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1.
Mol Cell ; 83(13): 2367-2386.e15, 2023 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-37311461

RESUMO

Epstein-Barr virus (EBV) causes infectious mononucleosis, triggers multiple sclerosis, and is associated with 200,000 cancers/year. EBV colonizes the human B cell compartment and periodically reactivates, inducing expression of 80 viral proteins. However, much remains unknown about how EBV remodels host cells and dismantles key antiviral responses. We therefore created a map of EBV-host and EBV-EBV interactions in B cells undergoing EBV replication, uncovering conserved herpesvirus versus EBV-specific host cell targets. The EBV-encoded G-protein-coupled receptor BILF1 associated with MAVS and the UFM1 E3 ligase UFL1. Although UFMylation of 14-3-3 proteins drives RIG-I/MAVS signaling, BILF1-directed MAVS UFMylation instead triggered MAVS packaging into mitochondrial-derived vesicles and lysosomal proteolysis. In the absence of BILF1, EBV replication activated the NLRP3 inflammasome, which impaired viral replication and triggered pyroptosis. Our results provide a viral protein interaction network resource, reveal a UFM1-dependent pathway for selective degradation of mitochondrial cargo, and highlight BILF1 as a novel therapeutic target.


Assuntos
Infecções por Vírus Epstein-Barr , Herpesvirus Humano 4 , Humanos , Herpesvirus Humano 4/genética , Infecções por Vírus Epstein-Barr/genética , Inflamassomos/metabolismo , Proteína 3 que Contém Domínio de Pirina da Família NLR/genética , Proteína 3 que Contém Domínio de Pirina da Família NLR/metabolismo , Mapas de Interação de Proteínas
2.
Cell Mol Life Sci ; 80(6): 154, 2023 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-37204469

RESUMO

Inflammation can impair intestinal barrier, while increased epithelial permeability can lead to inflammation. In this study, we found that the expression of Tspan8, a tetraspanin expressed specifically in epithelial cells, is downregulated in mouse model of ulcerative disease (UC) but correlated with those of cell-cell junction components, such as claudins and E-cadherin, suggesting that Tspan8 supports intestinal epithelial barrier. Tspan8 removal increases intestinal epithelial permeability and upregulates IFN-γ-Stat1 signaling. We also demonstrated that Tspan8 coalesces with lipid rafts and facilitates IFNγ-R1 localization at or near lipid rafts. As IFN-γ induces its receptor undergoing clathrin- or lipid raft-dependent endocytosis and IFN-γR endocytosis plays an important role in Jak-Stat1 signaling, our analysis on IFN-γR endocytosis revealed that Tspan8 silencing impairs lipid raft-mediated but promotes clathrin-mediated endocytosis of IFN-γR1, leading to increased Stat1 signaling. These changes in IFN-γR1 endocytosis upon Tspan8 silencing correlates with fewer lipid raft component GM1 at the cell surface and more clathrin heavy chain in the cells. Our findings indicate that Tspan8 determines the IFN-γR1 endocytosis route, to restrain Stat1 signaling, stabilize intestine epithelium, and subsequently prevent intestine from inflammation. Our finding also implies that Tspan8 is needed for proper endocytosis through lipid rafts.


Assuntos
Mucosa Intestinal , Receptores de Interferon , Tetraspaninas , Animais , Camundongos , Clatrina/metabolismo , Endocitose/fisiologia , Inflamação/metabolismo , Interferons/metabolismo , Mucosa Intestinal/metabolismo , Receptores de Interferon/metabolismo , Tetraspaninas/genética , Tetraspaninas/metabolismo
3.
Proteomics ; 23(3-4): e2100369, 2023 02.
Artigo em Inglês | MEDLINE | ID: mdl-36094355

RESUMO

BACKGROUND: Mass spectrometry (MS)-based proteomic analysis of posttranslational modifications (PTMs) usually requires the pre-enrichment of modified proteins or peptides. However, recent ultra-deep whole proteome profiling generates millions of spectra in a single experiment, leaving many unassigned spectra, some of which may be derived from PTM peptides. METHODS: Here we present JUMPptm, an integrative computational pipeline, to extract PTMs from unenriched whole proteome. JUMPptm combines the advantages of JUMP, MSFragger and Comet search engines, and includes de novo tags, customized database search and peptide filtering, which iteratively analyzes each PTM by a multi-stage strategy to improve sensitivity and specificity. RESULTS: We applied JUMPptm to the deep brain proteome of Alzheimer's disease (AD), and identified 34,954 unique peptides with phosphorylation, methylation, acetylation, ubiquitination, and others. The phosphorylated peptides were validated by enriched phosphoproteome from the same sample. TMT-based quantification revealed 482 PTM peptides dysregulated at different stages during AD progression. For example, the acetylation of numerous mitochondrial proteins is significantly decreased in AD. A total of 60 PTM sites are found in the pan-PTM map of the Tau protein. CONCLUSION: The JUMPptm program is an effective tool for pan-PTM analysis and the resulting AD pan-PTM profile serves as a valuable resource for AD research.


