Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.
Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, Italy.
Microbiol Spectr. 2023 Jun 15;11(3):e0123323. doi: 10.1128/spectrum.01233-23. Epub 2023 May 24.
Understanding the pathogenesis of bacterial infections is critical for combatting them. For some infections, animal models are inadequate and functional genomic studies are not possible. One example is bacterial meningitis, a life-threatening infection with high mortality and morbidity. Here, we used the newly developed, physiologically relevant, organ-on-a-chip platform integrating the endothelium with neurons, closely mimicking conditions. Using high-magnification microscopy, permeability measurements, electrophysiological recordings, and immunofluorescence staining, we studied the dynamic by which the pathogens cross the blood-brain barrier and damage the neurons. Our work opens up possibilities for performing large-scale screens with bacterial mutant libraries for identifying the virulence genes involved in meningitis and determining the role of these genes, including various capsule types, in the infection process. These data are essential for understanding and therapy of bacterial meningitis. Moreover, our system offers possibilities for the study of additional infections-bacterial, fungal, and viral. The interactions of newborn meningitis (NBM) with the neurovascular unit are very complex and are hard to study. This work presents a new platform to study NBM in a system that enables monitoring of multicellular interactions and identifies processes that were not observed before.
了解细菌感染的发病机制对于对抗它们至关重要。对于某些感染,动物模型不足够,并且无法进行功能基因组研究。一个例子是细菌性脑膜炎,这是一种危及生命的感染,死亡率和发病率都很高。在这里,我们使用了新开发的、与生理相关的、将内皮细胞与神经元集成在一起的器官芯片平台,非常接近地模拟了这些条件。我们使用高倍显微镜、通透性测量、电生理记录和免疫荧光染色来研究病原体穿过血脑屏障并损伤神经元的动态过程。我们的工作为使用细菌突变文库进行大规模筛选开辟了可能性,以鉴定参与脑膜炎的毒力基因,并确定这些基因(包括各种荚膜类型)在感染过程中的作用。这些数据对于理解和治疗细菌性脑膜炎至关重要。此外,我们的系统为研究其他感染(细菌、真菌和病毒)提供了可能性。新生儿脑膜炎(NBM)与神经血管单元的相互作用非常复杂,难以研究。这项工作提出了一个新的平台,用于在一个能够监测细胞间相互作用并识别以前未观察到的过程的系统中研究 NBM。