Lipidomic analysis of CD4+ T-cell subsets (Th1,Th2,Th17 and iTreg cells) (part I)
Ontology highlight
ABSTRACT:
ORGANISM(S): Human Homo Sapiens
TISSUE(S): T-cells
SUBMITTER: 
Partho Sen
PROVIDER: ST001676 | MetabolomicsWorkbench | Thu Jan 28 00:00:00 GMT 2021
REPOSITORIES: MetabolomicsWorkbench
Similar Datasets
Project description: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.
2023-01-04 | PXD036065 | JPOST Repository
Project description:A generic genome-scale metabolic model (GEMs) of human CD4+ T-cells. Several cell-specific GEMs for CD4+ T-cell subsets such as Th1, Th2, Th17 and iTreg cells derived from "HTimmR" are included as additional files. The model formats are compatible with RAVEN v.2.0 toolbox.
2023-03-29 | MODEL2101270002 | BioModels
Project description:Part 2/5: It includes quantitative targeted measurements of sphingolipids (ceramides and glycosphingolipids) in Th17, iTreg, and their paired controls (Th0 cells).
2021-01-31 | ST001675 | MetabolomicsWorkbench
Project description:In this study, we examined differential gene expression in naïve human CD4+ T cells, as well as in effector Th1, Th17-negative and Th17-enriched CD4- T cell subsets. We observed a marked enrichment for increased gene expression in effector CD4+ T cells compared to naive CD4+ among immune-mediated disease oci genes. Within effector T cells, expression of disease-associated genes was increased in Th17-enriched compared to Th17-negative cells. We used microarray to examine the gene expresssion profile and level of human naïve, Th1 and effector T cell subsets. Human PBMCs were isolated and sorted to naïve, CD161-CCR6- and CD161+CCR6+ memory T cells. Naïve T cells were differentiatied to Th1 cells, and CD161-CCR6- and CD161+CCR6+ memory T cells were in vitro expanded for Th17-negative and Th17-enriched effector T cells. The gene profile was compared among naive, Th1, Th17-negative, and Th17-enriched cell subsets.
2012-05-12 | E-GEOD-32901 | biostudies-arrayexpress
Project description:In this study, we examined differential gene expression in naïve human CD4+ T cells, as well as in effector Th1, Th17-negative and Th17-enriched CD4- T cell subsets. We observed a marked enrichment for increased gene expression in effector CD4+ T cells compared to naive CD4+ among immune-mediated disease oci genes. Within effector T cells, expression of disease-associated genes was increased in Th17-enriched compared to Th17-negative cells. We used microarray to examine the gene expresssion profile and level of human naïve, Th1 and effector T cell subsets.
2012-05-13 | GSE32901 | GEO
Project description:The early stages of human Th17 Cell differentiation were studied using label free proteomics to compare Th17 polarized CD4+ human T cells at 24 h and 72 h with activated cells (72 and 24 h) and Thp cells.
2019-01-28 | PXD008973 | Pride
Project description:Carbo2013 - Cytokine driven CD4+ T Cell differentiation and phenotype plasticity
CD4+ T cells can differentiate into different phenotypes
depending on the cytokine milieu. Here a computational and mathematical model with sixty ordinary differential equations representing a CD4+ T cell differentiating into either Th1, Th2, Th17 or iTreg cells, has been constructed.
The model includes cytokines,
nuclear receptors and transcription factors that define fate
and function of CD4+ T cells. Computational simulations
illustrate how a proinflammatory Th17 cell can undergo
reprogramming into an anti-inflammatory iTreg phenotype
following PPARc activation.
This model is described in the article:
Systems Modeling of Molecular Mechanisms Controlling Cytokine-driven CD4+ T Cell Differentiation and Phenotype Plasticity.
Carbo A, Hontecillas R, Kronsteiner B, Viladomiu M, Pedragosa M, Lu P, Philipson CW, Hoops S, Marathe M, Eubank S, Bisset K, Wendelsdorf K, Jarrah A, Mei Y, Bassaganya-Riera J
PLoS Computational Biology [2013, 9(4):e1003027]
Abstract:
Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPARγ) in modulating plasticity between Th17 and iTreg cells. PPARγ regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPARγ activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPARγ. Deletion of PPARγ in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPARγ in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.
Author's comment:
CD4+ T cell computational model (Version 1.4)
Steady state corrected. There was a problem in the internalization of IL-17 in its mathematical function.
This model is hosted on BioModels Database
and identified
by: MODEL1304230001
.
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.
2013-05-09 | BIOMD0000000451 | BioModels
Project description:Host defense against diverse pathogens involves the recruitment and differentiation of CD4+ T effector subsets including T helper 1 (Th1), Th2, Th17 and induced regulatory T (Treg) cells. Surface phenotype studies have revealed subset-specific surface markers for the identification and purification of human primary CD4+ T effector subsets. In the present study, we aimed to characterize the mRNA and large intergenic non-coding RNA (lincRNA) expression differences between human primary CD4+ T effector subsets and identify potential subset-specific genes. To achieve this goal, mRNA and lincRNA microarray profiling of flow cytometry-sorted human primary Th1, Th2, Th17 and Treg cells was performed. Principal component and pathway analyses revealed subset-specific gene expression patterns. A Th2-specific lincRNA, GATA3-AS1, also termed FLJ45983, was identified in primary immune cells and tissues, as well as in in vitro polarized CD4+ T effector subsets. Further analysis showed that GATA3-AS1 was a potential diagnostic marker in allergy, a Th2-associated disease. This first systematic genome-wide analysis of gene expression differences between primary CD4+ T effector subsets may help to identify novel regulatory protein-coding genes and lincRNAs regulating CD4+ T cell subset differentiation, as well as potential diagnostic markers. As an example, we identified a GATA3-associated Th2-specific marker lincRNA GATA3-AS1. Gene expression microarray analysis of flow-cytometry sorted human primary naïve CD4+ T cells, CD4+ T central memory cells, Th1, Th2, Th17 and Treg cells from buffy coat of four healthy controls Gene expression microarray analysis was performed using SurePrint G3 Human Gene Expression 8X60K microarray.
2013-06-01 | E-GEOD-43005 | biostudies-arrayexpress
Project description:CD4+ T cells are critical components in the human immune system. They produce cytokines to fight against pathogens and abnormal cells and stimulate other cells, such as B cells, macrophages, and neutrophils, to generate an immune response.
T helper 17 (Th17) cells are subtype CD4+ T cells that differentiate from naive CD4+ T cells in a specific cytokine environment. The Th17 cell model consists of 5282 reactions, 3263 metabolites, and 1250 genes. CD4+ T cell models helped identify drug targets and repurposable drugs against autoimmune diseases.
2023-11-27 | MODEL1909260006 | BioModels
Project description:CD4+ T cells are critical components in the human immune system. They produce cytokines to fight against pathogens and abnormal cells and stimulate other cells, such as B cells, macrophages, and neutrophils, to generate an immune response.
Naive CD4+ T cells are precursor cells that can differentiate into T helper - 1, - 2, - 17 (Th1, Th2, Th17) and regulatory T cells (Tregs) subtypes based on the type of pathogens or disease. The naive CD4+ T cell model consists of 5179 reactions, 3153 metabolites, and 1055 genes. Together with Th1, Th2, and Th17 models, the naive CD4+ T cell model helped identify drug targets and repurposable drugs against autoimmune diseases.
2023-11-27 | MODEL1909260003 | BioModels