Grant Andrew J, Restif Olivier, McKinley Trevelyan J, Sheppard Mark, Maskell Duncan J, Mastroeni Pietro
Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
PLoS Biol. 2008 Apr 8;6(4):e74. doi: 10.1371/journal.pbio.0060074.
Mechanistic determinants of bacterial growth, death, and spread within mammalian hosts cannot be fully resolved studying a single bacterial population. They are also currently poorly understood. Here, we report on the application of sophisticated experimental approaches to map spatiotemporal population dynamics of bacteria during an infection. We analyzed heterogeneous traits of simultaneous infections with tagged Salmonella enterica populations (wild-type isogenic tagged strains [WITS]) in wild-type and gene-targeted mice. WITS are phenotypically identical but can be distinguished and enumerated by quantitative PCR, making it possible, using probabilistic models, to estimate bacterial death rate based on the disappearance of strains through time. This multidisciplinary approach allowed us to establish the timing, relative occurrence, and immune control of key infection parameters in a true host-pathogen combination. Our analyses support a model in which shortly after infection, concomitant death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs, a process controlled by host antimicrobial mechanisms. Later, decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally, with subsequent mixing of bacteria between organs via bacteraemia and further stochastic selection. This approach provides us with an unprecedented outlook on the pathogenesis of S. enterica infections, illustrating the complex spatial and stochastic effects that drive an infectious disease. The application of the novel method that we present in appropriate and diverse host-pathogen combinations, together with modelling of the data that result, will facilitate a comprehensive view of the spatial and stochastic nature of within-host dynamics.
仅研究单一细菌群体，无法完全解析细菌在哺乳动物宿主体内生长、死亡及传播的机制性决定因素。目前，人们对此也知之甚少。在此，我们报告了运用精密实验方法来描绘感染过程中细菌的时空群体动态。我们分析了野生型和基因靶向小鼠同时感染标记的肠炎沙门氏菌群体（野生型同基因标记菌株 [WITS]）的异质性特征。WITS在表型上相同，但可通过定量PCR进行区分和计数，这使得利用概率模型根据菌株随时间的消失情况来估计细菌死亡率成为可能。这种多学科方法使我们能够在真实的宿主 - 病原体组合中确定关键感染参数的时间、相对发生率和免疫控制情况。我们的分析支持这样一种模型：感染后不久，伴随的死亡和细菌的快速复制导致在不同器官中建立独立的细菌亚群，这一过程受宿主抗菌机制控制。随后，微生物死亡率降低导致局部传播的细菌数量呈指数增长，随后细菌通过菌血症在器官之间混合，并进一步进行随机选择。这种方法为我们提供了关于肠炎沙门氏菌感染发病机制前所未有的见解，阐明了驱动传染病的复杂空间和随机效应。我们所展示的新方法在合适且多样的宿主 - 病原体组合中的应用，以及对所得数据的建模，将有助于全面了解宿主体内动态的空间和随机性质。
