Dataset Information

Project Title: Proteome analysis of CD4+ T cell subsets

ABSTRACT: Functionally distinct CD4+ helper T (Th) cell subsets, such as Th1, Th2, Th17, and regulatory T cells (Treg), play a pivotal role in the host-defense against pathogen invasion and the pathogenesis of inflammatory disorders. In this project, DIA-MS-based proteome analysis was performed on naïve CD4+ T, Th0, Th1, Th2, Th17 and iTreg cells using Q Exactive HF-X (Thermo Fisher Scientific) to search for proteins that differ among the cell subsets.

ORGANISM(S): Mus Musculus (mouse)

SUBMITTER: Yusuke Endo

PROVIDER: PXD036065 | JPOST Repository | Wed Jan 04 00:00:00 GMT 2023

REPOSITORIES: jPOST

ACCESS DATA

Publications

Characterization of proteogenomic signatures of differentiation of CD4+ T cell subsets.

Kanno Toshio T Konno Ryo R Miyako Keisuke K Nakajima Takahiro T Yokoyama Satoru S Sasamoto Shigemi S Asou Hikari K HK Ohzeki Junichiro J Kawashima Yusuke Y Hasegawa Yoshinori Y Ohara Osamu O Endo Yusuke Y

DNA research : an international journal for rapid publication of reports on genes and genomes 20230201 1

Functionally distinct CD4+ helper T (Th) cell subsets, including Th1, Th2, Th17, and regulatory T cells (Treg), play a pivotal role in the regulation of acquired immunity. Although the key proteins involved in the regulation of Th cell differentiation have already been identified how the proteogenomic landscape changes during the Th cell activation remains unclear. To address this issue, we characterized proteogenomic signatures of differentiation to each Th cell subsets by RNA sequencing and li ...[more]

PMID: 36579714

Similar Datasets

Project description:Raghunathan2010 - Genome-scale metabolic network of Francisella tularensis (iRS605) This model is described in the article: Systems approach to investigating host-pathogen interactions in infections with the biothreat agent Francisella. Constraints-based model of Francisella tularensis. Raghunathan A, Shin S, Daefler S. BMC Syst Biol 2010; 4: 118 Abstract: BACKGROUND: Francisella tularensis is a prototypic example of a pathogen for which few experimental datasets exist, but for which copious high-throughout data are becoming available because of its re-emerging significance as biothreat agent. The virulence of Francisella tularensis depends on its growth capabilities within a defined environmental niche of the host cell. RESULTS: We reconstructed the metabolism of Francisella as a stoichiometric matrix. This systems biology approach demonstrated that changes in carbohydrate utilization and amino acid metabolism play a pivotal role in growth, acid resistance, and energy homeostasis during infection with Francisella. We also show how varying the expression of certain metabolic genes in different environments efficiently controls the metabolic capacity of F. tularensis. Selective gene-expression analysis showed modulation of sugar catabolism by switching from oxidative metabolism (TCA cycle) in the initial stages of infection to fatty acid oxidation and gluconeogenesis later on. Computational analysis with constraints derived from experimental data revealed a limited set of metabolic genes that are operational during infection. CONCLUSIONS: This integrated systems approach provides an important tool to understand the pathogenesis of an ill-characterized biothreat agent and to identify potential novel drug targets when rapid target identification is required should such microbes be intentionally released or become epidemic. This model is hosted on BioModels Database and identified by: MODEL1507180003. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Project Title: Proteome analysis of CD4+ T cell subsets

Publications

Characterization of proteogenomic signatures of differentiation of CD4+ T cell subsets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure