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系统生物学方法揭示了神经嵴中控制虹膜细胞命运选择的核心基因调控网络。

A systems biology approach uncovers the core gene regulatory network governing iridophore fate choice from the neural crest.

机构信息

Department of Biology and Biochemistry and Centre for Regenerative Medicine, Faculty of Science, University of Bath, Bath, United Kingdom.

Department of Human and Molecular Genetics and Massey Cancer Center, VCU School of Medicine, Virginia Commonwealth University, Richmond, Virginia, United States of America.

出版信息

PLoS Genet. 2018 Oct 4;14(10):e1007402. doi: 10.1371/journal.pgen.1007402. eCollection 2018 Oct.

DOI:10.1371/journal.pgen.1007402
PMID:30286071
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6191144/
Abstract

Multipotent neural crest (NC) progenitors generate an astonishing array of derivatives, including neuronal, skeletal components and pigment cells (chromatophores), but the molecular mechanisms allowing balanced selection of each fate remain unknown. In zebrafish, melanocytes, iridophores and xanthophores, the three chromatophore lineages, are thought to share progenitors and so lend themselves to investigating the complex gene regulatory networks (GRNs) underlying fate segregation of NC progenitors. Although the core GRN governing melanocyte specification has been previously established, those guiding iridophore and xanthophore development remain elusive. Here we focus on the iridophore GRN, where mutant phenotypes identify the transcription factors Sox10, Tfec and Mitfa and the receptor tyrosine kinase, Ltk, as key players. Here we present expression data, as well as loss and gain of function results, guiding the derivation of an initial iridophore specification GRN. Moreover, we use an iterative process of mathematical modelling, supplemented with a Monte Carlo screening algorithm suited to the qualitative nature of the experimental data, to allow for rigorous predictive exploration of the GRN dynamics. Predictions were experimentally evaluated and testable hypotheses were derived to construct an improved version of the GRN, which we showed produced outputs consistent with experimentally observed gene expression dynamics. Our study reveals multiple important regulatory features, notably a sox10-dependent positive feedback loop between tfec and ltk driving iridophore specification; the molecular basis of sox10 maintenance throughout iridophore development; and the cooperation between sox10 and tfec in driving expression of pnp4a, a key differentiation gene. We also assess a candidate repressor of mitfa, a melanocyte-specific target of sox10. Surprisingly, our data challenge the reported role of Foxd3, an established mitfa repressor, in iridophore regulation. Our study builds upon our previous systems biology approach, by incorporating physiologically-relevant parameter values and rigorous evaluation of parameter values within a qualitative data framework, to establish for the first time the core GRN guiding specification of the iridophore lineage.

摘要

多能神经嵴(NC)祖细胞产生了令人惊讶的一系列衍生物,包括神经元、骨骼成分和色素细胞(色素细胞),但允许平衡选择每种命运的分子机制尚不清楚。在斑马鱼中,黑素细胞、虹膜细胞和黄色素细胞,这三种色素细胞谱系,被认为共享祖细胞,因此适合研究神经嵴祖细胞命运分离的复杂基因调控网络(GRN)。尽管以前已经确定了指导黑素细胞特化的核心 GRN,但指导虹膜细胞和黄色素细胞发育的 GRN 仍然难以捉摸。在这里,我们专注于虹膜细胞的 GRN,突变表型确定了转录因子 Sox10、Tfec 和 Mitfa 以及受体酪氨酸激酶 Ltk 是关键因素。在这里,我们提供了表达数据,以及功能丧失和获得的结果,指导了初始虹膜细胞特化 GRN 的推导。此外,我们使用数学建模的迭代过程,辅以适合实验数据定性性质的蒙特卡罗筛选算法,允许对 GRN 动力学进行严格的预测探索。预测结果经过实验评估,并得出可测试的假设,以构建 GRN 的改进版本,我们证明该版本产生的输出与实验观察到的基因表达动力学一致。我们的研究揭示了多个重要的调控特征,特别是 Sox10 依赖性的正反馈环在 tfec 和 ltk 之间驱动虹膜细胞特化; Sox10 在整个虹膜细胞发育过程中维持的分子基础; Sox10 和 tfec 合作驱动关键分化基因 pnp4a 的表达。我们还评估了 Sox10 特异性靶点 Mitfa 的候选抑制剂。令人惊讶的是,我们的数据挑战了 Foxd3 的报告作用,Foxd3 是 Mitfa 的一个已建立的抑制剂,在虹膜细胞调控中。我们的研究通过纳入生理相关的参数值并在定性数据框架内对参数值进行严格评估,在以前的系统生物学方法的基础上进一步发展,首次建立了指导虹膜细胞谱系特化的核心 GRN。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/80333e202a9d/pgen.1007402.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/7488eb780142/pgen.1007402.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/0bf840977515/pgen.1007402.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/f549d6d49c20/pgen.1007402.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/0f3b521ef9d2/pgen.1007402.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/a9a892a3e241/pgen.1007402.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/f912e2a5fe4c/pgen.1007402.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/80333e202a9d/pgen.1007402.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/7488eb780142/pgen.1007402.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/0bf840977515/pgen.1007402.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/f549d6d49c20/pgen.1007402.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/0f3b521ef9d2/pgen.1007402.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/a9a892a3e241/pgen.1007402.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/f912e2a5fe4c/pgen.1007402.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de1a/6191144/80333e202a9d/pgen.1007402.g007.jpg

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