Modelling within-host spatiotemporal dynamics of invasive bacterial disease.
Modelling within-host spatiotemporal dynamics of invasive bacterial disease.
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DOI：
10.1371/journal.pbio.0060074
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发表时间：
2008-04-08
期刊：
影响因子：
9.8
通讯作者：
Mastroeni P
中科院分区：
文献类型：
--
作者：
Grant AJ;Restif O;McKinley TJ;Sheppard M;Maskell DJ;Mastroeni P
细菌生长、死亡和在哺乳动物宿主内传播的机制决定因素不能通过研究单个细菌群体而完全解决。目前对它们也知之甚少。在这里，我们报告应用先进的实验方法来映射感染过程中细菌的时空种群动态。我们分析了在野生型和基因靶向小鼠中同时感染标记的肠道沙门氏菌种群（野生型等基因标记菌株[WITS]）的异质性特征。WITS在表型上是相同的，但可以通过定量PCR进行区分和计数，从而可以使用概率模型基于菌株随时间的消失来估计细菌死亡率。这种多学科的方法使我们能够建立一个真正的宿主-病原体组合的时间，相对发生率和关键感染参数的免疫控制。我们的分析支持一种模型，在该模型中，感染后不久，伴随的死亡和快速细菌复制导致在不同器官中建立独立的细菌亚群，这是由宿主抗菌机制控制的过程。随后，微生物死亡率降低导致局部传播的细菌数量呈指数级增加，随后通过菌血症和进一步的随机选择在器官之间混合细菌。这种方法为我们提供了一个前所未有的前景的发病机制，S。肠道感染，说明了驱动传染病的复杂的空间和随机效应。我们提出的新方法，在适当的和不同的主机-病原体的组合，加上建模的数据，结果的应用，将有利于全面了解的空间和随机性质的主机内动态。细菌在哺乳动物中传播的全球模式和机制决定因素很难通过单个细菌种群的数字和地形图来获得。在感染过程中真正的致病事件的欣赏需要基于对控制同一宿主中各个亚群的感染动力学的精细相互作用的理解。我们已经使用分子技术来标记单独的，否则相同的细菌亚群。我们使用这些细菌，称为野生型等基因标记菌株（WITS），在同一动物中同时感染，以收集对组织中细菌个体亚群传播模式以及细菌和吞噬细胞之间相互作用的见解。结合数值波动的WITS人口的数学建模和统计分析，我们已经收集了数据的细菌生长和死亡的相对发生率在疾病过程的不同阶段。我们的分析支持一种模型，在该模型中，感染后不久，伴随的死亡和快速细菌复制导致在不同器官中建立独立的细菌亚群。后来，微生物死亡率的降低导致局部传播的细菌数量呈指数级增加，随后细菌在器官之间混合。这项工作说明了解开感染的异质性特征对重建和理解全球疾病过程的真实性质的重要性。遗传上相同的细菌菌株揭示了感染期间细菌亚群与宿主体内免疫系统的群体动力学和相互作用。
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. Global patterns and mechanistic determinants of bacterial spread in mammalian organisms are difficult to obtain through numerical and topographical mapping of a single bacterial population. Appreciation of the true pathogenetic events during infections needs to be based on the understanding of the fine interactions that control the infection dynamics of individual subpopulations in the same host. We have used molecular techniques to tag individually otherwise identical subpopulations of bacteria. We have used these bacteria, called wild-type isogenic tagged strains (WITS), in simultaneous infections in the same animal to gather insights into the patterns of spread of individual subpopulations of bacteria in the tissues and interactions between bacteria and phagocytes. Combining numerical fluctuation in the WITS populations with mathematical modelling and statistical analysis, we have gathered data on the relative occurrence of bacterial growth and death in different phases of the disease process. 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. Later, decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally, with subsequent mixing of bacteria between organs. The work illustrates the importance of unravelling heterogeneous traits of infections to reconstruct and understand the true nature of the global disease process. Genetically identical bacterial strains reveal the population dynamics and interactions of subpopulations of bacteria with the host's immune system in vivo during infection.
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影响因子：
15.3
作者：
Vazquez-Torres, A;Jones-Carson, J;Mastroeni, P;Ischiropoulos, H;Fang, F C
通讯作者：
Fang, F C
影响因子：
15.3
作者：
Mastroeni, P;Vazquez-Torres, A;Fang, F C;Xu, Y;Khan, S;Hormaeche, C E;Dougan, G
通讯作者：
Dougan, G
影响因子：
14.9
作者：
Muyrers, JPP;Zhang, YM;Stewart, AF
通讯作者：
Stewart, AF
影响因子：
15.3
作者：
Barnes, Penelope D.;Bergman, Molly A.;Isberg, Ralph R.
通讯作者：
Isberg, Ralph R.
影响因子：
3.8
作者：
DUNLAP, NE;BENJAMIN, WH;BRILES, DE
通讯作者：
BRILES, DE