Understanding parasite-brain microvascular interactions with engineered 3D blood-brain barrier models.

Microbial interactions with the blood-brain barrier (BBB) can be highly pathogenic and are still not well understood. Among these, parasites present complex interactions with the brain microvasculature that are difficult to decipher using experimental animal models or reductionist 2D in vitro cultures. Novel 3D engineered blood-brain barrier models hold great promise to overcome limitations in traditional research approaches. These models better mimic the intricate 3D architecture of the brain microvasculature and recapitulate several aspects of BBB properties, physiology, and function. Moreover, they provide improved control over biophysical and biochemical experimental parameters and are compatible with advanced imaging and molecular biology techniques. Here, we review design considerations and methodologies utilized to successfully engineer BBB microvessels. Finally, we highlight the advantages and limitations of existing engineered models and propose applications to study parasite interactions with the BBB, including mechanisms of barrier disruption.

Super resolution microscopy and deep learning identify Zika virus reorganization of the endoplasmic reticulum.

The endoplasmic reticulum (ER) is a complex subcellular organelle composed of diverse structures such as tubules, sheets and tubular matrices. Flaviviruses such as Zika virus (ZIKV) induce reorganization of ER membranes to facilitate viral replication. Here, using 3D super resolution microscopy, ZIKV infection is shown to induce the formation of dense tubular matrices associated with viral replication in the central ER. Viral non-structural proteins NS4B and NS2B associate with replication complexes within the ZIKV-induced tubular matrix and exhibit distinct ER distributions outside this central ER region. Deep neural networks trained to distinguish ZIKV-infected versus mock-infected cells successfully identified ZIKV-induced central ER tubular matrices as a determinant of viral infection. Super resolution microscopy and deep learning are therefore able to identify and localize morphological features of the ER and allow for better understanding of how ER morphology changes due to viral infection.