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Upon infection, human immunodeficiency virus (HIV-1) releases its cone-shaped capsid into the cytoplasm of infected T-cells and macrophages. As its largest known cargo, the capsid enters the nuclear pore complex (NPC), driven by interactions with numerous FG-repeat nucleoporins (FG-Nups). Whether NPCs structurally adapt to capsid passage and whether capsids are modified during passage remains unknown, however. Here, we combined super-resolution and correlative microscopy with cryo electron tomography and molecular simulations to study nuclear entry of HIV-1 capsids in primary human macrophages. We found that cytosolically bound cyclophilin A is stripped off capsids entering the NPC, and the capsid hexagonal lattice remains largely intact inside and beyond the central channel. Strikingly, the NPC scaffold rings frequently crack during capsid passage, consistent with computer simulations indicating the need for NPC widening. The unique cone shape of the HIV-1 capsid facilitates its entry into NPCs and helps to crack their rings.
Cryo-electron tomography (CryoET) resolves individual macromolecules inside living cells. However, the complex composition and high density of cells challenge the faithful identification of features in tomograms. Here, we capitalize on recent advances in electron tomography and demonstrate that 3D template matching (TM) localizes a wide range of structures inside crowded eukaryotic cells with confidence 10 to 100-fold above the noise level. We establish a TM pipeline with systematically tuned parameters for automated, objective and comprehensive feature identification. High-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, lipid membranes and microtubules, and individual subunits, demonstrate that TM is generic. We resolve ~100-kDa proteins, connect the functional states of complexes to their cellular localization, and capture vaults carrying ribosomal cargo in situ. By capturing individual molecular events inside living cells with defined statistical confidence, high-confidence TM greatly speeds up the CryoET workflow and sets the stage for visual proteomics.