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对翅芽中Hh梯度的动态读取揭示了稳健性和精确性之间特定模式的权衡。

Dynamic readout of the Hh gradient in the wing disc reveals pattern-specific tradeoffs between robustness and precision.

作者信息

Reyes Rosalío, Lander Arthur D, Nahmad Marcos

机构信息

Department of Physiology, Biophysics, and Neurosciences; Center for Research and Advanced Studies of the National Polytechnic Institute (Cinvestav), Mexico City, Mexico.

Interdisciplinary Polytechnic Unit of Biotechnology of the National Polytechnic Institute, Mexico City, Mexico.

出版信息

Elife. 2024 Nov 7;13:e85755. doi: 10.7554/eLife.85755.

DOI:10.7554/eLife.85755
PMID:39508736
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11651649/
Abstract

Understanding the principles underlying the design of robust, yet flexible patterning systems is a key problem in developmental biology. In the wing, Hedgehog (Hh) signaling determines patterning outputs using dynamical properties of the Hh gradient. In particular, the pattern of () is established by the steady-state Hh gradient, whereas the pattern of (), is established by a transient gradient of Hh known as the Hh overshoot. Here, we use mathematical modeling to suggest that this dynamical interpretation of the Hh gradient results in specific robustness and precision properties. For instance, the location of the anterior border of , which is subject to self-enhanced ligand degradation is more robustly specified than that of to changes in morphogen dosage, and we provide experimental evidence of this prediction. However, the anterior border of expression pattern, which is established by the overshoot gradient is much more precise to what would be expected by the steady-state gradient. Therefore, the dynamical interpretation of Hh signaling offers tradeoffs between robustness and precision to establish tunable patterning properties in a target-specific manner.

摘要

理解稳健且灵活的模式形成系统设计背后的原理是发育生物学中的一个关键问题。在翅膀中,刺猬信号通路(Hh)利用Hh梯度的动态特性来决定模式形成的输出。具体而言,()的模式由Hh稳态梯度建立,而()的模式则由Hh的瞬态梯度(称为Hh过冲)建立。在此,我们通过数学建模表明,对Hh梯度的这种动态解释会产生特定的稳健性和精确性特性。例如,受自增强配体降解影响的()前边界的位置,相较于()对形态发生素剂量变化的敏感度更低,我们为这一预测提供了实验证据。然而,由过冲梯度建立的()表达模式的前边界比稳态梯度预期的要精确得多。因此,对Hh信号通路的动态解释在稳健性和精确性之间进行了权衡,以目标特异性的方式建立可调节的模式形成特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/06fac70f5fae/elife-85755-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/6e1e0b569a52/elife-85755-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/66092fcbcdc5/elife-85755-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/232a912b7ef7/elife-85755-fig3.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/01913ed83831/elife-85755-fig3-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/851e60fc9020/elife-85755-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/06fac70f5fae/elife-85755-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/6e1e0b569a52/elife-85755-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/66092fcbcdc5/elife-85755-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/232a912b7ef7/elife-85755-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/3a32085ef4d9/elife-85755-fig3-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/01913ed83831/elife-85755-fig3-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/851e60fc9020/elife-85755-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7db6/11651649/06fac70f5fae/elife-85755-fig5.jpg

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本文引用的文献

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