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辅助性 T 细胞 17 细胞和诱导性调节 T 细胞的相互分化的数学模型。

A mathematical model for the reciprocal differentiation of T helper 17 cells and induced regulatory T cells.

机构信息

Genetics, Bioinformatics, and Computational Biology Program, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.

出版信息

PLoS Comput Biol. 2011 Jul;7(7):e1002122. doi: 10.1371/journal.pcbi.1002122. Epub 2011 Jul 28.

Abstract

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.

摘要

辅助性 T 细胞 17(T(H)17)细胞和诱导性调节性 T(iT(reg))细胞的相互分化在多种人类炎症性疾病的发病机制和缓解中起着关键作用。尽管最初的研究表明 T(H)17 或 iT(reg) 谱系存在稳定的分化倾向,但最近的研究结果揭示了其显著的可塑性和异质性,表现在分化效应细胞在 T(H)17 和 iT(reg) 谱系之间重新编程的能力,以及一个有趣的现象,即一群幼稚前体 CD4(+)T 细胞可以被同一分化信号——转化生长因子β(transforming growth factor beta)编程为表型不同的群体。为了解决这些观察结果之间的矛盾,我们构建了一个 T(H)17/iT(reg)分化的数学模型,该模型表现出四个不同的稳定稳态,由叉形分岔控制,具有一定程度的对称破缺。根据该模型,一群具有一定细胞间变异性的前体细胞可以分化为具有不同表型的细胞亚群,这些细胞亚群表现出两种 T 细胞谱系的主转录因子调控因子的不同水平。具有这些特性的动力学控制系统具有足够的灵活性,可以通过极化信号(如白细胞介素 6 和视黄酸)引导其进入替代途径,并且免疫系统可能会利用它来产生具有不同功能的效应细胞,以响应一系列分化信号。此外,该模型还为高表达两种主调控因子的表型提供了定量解释。这种表型对应于一个重新稳定的共表达状态,出现在分化的晚期,而不是在其他一些情况下观察到的双潜能前体细胞状态。我们的模拟结果与大多数已发表的实验观察结果相吻合,并预测了新的分化状态以及尚未在实验中观察到的不同表型之间的转变。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/90bb/3145653/584dfff8c992/pcbi.1002122.g001.jpg

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