Wang Mengyao, Wang Junqiang, Zhang Xingxing, Yuan Ruoshi
School of Life Science, Shanghai University, Shanghai 200444, People's Republic of China.
Shanghai Center for Quantitative Life Sciences and Physics Department, Shanghai University, Shanghai 200444, People's Republic of China.
R Soc Open Sci. 2021 Nov 3;8(11):211289. doi: 10.1098/rsos.211289. eCollection 2021 Nov.
Haematopoietic lineage commitments are presented by a canonical roadmap in which haematopoietic stem cells or multipotent progenitors (MPPs) bifurcate into progenitors of more restricted lineages and ultimately mature to terminally differentiated cells. Although transcription factors playing significant roles in cell-fate commitments have been extensively studied, integrating such knowledge into the dynamic models to understand the underlying biological mechanism remains challenging. The hypothesis and modelling approach of the endogenous network has been developed previously and tested in various biological processes and is used in the present study of haematopoietic lineage commitments. The endogenous network is constructed based on the key transcription factors and their interactions that determine haematopoietic cell-fate decisions at each lineage branchpoint. We demonstrate that the process of haematopoietic lineage commitments can be reproduced from the landscape which orchestrates robust states of network dynamics and their transitions. Furthermore, some non-trivial characteristics are unveiled in the dynamical model. Our model also predicted previously under-represented regulatory interactions and heterogeneous MPP states by which distinct differentiation routes are intermediated. Moreover, network perturbations resulting in state transitions indicate the effects of ectopic gene expression on cellular reprogrammes. This study provides a predictive model to integrate experimental data and uncover the possible regulatory mechanism of haematopoietic lineage commitments.
造血谱系定向分化由一个经典路线图呈现，其中造血干细胞或多能祖细胞（MPP）分化为谱系限制更强的祖细胞，并最终成熟为终末分化细胞。尽管在细胞命运定向分化中起重要作用的转录因子已得到广泛研究，但将这些知识整合到动态模型中以理解潜在生物学机制仍具有挑战性。内源性网络的假设和建模方法此前已被开发，并在各种生物学过程中进行了测试，本研究将其用于造血谱系定向分化的研究。内源性网络基于关键转录因子及其相互作用构建，这些转录因子及其相互作用决定了每个谱系分支点的造血细胞命运决定。我们证明，造血谱系定向分化过程可以从协调网络动态稳定状态及其转变的格局中重现。此外，动力学模型还揭示了一些非平凡的特征。我们的模型还预测了以前未被充分代表的调控相互作用以及不同分化途径所介导的异质性MPP状态。此外，导致状态转变的网络扰动表明异位基因表达对细胞重编程的影响。本研究提供了一个预测模型，以整合实验数据并揭示造血谱系定向分化可能的调控机制。
