Complex Systems Lagrange Lab, Institute for Scientific Interchange-ISI, Torino, Italy.
PLoS One. 2012;7(9):e44849. doi: 10.1371/journal.pone.0044849. Epub 2012 Sep 20.
A new word, phylodynamics, was coined to emphasize the interconnection between phylogenetic properties, as observed for instance in a phylogenetic tree, and the epidemic dynamics of viruses, where selection, mediated by the host immune response, and transmission play a crucial role. The challenges faced when investigating the evolution of RNA viruses call for a virtuous loop of data collection, data analysis and modeling. This already resulted both in the collection of massive sequences databases and in the formulation of hypotheses on the main mechanisms driving qualitative differences observed in the (reconstructed) evolutionary patterns of different RNA viruses. Qualitatively, it has been observed that selection driven by the host immune response induces an uneven survival ability among co-existing strains. As a consequence, the imbalance level of the phylogenetic tree is manifestly more pronounced if compared to the case when the interaction with the host immune system does not play a central role in the evolutive dynamics. While many imbalance metrics have been introduced, reliable methods to discriminate in a quantitative way different level of imbalance are still lacking. In our work, we reconstruct and analyze the phylogenetic trees of six RNA viruses, with a special emphasis on the human Influenza A virus, due to its relevance for vaccine preparation as well as for the theoretical challenges it poses due to its peculiar evolutionary dynamics. We focus in particular on topological properties. We point out the limitation featured by standard imbalance metrics, and we introduce a new methodology with which we assign the correct imbalance level of the phylogenetic trees, in agreement with the phylodynamics of the viruses. Our thorough quantitative analysis allows for a deeper understanding of the evolutionary dynamics of the considered RNA viruses, which is crucial in order to provide a valuable framework for a quantitative assessment of theoretical predictions.
一个新的术语,系统发生动力学生物学,被创造出来强调系统发生特性与病毒流行动力学之间的相互联系,例如在系统发生树中观察到的那样,其中选择、宿主免疫反应介导以及传播起着至关重要的作用。在研究 RNA 病毒进化时面临的挑战需要数据收集、数据分析和建模的良性循环。这已经导致了大规模序列数据库的收集,并提出了关于驱动不同 RNA 病毒(重建的)进化模式中观察到的定性差异的主要机制的假设。从定性上看,已经观察到宿主免疫反应驱动的选择导致共存菌株之间的生存能力不均衡。因此,如果与宿主免疫系统的相互作用在进化动态中不发挥核心作用,系统发生树的不平衡程度明显更为明显。虽然已经引入了许多不平衡度量标准,但仍然缺乏可靠的方法来定量区分不同水平的不平衡。在我们的工作中,我们重建和分析了六种 RNA 病毒的系统发生树,特别关注人类流感 A 病毒,因为它对疫苗制备具有重要意义,并且由于其特殊的进化动态而带来了理论挑战。我们特别关注拓扑性质。我们指出了标准不平衡度量标准的局限性,并引入了一种新的方法,我们用该方法为系统发生树分配正确的不平衡水平,与病毒的系统发生动力学生物学一致。我们的全面定量分析有助于更深入地了解所考虑的 RNA 病毒的进化动态,这对于提供对理论预测进行定量评估的有价值框架至关重要。