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1.
Cell Rep ; 43(1): 113517, 2024 01 23.
Artículo en Inglés | MEDLINE | ID: mdl-38142397

RESUMEN

Randomly barcoded transposon mutant libraries are powerful tools for studying gene function and organization, assessing gene essentiality and pathways, discovering potential therapeutic targets, and understanding the physiology of gut bacteria and their interactions with the host. However, construction of high-quality libraries with uniform representation can be challenging. In this review, we survey various strategies for barcoded library construction, including transposition systems, methods of transposon delivery, optimal library size, and transconjugant selection schemes. We discuss the advantages and limitations of each approach, as well as factors to consider when selecting a strategy. In addition, we highlight experimental and computational advances in arraying condensed libraries from mutant pools. We focus on examples of successful library construction in gut bacteria and their application to gene function studies and drug discovery. Given the need for understanding gene function and organization in gut bacteria, we provide a comprehensive guide for researchers to construct randomly barcoded transposon mutant libraries.


Asunto(s)
Elementos Transponibles de ADN , Secuenciación de Nucleótidos de Alto Rendimiento , Elementos Transponibles de ADN/genética , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Clonación Molecular , Biblioteca de Genes , Bacterias/genética , Mutagénesis Insercional/genética
2.
Elife ; 112022 01 13.
Artículo en Inglés | MEDLINE | ID: mdl-35023830

RESUMEN

Despite tremendous progress in the understanding of COVID-19, mechanistic insight into immunological, disease-driving factors remains limited. We generated maVie16, a mouse-adapted SARS-CoV-2, by serial passaging of a human isolate. In silico modeling revealed how only three Spike mutations of maVie16 enhanced interaction with murine ACE2. maVie16 induced profound pathology in BALB/c and C57BL/6 mice, and the resulting mouse COVID-19 (mCOVID-19) replicated critical aspects of human disease, including early lymphopenia, pulmonary immune cell infiltration, pneumonia, and specific adaptive immunity. Inhibition of the proinflammatory cytokines IFNγ and TNF substantially reduced immunopathology. Importantly, genetic ACE2-deficiency completely prevented mCOVID-19 development. Finally, inhalation therapy with recombinant ACE2 fully protected mice from mCOVID-19, revealing a novel and efficient treatment. Thus, we here present maVie16 as a new tool to model COVID-19 for the discovery of new therapies and show that disease severity is determined by cytokine-driven immunopathology and critically dependent on ACE2 in vivo.


Asunto(s)
Enzima Convertidora de Angiotensina 2/metabolismo , COVID-19/virología , Interferón gamma/farmacología , SARS-CoV-2/patogenicidad , Inmunidad Adaptativa/inmunología , Animales , Modelos Animales de Enfermedad , Interferón gamma/metabolismo , Ratones Endogámicos BALB C , Ratones Endogámicos C57BL , Peptidil-Dipeptidasa A/genética , Glicoproteína de la Espiga del Coronavirus/genética
3.
Elife ; 102021 12 20.
Artículo en Inglés | MEDLINE | ID: mdl-34927585

RESUMEN

Infection and viral entry of SARS-CoV-2 crucially depends on the binding of its Spike protein to angiotensin converting enzyme 2 (ACE2) presented on host cells. Glycosylation of both proteins is critical for this interaction. Recombinant soluble human ACE2 can neutralize SARS-CoV-2 and is currently undergoing clinical tests for the treatment of COVID-19. We used 3D structural models and molecular dynamics simulations to define the ACE2 N-glycans that critically influence Spike-ACE2 complex formation. Engineering of ACE2 N-glycosylation by site-directed mutagenesis or glycosidase treatment resulted in enhanced binding affinities and improved virus neutralization without notable deleterious effects on the structural stability and catalytic activity of the protein. Importantly, simultaneous removal of all accessible N-glycans from recombinant soluble human ACE2 yields a superior SARS-CoV-2 decoy receptor with promise as effective treatment for COVID-19 patients.


Asunto(s)
Enzima Convertidora de Angiotensina 2/metabolismo , Simulación de Dinámica Molecular , Polisacáridos/metabolismo , Receptores Virales/metabolismo , SARS-CoV-2/metabolismo , Glicoproteína de la Espiga del Coronavirus/metabolismo , Enzima Convertidora de Angiotensina 2/química , Enzima Convertidora de Angiotensina 2/genética , COVID-19/prevención & control , COVID-19/virología , Glicosilación , Humanos , Polisacáridos/química , Unión Proteica , Ingeniería de Proteínas , Receptores Virales/química , Receptores Virales/genética , SARS-CoV-2/genética , SARS-CoV-2/fisiología , Glicoproteína de la Espiga del Coronavirus/química , Glicoproteína de la Espiga del Coronavirus/genética , Internalización del Virus
4.
Nat Methods ; 18(5): 520-527, 2021 05.
Artículo en Inglés | MEDLINE | ID: mdl-33859439

RESUMEN

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein-protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography-MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.


Asunto(s)
Aprendizaje Automático , Proteínas/química , Proteómica , Programas Informáticos , Teorema de Bayes , Cromatografía en Gel , Bases de Datos de Proteínas , Conformación Proteica
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