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

被引:20
|
作者
Long, Rory K. M. [1 ,2 ,4 ]
Moriarty, Kathleen P. [3 ]
Cardoen, Ben [3 ]
Gao, Guang [1 ,2 ]
Vogl, A. Wayne [1 ,2 ]
Jean, Francois [1 ,4 ]
Hamarneh, Ghassan [3 ]
Nabi, Ivan R. [1 ,2 ,5 ]
机构
[1] Univ British Columbia, Life Sci Inst, Vancouver, BC V6T 1Z3, Canada
[2] Univ British Columbia, Dept Cellular & Physiol Sci, Vancouver, BC V6T 1Z3, Canada
[3] Simon Fraser Univ, Sch Comp Sci, Burnaby, BC V5A 1S6, Canada
[4] Univ British Columbia, Dept Microbiol & Immunol, Vancouver, BC V6T 1Z3, Canada
[5] Univ British Columbia, Sch Biomed Engn, Vancouver, BC V6T 1Z3, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
REPLICATION; PROTEINS; NS4B;
D O I
10.1038/s41598-020-77170-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:18
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