Molecular mechanisms of dorsoventral and left-right pattern formation in the zebrafish, Danio rerio

斑马鱼背腹和左右模式形成的分子机制

• 批准号: 5312552
• 负责人: Professor Dr. Martin Blum
• 金额: --
• 依托单位: Fachgebiet Zoologie (190z)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2001
• 资助国家: 德国
• 起止时间: 2000-12-31 至 2007-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/5312552?language=en
• 关键词: Molecular; mechanisms; dorsoventral; left; right

Midline mesodern is an important organising centre of vertebrate development. Differentiation of the ventral neural tube but also establishment of left-right asymmetry of the heart and viscera are dependent on signals from the midline mesoderm. Key signalling molecule in these processes is the secreted growth factor Nodal. Nodal which forms a subgroup of the TGFß superfamily of signalling molecules controls, however, a variety of distinct differentiation processes during development indicating context dependence of its patterning activities. This provokes the existence of additional factors which determine a specific target cell response. The aim of this proposal is to carry out a genomewide search to characterise and compare the gene expression programmes in two distinct Nodal target cell populations, the left lateral plate mesodern and the floor plate/notochord. This work will elucidate the differences and similarities in gene responses to Nodal signals. Furthermore it will identify downstream genes which characterise the distinct differentiation states of the two cell populations and may identify signals acting in addition to Nodal related factors on target cells. This work will make a contribution to the analysis of gene networks in the approaching post sequencing era.

中线中现代是脊椎动物发育的重要组织中心。腹侧神经管的分化以及心脏和内脏的左右不对称的建立都依赖于中线中胚层的信号。在这些过程中的关键信号分子是分泌的生长因子Nodal。Nodal是TGFß信号分子超家族的一个亚群，然而，在发育过程中，Nodal控制着各种不同的分化过程，表明其模式活动依赖于环境。这引发了决定特定靶细胞反应的其他因素的存在。本提案的目的是进行全基因组搜索，以表征和比较两个不同的淋巴结靶细胞群体（左外侧板中现代细胞和底板/脊索细胞）的基因表达程序。这项工作将阐明基因对节点信号反应的异同。此外，它将识别表征两种细胞群体不同分化状态的下游基因，并可能识别除节点相关因子外作用于靶细胞的信号。这项工作将对即将到来的后测序时代的基因网络分析做出贡献。