Assuntos
Doença de Alzheimer , Humanos , Doença de Alzheimer/metabolismo , Proteoma/metabolismo , Proteômica/métodos , Processamento de Proteína Pós-Traducional , Software , Peptídeos/metabolismo
4.
Proteomics ; 22(19-20): e2100243, 2022 10.
Artigo em Inglês | MEDLINE | ID: mdl-35723178

RESUMO

Tandem mass tag (TMT) mass spectrometry is a mainstream isobaric chemical labeling strategy for profiling proteomes. Here we present a 29-plex TMT method to combine the 11-plex and 18-plex labeling strategies. The 29-plex method was examined with a pooled sample composed of 1×, 3×, and 10× Escherichia coli peptides with 100× human background peptides, which generated two E. coli datasets (TMT11 and TMT18), displaying the distorted ratios of 1.0:1.7:4.2 and 1.0:1.8:4.9, respectively. This ratio compression from the expected 1:3:10 ratios was caused by co-isolated TMT-labeled ions (i.e., noise). Interestingly, the mixture of two TMT sets produced MS/MS spectra with unique features for the noise detection: (i) in TMT11-labeled spectra, TMT18-specific reporter ions (e.g., 135N) were shown as the noise; (ii) in TMT18-labeled spectra, the TMT11/TMT18-shared reporter ions (e.g., 131C) typically exhibited higher intensities than TMT18-specific reporter ions, due to contaminated TMT11-labeled ions in these shared channels. We further estimated the noise levels contributed by both TMT11- and TMT18-labeled peptides, and corrected reporter ion intensities in every spectrum. Finally, the anticipated 1:3:10 ratios were largely restored. This strategy was also validated using another 29-plex sample with 1:5 ratios. Thus the 29-plex method expands the TMT throughput and enhances the quantitative accuracy.


Assuntos
Proteoma , Espectrometria de Massas em Tandem , Humanos , Espectrometria de Massas em Tandem/métodos , Proteoma/análise , Proteômica/métodos , Escherichia coli , Peptídeos/análise , Íons
5.
J Vis Exp ; (176)2021 10 19.
Artigo em Inglês | MEDLINE | ID: mdl-34747401

RESUMO

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.


Assuntos
Proteoma , Proteômica , Análise por Conglomerados , Espectrometria de Massas/métodos , Processamento de Proteína Pós-Traducional , Proteoma/análise , Proteômica/métodos , Software
7.
Mol Neurodegener ; 16(1): 55, 2021 08 12.
Artigo em Inglês | MEDLINE | ID: mdl-34384464

RESUMO

Mass spectrometry-based proteomics empowers deep profiling of proteome and protein posttranslational modifications (PTMs) in Alzheimer's disease (AD). Here we review the advances and limitations in historic and recent AD proteomic research. Complementary to genetic mapping, proteomic studies not only validate canonical amyloid and tau pathways, but also uncover novel components in broad protein networks, such as RNA splicing, development, immunity, membrane transport, lipid metabolism, synaptic function, and mitochondrial activity. Meta-analysis of seven deep datasets reveals 2,698 differentially expressed (DE) proteins in the landscape of AD brain proteome (n = 12,017 proteins/genes), covering 35 reported AD genes and risk loci. The DE proteins contain cellular markers enriched in neurons, microglia, astrocytes, oligodendrocytes, and epithelial cells, supporting the involvement of diverse cell types in AD pathology. We discuss the hypothesized protective or detrimental roles of selected DE proteins, emphasizing top proteins in "amyloidome" (all biomolecules in amyloid plaques) and disease progression. Comprehensive PTM analysis represents another layer of molecular events in AD. In particular, tau PTMs are correlated with disease stages and indicate the heterogeneity of individual AD patients. Moreover, the unprecedented proteomic coverage of biofluids, such as cerebrospinal fluid and serum, procures novel putative AD biomarkers through meta-analysis. Thus, proteomics-driven systems biology presents a new frontier to link genotype, proteotype, and phenotype, accelerating the development of improved AD models and treatment strategies.


Assuntos
Doença de Alzheimer/metabolismo , Proteínas do Tecido Nervoso/metabolismo , Proteoma , Doença de Alzheimer/etiologia , Doença de Alzheimer/genética , Doenças Assintomáticas , Biomarcadores , Proteínas Sanguíneas/análise , Proteínas do Líquido Cefalorraquidiano/análise , Cromatografia Líquida , Disfunção Cognitiva/metabolismo , Mineração de Dados , Bases de Dados de Proteínas , Conjuntos de Dados como Assunto , Humanos , Metanálise como Assunto , Proteínas do Tecido Nervoso/análise , Proteínas do Tecido Nervoso/genética , Placa Amiloide/química , Processamento de Proteína Pós-Traducional , Proteômica/métodos , Espectrometria de Massas em Tandem
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