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引用次数: 0
摘要
阐明细胞分化网络的新兴动态对于理解细胞命运的决定至关重要。切换开关(Toggle switch)--一个由相互抑制的特异性转录因子 A 和 B 组成的网络--能从一个共同的祖细胞分化出两种表型:(高 A、低 B)和(低 A、高 B)。然而,对能从一个祖细胞分化出两种以上表型的网络的动态还没有进行深入研究。在这里,我们研究了一个四节点网络 A、B、C 和 D 相互抑制,形成一个切换四面体的动力学。我们的模拟结果表明,该网络是多稳态的,主要允许六种混合表型共存,其中两个节点的表达量相对于其余两个节点较高,例如(高 A、高 B、低 C、低 D)。最后,我们将研究结果应用于理解幼稚 CD4+ T 细胞分化成 Th1、Th2、Th17 和 Treg 亚群的过程,这表明 Th1/Th2/Th17/Treg 的决策是一个两步过程。
Multistability and predominant hybrid phenotypes in a four node mutually repressive network of Th1/Th2/Th17/Treg differentiation.
Elucidating the emergent dynamics of cellular differentiation networks is crucial to understanding cell-fate decisions. Toggle switch - a network of mutually repressive lineage-specific transcription factors A and B - enables two phenotypes from a common progenitor: (high A, low B) and (low A, high B). However, the dynamics of networks enabling differentiation of more than two phenotypes from a progenitor cell has not been well-studied. Here, we investigate the dynamics of a four-node network A, B, C, and D inhibiting each other, forming a toggle tetrahedron. Our simulations show that this network is multistable and predominantly allows for the co-existence of six hybrid phenotypes where two of the nodes are expressed relatively high as compared to the remaining two, for instance (high A, high B, low C, low D). Finally, we apply our results to understand naïve CD4+ T cell differentiation into Th1, Th2, Th17 and Treg subsets, suggesting Th1/Th2/Th17/Treg decision-making to be a two-step process.
期刊介绍:
npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology.
We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.