Reference proteome of highly purified human Th1 cells reveals strong effects on metabolism and protein ubiquitination upon differentiation

PROTEOMICS ◽  
2015 ◽  
Vol 15 (21) ◽  
pp. 3644-3647 ◽  
Author(s):  
Massimiliano Pagani ◽  
Maxie Rockstroh ◽  
Maj Schuster ◽  
Grazisa Rossetti ◽  
Monica Moro ◽  
...  
Keyword(s):  
Th1 Cells ◽  
Protein Ubiquitination ◽  
Reference Proteome

E3 ubiquitin ligases

2005 ◽  
Vol 41 ◽  
pp. 15-30 ◽  
Author(s):  
Helen C. Ardley ◽  
Philip A. Robinson
Keyword(s):  
Protein Complexes ◽  
Loss Of Function ◽  
Direct Role ◽  
Cellular Processes ◽  
Substrate Protein ◽  
Protein Ubiquitination ◽  
C Terminus ◽  
Full Complement ◽  
Spatio Temporal ◽  
Eukaryotic Organisms

The selectivity of the ubiquitin–26 S proteasome system (UPS) for a particular substrate protein relies on the interaction between a ubiquitin-conjugating enzyme (E2, of which a cell contains relatively few) and a ubiquitin–protein ligase (E3, of which there are possibly hundreds). Post-translational modifications of the protein substrate, such as phosphorylation or hydroxylation, are often required prior to its selection. In this way, the precise spatio-temporal targeting and degradation of a given substrate can be achieved. The E3s are a large, diverse group of proteins, characterized by one of several defining motifs. These include a HECT (homologous to E6-associated protein C-terminus), RING (really interesting new gene) or U-box (a modified RING motif without the full complement of Zn2+-binding ligands) domain. Whereas HECT E3s have a direct role in catalysis during ubiquitination, RING and U-box E3s facilitate protein ubiquitination. These latter two E3 types act as adaptor-like molecules. They bring an E2 and a substrate into sufficiently close proximity to promote the substrate's ubiquitination. Although many RING-type E3s, such as MDM2 (murine double minute clone 2 oncoprotein) and c-Cbl, can apparently act alone, others are found as components of much larger multi-protein complexes, such as the anaphase-promoting complex. Taken together, these multifaceted properties and interactions enable E3s to provide a powerful, and specific, mechanism for protein clearance within all cells of eukaryotic organisms. The importance of E3s is highlighted by the number of normal cellular processes they regulate, and the number of diseases associated with their loss of function or inappropriate targeting.

Transfer of resistance against reinfection in experimental infectious colitis by CD4+ Th1 cells

2005 ◽  
Vol 43 (05) ◽  
Author(s):  
M Ross ◽  
T Kucharzik ◽  
W Domschke ◽  
TW Spahn
Keyword(s):  
Th1 Cells ◽  
Infectious Colitis

Characterization of NF1 Protein Ubiquitination

2008 ◽  
Author(s):  
Koko Murakami ◽  
Victor A. Fried
Keyword(s):  
Protein Ubiquitination

Towards Computational Models of Identifying Protein Ubiquitination Sites

2019 ◽  
Vol 20 (5) ◽  
pp. 565-578 ◽  
Author(s):  
Lidong Wang ◽  
Ruijun Zhang
Keyword(s):  
Computational Methods ◽  
Computational Models ◽  
Feature Representation ◽  
Biological Sequence ◽  
Post Translational Modification ◽  
Test Dataset ◽  
Protein Ubiquitination ◽  
Protein Functions ◽  
Independent Test Dataset ◽  
Benchmark Datasets

Ubiquitination is an important post-translational modification (PTM) process for the regulation of protein functions, which is associated with cancer, cardiovascular and other diseases. Recent initiatives have focused on the detection of potential ubiquitination sites with the aid of physicochemical test approaches in conjunction with the application of computational methods. The identification of ubiquitination sites using laboratory tests is especially susceptible to the temporality and reversibility of the ubiquitination processes, and is also costly and time-consuming. It has been demonstrated that computational methods are effective in extracting potential rules or inferences from biological sequence collections. Up to the present, the computational strategy has been one of the critical research approaches that have been applied for the identification of ubiquitination sites, and currently, there are numerous state-of-the-art computational methods that have been developed from machine learning and statistical analysis to undertake such work. In the present study, the construction of benchmark datasets is summarized, together with feature representation methods, feature selection approaches and the classifiers involved in several previous publications. In an attempt to explore pertinent development trends for the identification of ubiquitination sites, an independent test dataset was constructed and the predicting results obtained from five prediction tools are reported here, together with some related discussions.

Cadmium induces endoplasmic reticulum stress-mediated apoptosis in pig pancreas via the increase of Th1 cells

Toxicology ◽  
2021 ◽  
pp. 152790
Author(s):  
Hao Wu ◽  
Shufang Zheng ◽  
Jinxi Zhang ◽  
Shiwen Xu ◽  
Zhiruo Miao
Keyword(s):  
Endoplasmic Reticulum ◽  
Endoplasmic Reticulum Stress ◽  
Th1 Cells

scholarly journals Label-free quantitative proteomic analysis of the inhibition effect of Lactobacillus rhamnosus GG on Escherichia coli biofilm formation in co-culture

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Huiyi Song ◽  
Ni Lou ◽  
Jianjun Liu ◽  
Hong Xiang ◽  
Dong Shang
Keyword(s):  
Escherichia Coli ◽  
Proteomic Analysis ◽  
Biofilm Formation ◽  
Lactobacillus Rhamnosus ◽  
Differentially Expressed ◽  
Differentially Expressed Proteins ◽  
Label Free ◽  
Quantitative Proteomic Analysis ◽  
E Coli ◽  
Protein Ubiquitination

Abstract Background Escherichia coli (E. coli) is the principal pathogen that causes biofilm formation. Biofilms are associated with infectious diseases and antibiotic resistance. This study employed proteomic analysis to identify differentially expressed proteins after coculture of E. coli with Lactobacillus rhamnosus GG (LGG) microcapsules. Methods To explore the relevant protein abundance changes after E. coli and LGG coculture, label-free quantitative proteomic analysis and qRT-PCR were applied to E. coli and LGG microcapsule groups before and after coculture, respectively. Results The proteomic analysis characterised a total of 1655 proteins in E. coli K12MG1655 and 1431 proteins in the LGG. After coculture treatment, there were 262 differentially expressed proteins in E. coli and 291 in LGG. Gene ontology analysis showed that the differentially expressed proteins were mainly related to cellular metabolism, the stress response, transcription and the cell membrane. A protein interaction network and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis indicated that the differentiated proteins were mainly involved in the protein ubiquitination pathway and mitochondrial dysfunction. Conclusions These findings indicated that LGG microcapsules may inhibit E. coli biofilm formation by disrupting metabolic processes, particularly in relation to energy metabolism and stimulus responses, both of which are critical for the growth of LGG. Together, these findings increase our understanding of the interactions between bacteria under coculture conditions.

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