Dataset Information

Control of the differentiation of regulatory T cells and T(H)17 cells by the DNA-binding inhibitor Id3.

ABSTRACT: The molecular mechanisms that direct transcription of the gene encoding the transcription factor Foxp3 in CD4(+) T cells remain ill-defined. We show here that deletion of the DNA-binding inhibitor Id3 resulted in the defective generation of Foxp3(+) regulatory T cells (T(reg) cells). We identify two transforming growth factor-?1 (TGF-?1)-dependent mechanisms that were vital for activation of Foxp3 transcription and were defective in Id3(-/-) CD4(+) T cells. Enhanced binding of the transcription factor E2A to the Foxp3 promoter promoted Foxp3 transcription. Id3 was required for relief of inhibition by the transcription factor GATA-3 at the Foxp3 promoter. Furthermore, Id3(-/-) T cells showed greater differentiation into the T(H)17 subset of helper T cells in vitro and in a mouse asthma model. Therefore, a network of factors acts in a TGF-?-dependent manner to control Foxp3 expression and inhibit the development of T(H)17 cells.

SUBMITTER: Maruyama T

PROVIDER: S-EPMC3140164 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Control of the differentiation of regulatory T cells and T(H)17 cells by the DNA-binding inhibitor Id3.

Maruyama Takashi T Li Jun J Vaque Jose P JP Konkel Joanne E JE Wang Weifeng W Zhang Baojun B Zhang Pin P Zamarron Brian F BF Yu Dongyang D Wu Yuntao Y Zhuang Yuan Y Gutkind J Silvio JS Chen WanJun W

Nature immunology 20101205 1

The molecular mechanisms that direct transcription of the gene encoding the transcription factor Foxp3 in CD4(+) T cells remain ill-defined. We show here that deletion of the DNA-binding inhibitor Id3 resulted in the defective generation of Foxp3(+) regulatory T cells (T(reg) cells). We identify two transforming growth factor-β1 (TGF-β1)-dependent mechanisms that were vital for activation of Foxp3 transcription and were defective in Id3(-/-) CD4(+) T cells. Enhanced binding of the transcription ...[more]

PMID: 21131965

Similar Datasets

Project description:The reciprocal differentiation of T helper 17 (T(H)17) cells and induced regulatory T (iT(reg)) cells plays a critical role in both the pathogenesis and resolution of diverse human inflammatory diseases. Although initial studies suggested a stable commitment to either the T(H)17 or the iT(reg) lineage, recent results reveal remarkable plasticity and heterogeneity, reflected in the capacity of differentiated effectors cells to be reprogrammed among T(H)17 and iT(reg) lineages and the intriguing phenomenon that a group of naïve precursor CD4(+) T cells can be programmed into phenotypically diverse populations by the same differentiation signal, transforming growth factor beta. To reconcile these observations, we have built a mathematical model of T(H)17/iT(reg) differentiation that exhibits four different stable steady states, governed by pitchfork bifurcations with certain degrees of broken symmetry. According to the model, a group of precursor cells with some small cell-to-cell variability can differentiate into phenotypically distinct subsets of cells, which exhibit distinct levels of the master transcription-factor regulators for the two T cell lineages. A dynamical control system with these properties is flexible enough to be steered down alternative pathways by polarizing signals, such as interleukin-6 and retinoic acid and it may be used by the immune system to generate functionally distinct effector cells in desired fractions in response to a range of differentiation signals. Additionally, the model suggests a quantitative explanation for the phenotype with high expression levels of both master regulators. This phenotype corresponds to a re-stabilized co-expressing state, appearing at a late stage of differentiation, rather than a bipotent precursor state observed under some other circumstances. Our simulations reconcile most published experimental observations and predict novel differentiation states as well as transitions among different phenotypes that have not yet been observed experimentally.

Dataset Information

Control of the differentiation of regulatory T cells and T(H)17 cells by the DNA-binding inhibitor Id3.

Publications

Control of the differentiation of regulatory T cells and T(H)17 cells by the DNA-binding inhibitor Id3.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets