Correlated cryogenic fluorescence microscopy and electron cryo-tomography shows that exogenous TRIM5α can form hexagonal lattices or autophagy aggregates in vivo.
Stephen D CarterJoão I MamedeThomas J HopeGeorge J LuPublished in: Proceedings of the National Academy of Sciences of the United States of America (2020)
Members of the tripartite motif (TRIM) protein family have been shown to assemble into structures in both the nucleus and cytoplasm. One TRIM protein family member, TRIM5α, has been shown to form cytoplasmic bodies involved in restricting retroviruses such as HIV-1. Here we applied cryogenic correlated light and electron microscopy, combined with electron cryo-tomography, to intact mammalian cells expressing YFP-rhTRIM5α and found the presence of hexagonal nets whose arm lengths were similar to those of the hexagonal nets formed by purified TRIM5α in vitro. We also observed YFP-rhTRIM5α within a diversity of structures with characteristics expected for organelles involved in different stages of macroautophagy, including disorganized protein aggregations (sequestosomes), sequestosomes flanked by flat double-membraned vesicles (sequestosome:phagophore complexes), sequestosomes within double-membraned vesicles (autophagosomes), and sequestosomes within multivesicular autophagic vacuoles (amphisomes or autolysosomes). Vaults were also seen in these structures, consistent with their role in autophagy. Our data 1) support recent reports that TRIM5α can form both well-organized signaling complexes and nonsignaling aggregates, 2) offer images of the macroautophagy pathway in a near-native state, and 3) reveal that vaults arrive early in macroautophagy.
Keyphrases
- electron microscopy
- high resolution
- cell death
- protein protein
- signaling pathway
- single molecule
- antiretroviral therapy
- endoplasmic reticulum stress
- deep learning
- hepatitis c virus
- human immunodeficiency virus
- genome wide
- hiv positive
- optical coherence tomography
- hiv aids
- mass spectrometry
- single cell
- convolutional neural network
- electronic health record
- machine learning
- dna methylation
- quantum dots
- adverse drug
- energy transfer