一个系统水平的模型显示,转录随机性是造血干细胞分化所必需的。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Joel Herrera, Antonio Bensussen, Mónica L García-Gómez, Adriana Garay-Arroyo, Elena R Álvarez-Buylla
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引用次数: 0

摘要

由于涉及的成分和相互作用数量众多,以及各种生理条件的影响,造血干细胞的分化一直难以进行实验研究。从基于实验数据的200个节点网络中,我们通过瓦解线性路径并保留功能反馈回路,推导出21个节点的调节网络。这个调控网络核心整合了造血干细胞分化的关键节点和相互作用,包括转录因子、代谢和氧化还原信号通路。采用布尔模型、连续模型和随机模型模拟了造血干细胞生态位的缺氧条件,以及造血干细胞转变和分化的模式和时间序列。我们的研究结果表明造血干细胞的分化是一个可塑性的过程,在这个过程中,细胞命运可以在它们自己之间进行转分化。此外,我们发现细胞异质性是造血干细胞分化的基础。最后,我们发现氧气激活ROS的产生,抑制静止,促进造血干细胞的生长和分化途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A system-level model reveals that transcriptional stochasticity is required for hematopoietic stem cell differentiation.

HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by collapsing linear pathways and retaining the functional feedback loops. This regulatory network core integrates key nodes and interactions underlying HSCs differentiation, including transcription factors, metabolic, and redox signaling pathways. We used Boolean, continuous, and stochastic dynamic models to simulate the hypoxic conditions of the HSCs niche, as well as the patterns and temporal sequences of HSCs transitions and differentiation. Our findings indicate that HSCs differentiation is a plastic process in which cell fates can transdifferentiate among themselves. Additionally, we found that cell heterogeneity is fundamental for HSCs differentiation. Lastly, we found that oxygen activates ROS production, inhibiting quiescence and promoting growth and differentiation pathways of HSCs.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
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.
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