School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332.
Emory-Children's Cystic Fibrosis Center, Atlanta, GA 30322.
Proc Natl Acad Sci U S A. 2018 May 1;115(18):4779-4784. doi: 10.1073/pnas.1719317115. Epub 2018 Apr 17.
Quorum sensing (QS) is a bacterial communication system that involves production and sensing of extracellular signals. In laboratory models, QS allows bacteria to monitor and respond to their own cell density and is critical for fitness. However, how QS proceeds in natural, spatially structured bacterial communities is not well understood, which significantly hampers our understanding of the emergent properties of natural communities. To address this gap, we assessed QS signaling in the opportunistic pathogen in a cystic fibrosis (CF) lung infection model that recapitulates the biogeographical aspects of the natural human infection. In this model, grows as spatially organized, highly dense aggregates similar to those observed in the human CF lung. By combining this natural aggregate system with a micro-3D-printing platform that allows for confinement and precise spatial positioning of aggregates, we assessed the impact of aggregate size and spatial positioning on both intra- and interaggregate signaling. We discovered that aggregates containing ∼2,000 signal-producing were unable to signal neighboring aggregates, while those containing ≥5,000 cells signaled aggregates as far away as 176 µm. Not all aggregates within this "calling distance" responded, indicating that aggregates have differential sensitivities to signal. Overexpression of the signal receptor increased aggregate sensitivity to signal, suggesting that the ability of aggregates to respond is defined in part by receptor levels. These studies provide quantitative benchmark data for the impact of spatial arrangement and phenotypic heterogeneity on signaling in vivo.
群体感应(QS)是一种细菌通讯系统,涉及细胞外信号的产生和感应。在实验室模型中,QS 允许细菌监测和响应自身细胞密度,对适应性至关重要。然而,QS 在自然的、具有空间结构的细菌群落中是如何进行的还不太清楚,这极大地阻碍了我们对自然群落涌现特性的理解。为了解决这一差距,我们在囊性纤维化(CF)肺部感染模型中评估了机会性病原体 中的 QS 信号,该模型再现了自然人类感染的生物地理方面。在这个模型中, 作为空间组织的、高度密集的聚集体生长,类似于在人类 CF 肺部中观察到的聚集体。通过将这个自然的聚集体系统与一个微 3D 打印平台结合起来,该平台允许 聚集体的限制和精确的空间定位,我们评估了聚集体大小和空间定位对聚集体内和聚集体间信号的影响。我们发现,含有约 2000 个产生信号的 细胞的聚集体无法向邻近的聚集体发出信号,而含有≥5000 个细胞的聚集体则可以向远至 176µm 的聚集体发出信号。在这个“呼叫距离”内,并非所有的聚集体都有反应,这表明聚集体对信号的敏感性不同。信号受体的过表达增加了聚集体对信号的敏感性,这表明聚集体的响应能力部分取决于受体水平。这些研究为空间排列和表型异质性对体内 信号的影响提供了定量基准数据。