Michael, Shubin Mikhail, Marttinen Pekka, Méric Guillaume, van Schaik Willem, Coque Teresa M, Baquero Fernando, Willems Rob J L, Sheppard Samuel K, Feil Edward J, Hanage William P, Corander Jukka
Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland; Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Finland.
J Theor Biol. 2016 May 7;396:53-62. doi: 10.1016/j.jtbi.2016.02.019. Epub 2016 Feb 23.
Many key bacterial pathogens are frequently carried asymptomatically, and the emergence and spread of these opportunistic pathogens can be driven, or mitigated, via demographic changes within the host population. These inter-host transmission dynamics combine with basic evolutionary parameters such as rates of mutation and recombination, population size and selection, to shape the genetic diversity within bacterial populations. Whilst many studies have focused on how molecular processes underpin bacterial population structure, the impact of host migration and the connectivity of the local populations has received far less attention. A stochastic neutral model incorporating heightened local transmission has been previously shown to fit closely with genetic data for several bacterial species. However, this model did not incorporate transmission limiting population stratification, nor the possibility of migration of strains between subpopulations, which we address here by presenting an extended model. We study the consequences of migration in terms of shared genetic variation and show by simulation that the previously used summary statistic, the allelic mismatch distribution, can be insensitive to even large changes in microepidemic and migration rates. Using likelihood-free inference with genotype network topological summaries we fit a simpler model to commensal and hospital samples from the common nosocomial pathogens Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis and Enterococcus faecium. Only the hospital data for E. faecium display clearly marked deviations from the model predictions which may be attributable to its adaptation to the hospital environment.
许多关键的细菌病原体常常在无症状的情况下被携带，这些机会性病原体的出现和传播可通过宿主群体内的人口结构变化来推动或缓解。这些宿主间的传播动态与基本的进化参数（如突变率、重组率、种群大小和选择）相结合，塑造了细菌群体内的遗传多样性。虽然许多研究聚焦于分子过程如何支撑细菌种群结构，但宿主迁移以及当地种群的连通性所产生的影响却很少受到关注。先前已证明，一个纳入增强的局部传播的随机中性模型与几种细菌物种的遗传数据非常吻合。然而，该模型未纳入限制传播的种群分层，也未考虑菌株在亚种群之间迁移的可能性，我们在此通过提出一个扩展模型来解决这些问题。我们从共享遗传变异的角度研究迁移的后果，并通过模拟表明，先前使用的汇总统计量——等位基因错配分布，甚至对微流行和迁移率的大幅变化也可能不敏感。我们使用基于基因型网络拓扑摘要的无似然推断，将一个更简单的模型拟合到常见医院病原体金黄色葡萄球菌、表皮葡萄球菌、粪肠球菌和屎肠球菌的共生样本和医院样本中。只有屎肠球菌的医院数据显示出明显偏离模型预测的情况，这可能归因于其对医院环境的适应性。
