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1.
Mol Cell Proteomics ; 22(12): 100676, 2023 Dec.
Article in English | MEDLINE | ID: mdl-37940003

ABSTRACT

Extracellular vesicles (EVs) are biomolecule carriers for intercellular communication in health and disease. Nef is a HIV virulence factor that is released from cells within EVs and is present in plasma EVs of HIV-1 infected individuals. We performed a quantitative proteomic analysis to fully characterize the Nef-induced changes in protein composition of T cell-derived EVs and identify novel host targets of HIV. Several proteins with well-described roles in infection or not previously associated with HIV pathogenesis were specifically modulated by Nef in EVs. Among the downregulated proteins are the interferon-induced transmembrane 1, 2, and 3 (IFITM1-3) proteins, broad-spectrum antiviral factors known to be cell-to-cell transferable by EVs. We demonstrate that Nef depletes IFITM1-3 from EVs by excluding these proteins from the plasma membrane and lipid rafts, which are sites of EVs biogenesis in T cells. Our data establish Nef as a modulator of EVs' global protein content and as an HIV factor that antagonizes IFITMs.


Subject(s)
Extracellular Vesicles , HIV Infections , HIV-1 , Humans , T-Lymphocytes , Proteome/metabolism , Proteomics , Extracellular Vesicles/metabolism , Interferons/metabolism , HIV Infections/metabolism , Antiviral Agents/metabolism
2.
Article in English | MEDLINE | ID: mdl-30338257

ABSTRACT

Since the advent of systems and synthetic biology, many studies have sought to harness microbes as cell factories through genetic and metabolic engineering approaches. Yeast and filamentous fungi have been successfully harnessed to produce fine and high value-added chemical products. In this review, we present some of the most promising advances from recent years in the use of fungi for this purpose, focusing on the manipulation of fungal strains using systems and synthetic biology tools to improve metabolic flow and the flow of secondary metabolites by pathway redesign. We also review the roles of bioinformatics analysis and predictions in synthetic circuits, highlighting in silico systemic approaches to improve the efficiency of synthetic modules.

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