Dynamics of morphogen source formation in a growing tissue.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-10-14 eCollection Date: 2024-10-01 DOI:10.1371/journal.pcbi.1012508
Richard D J G Ho, Kasumi Kishi, Maciej Majka, Anna Kicheva, Marcin Zagorski
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Abstract

A tight regulation of morphogen production is key for morphogen gradient formation and thereby for reproducible and organised organ development. Although many genetic interactions involved in the establishment of morphogen production domains are known, the biophysical mechanisms of morphogen source formation are poorly understood. Here we addressed this by focusing on the morphogen Sonic hedgehog (Shh) in the vertebrate neural tube. Shh is produced by the adjacently located notochord and by the floor plate of the neural tube. Using a data-constrained computational screen, we identified different possible mechanisms by which floor plate formation can occur, only one of which is consistent with experimental data. In this mechanism, the floor plate is established rapidly in response to Shh from the notochord and the dynamics of regulatory interactions within the neural tube. In this process, uniform activators and Shh-dependent repressors are key for establishing the floor plate size. Subsequently, the floor plate becomes insensitive to Shh and increases in size due to tissue growth, leading to scaling of the floor plate with neural tube size. In turn, this results in scaling of the Shh amplitude with tissue growth. Thus, this mechanism ensures a separation of time scales in floor plate formation, so that the floor plate domain becomes growth-dependent after an initial rapid establishment phase. Our study raises the possibility that the time scale separation between specification and growth might be a common strategy for scaling the morphogen gradient amplitude in growing organs. The model that we developed provides a new opportunity for quantitative studies of morphogen source formation in growing tissues.

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生长组织中形态发生源的动态形成。
形态发生器产生的严格调控是形态发生器梯度形成的关键,因此也是可重现和有组织的器官发育的关键。虽然形态发生器生成域的建立过程中涉及的许多基因相互作用已为人熟知,但形态发生器源形成的生物物理机制却鲜为人知。在这里,我们通过重点研究脊椎动物神经管中的形态发生器声刺猬(Shh)来解决这个问题。Shh由邻近的脊索和神经管底板产生。通过数据限制计算筛选,我们确定了底板形成的不同可能机制,其中只有一种机制与实验数据一致。在这一机制中,底板是根据来自脊索的 Shh 和神经管内的动态调控相互作用迅速形成的。在这一过程中,均匀的激活因子和依赖于 Shh 的抑制因子是确定底板大小的关键。随后,底板变得对 Shh 不敏感,并随着组织的生长而增大,导致底板随着神经管的大小而缩放。反过来,这又导致 Shh 振幅随组织生长而缩放。因此,这种机制确保了底板形成过程中时间尺度的分离,使底板域在最初的快速建立阶段之后变得依赖于生长。我们的研究提出了一种可能性,即规格化和生长之间的时间尺度分离可能是生长器官中形态发生器梯度振幅缩放的一种常见策略。我们建立的模型为定量研究生长组织中形态发生源的形成提供了新的机会。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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