Department of Environmental Science, Policy and Management, University of California, Berkeley, California, United States of America.
PLoS One. 2011;6(8):e22685. doi: 10.1371/journal.pone.0022685. Epub 2011 Aug 1.
Using DNA sequence data from pathogens to infer transmission networks has traditionally been done in the context of epidemics and outbreaks. Sequence data could analogously be applied to cases of ubiquitous commensal bacteria; however, instead of inferring chains of transmission to track the spread of a pathogen, sequence data for bacteria circulating in an endemic equilibrium could be used to infer information about host contact networks. Here, we show--using simulated data--that multilocus DNA sequence data, based on multilocus sequence typing schemes (MLST), from isolates of commensal bacteria can be used to infer both local and global properties of the contact networks of the populations being sampled. Specifically, for MLST data simulated from small-world networks, the small world parameter controlling the degree of structure in the contact network can robustly be estimated. Moreover, we show that pairwise distances in the network--degrees of separation--correlate with genetic distances between isolates, so that how far apart two individuals in the network are can be inferred from MLST analysis of their commensal bacteria. This result has important consequences, and we show an example from epidemiology: how this result could be used to test for infectious origins of diseases of unknown etiology.
利用病原体的 DNA 序列数据来推断传播网络，传统上是在流行病和疫情的背景下进行的。序列数据也可以类似地应用于无处不在的共生细菌的情况；然而，与推断传播链以追踪病原体的传播不同，在地方病平衡中循环的细菌的序列数据可用于推断有关宿主接触网络的信息。在这里，我们使用模拟数据表明，基于多位点序列分型方案（MLST）的共生细菌分离株的多位点 DNA 序列数据可用于推断被采样人群的接触网络的局部和全局特性。具体来说，对于从小世界网络模拟的 MLST 数据，可以稳健地估计控制接触网络结构程度的小世界参数。此外，我们还表明，网络中的成对距离（分离度）与分离株之间的遗传距离相关，因此可以从共生细菌的 MLST 分析中推断出网络中两个人之间的距离。这一结果具有重要意义，我们从流行病学中举了一个例子：这一结果如何用于测试未知病因疾病的传染性起源。
