Judith, D. | bioRxiv Science

bioRxiv2023

ATG5 selectively engages virus-tethered BST2/Tetherin in an LC3C-associated pathway

BST2/Tetherin is a restriction factor that reduces HIV-1 dissemination by tethering virus at the cell surface. BST2 also acts as a sensor of HIV-1 budding, establishing a cellular anti-viral state. The HIV-1 Vpu protein antagonizes BST2 antiviral functions, notably by subverting an LC3C-associated pathway, a key cell intrinsic anti-microbial mechanism. Here, we show that ATG5 associates with BST2 and acts as a signaling scaffold to trigger an LC3C-associated pathway induced by HIV-1 infection. This process is initiated at the plasma membrane through the recognition of virus-tethered BST2 by ATG5. ATG5 and BST2 assemble as a complex, independently of the viral protein Vpu and ahead of the recruitment of the ATG protein LC3C. The conjugation of ATG5 with ATG12 is dispensable for this interaction. ATG5 recognizes cysteine-linked homodimerized BST2 and specifically engages phosphorylated BST2 tethering viruses at the plasma membrane, in an LC3C-associated pathway. We also found that this LC3C-associated pathway is used by Vpu to attenuate the inflammatory responses mediated by virion retention. Overall, we highlight that by targeting BST2 tethering viruses, ATG5 acts as a transducer of the LC3C-associated pathway induced by HIV-1 infection. Significance statementThe outcome of viral infection in cells is dependent on the balance between host restriction factors and viral countermeasures. BST2/Tetherin is a restriction factor that reduces HIV-1 dissemination by tethering virions at the cell surface. Its action is counteracted by the viral protein Vpu through multiple mechanisms. Here, we describe the initial step of a non-canonical autophagic pathway, called LC3C-associated pathway, subverted by Vpu to counteract BST2 antiviral activities. We found that the autophagic protein ATG5 acts as a transducer by targeting phosphorylated and dimerized virus-tethered BST2 from cell surface to the degradation. Our discovery opens new avenue in the discovery of unconventional functions of ATG5, as an adaptor for receptor at the plasma membrane initiating an unconventional autophagy process.

microbiology↗

bioRxiv2022

Unraveling membrane properties at the organelle-level with LipidDyn

Cellular membranes are formed from many different lipids in various amounts and proportions depending on the subcellular localization. The lipid composition of membranes is sensitive to changes in the cellular environment, and their alterations are linked to several diseases, including cancer. Lipids not only form lipid-lipid interactions but also interact with other biomolecules, including proteins, profoundly impacting each other. Molecular dynamics (MD) simulations are a powerful tool to study the properties of cellular membranes and membrane-protein interactions on different timescales and at varying levels of resolution. Over the last few years, software and hardware for biomolecular simulations have been optimized to routinely run long simulations of large and complex biological systems. On the other hand, high-throughput techniques based on lipidomics provide accurate estimates of the composition of cellular membranes at the level of subcellular compartments. The community needs computational tools for lipidomics and simulation data effectively interacting to better understand how changes in lipid compositions impact membrane function and structure. Lipidomic data can be analyzed to design biologically relevant models of membranes for MD simulations. Similar applications easily result in a massive amount of simulation data where the bottleneck becomes the analysis of the data to understand how membrane properties and membrane-protein interactions are changing in the different conditions. In this context, we developed LipidDyn, an in silico pipeline to streamline the analyses of MD simulations of membranes of different compositions. Once the simulations are collected, LipidDyn provides average properties and time series for several membrane properties such as area per lipid, thickness, diffusion motions, the density of lipid bilayers, and lipid enrichment/depletion. The calculations exploit parallelization and the pipelines include graphical outputs in a publication-ready form. We applied LipidDyn to different case studies to illustrate its potential, including membranes from cellular compartments and transmembrane protein domains. LipidDyn is implemented in Python and relies on open-source libraries. LipidDyn is available free of charge under the GNU General Public License from https://github.com/ELELAB/LipidDyn.

bioinformatics↗