Patterned Gene Expression in Drosophila Development

 描述(申请人提供)：在动物中，发育和分化是通过基因表达的顺序激活进行的。我们的主要目标是阐明构成正常发育、疾病和进化过程的基因相互作用的复杂网络。对这些相互作用的全面分析需要了解生物体中全部基因的基因表达谱。在伯克利果蝇基因组计划(BDGP)中，我们已经建立了果蝇发育的基因表达资源，其中包含通过整体安装RNA原位杂交确定的空间和时间胚胎表达模式。使用基于解剖本体的标准化受控词汇和图案的标准化虚拟表示来注释这些图案，以便于基于图像的搜索和分析。这一资源现在包括控制动物器官发生过程的所有序列特异性DNA结合蛋白或转录因子(TF)的胚胎表达模式。具体地说，我们建议继续收集其余蛋白质编码基因的RNA表达模式，包括新发现的基因，并扩大收集范围，包括非编码基因和可能具有胚胎表达的替代转录本；分析假定的转录因子(TF)CRM驱动的表达模式，并使用我们新开发的计算图像分析工具分析表达；最后，将所有TF的TF RNA表达模式与从GFP标记的品系确定的相应蛋白质表达进行比较。我们的研究产生的基因表达数据将为阐明果蝇和其他真核生物(包括人类)的基因功能提供基础信息。尤其是大多数非编码RNA的作用仍不清楚。对调控区域的功能分析将提供对转录因子的发育作用的见解。转录本、蛋白质和CRM时空表达数据的整合将促进调控相互作用网络的发现。这些研究旨在了解生命过程，并为促进更好的人类健康奠定基础。

 DESCRIPTION (provided by applicant): In animals, development and differentiation proceeds by the sequential activation of gene expression. Our primary goal is to elucidate the complex network of genetic interactions that underlies the processes of normal development, disease and evolution. A comprehensive analysis of these interactions requires knowledge of the gene expression profiles for the full complement of genes in an organism. At the Berkeley Drosophila Genome Project (BDGP), we have established a gene expression resource for Drosophila development that contains spatial and temporal embryonic expression patterns determined from whole mount RNA in-situ hybridization. These patterns are annotated using a standardized, controlled vocabulary based on an anatomical ontology and a standardized virtual representation of the patterns to facilitate image based search and analysis. This resource now includes embryonic expression patterns for all sequence-specific DNA-binding proteins or transcription factors (TFs) that control the processes of animal organogenesis. Specifically we propose to continue to collect RNA expression patterns for the remaining protein-coding genes, including newly identified genes, and expand the collection to include non-coding genes and alternative transcripts with likely embryonic expression; assay patterns of expression driven by putative CRMs for transcription factors (TFs) and analyze the expression using our newly developed computational image analysis tools; and finally compare TF RNA expression patterns to corresponding protein expression determined from GFP-tagged lines for all TFs. The gene expression data produced by our study will provide fundamental information for elucidating gene function in Drosophila and, by homology, in other eukaryotes, including humans. The roles of most non-coding RNAs in particular remain unknown. The functional analysis of regulatory regions will provide insights into the developmental roles of the transcription factors. The integration of transcript, protein and CRM spatiotemporal expression data will promote discovery of networks of regulatory interactions. These studies are directed toward the understanding of life processes and lay the foundation for promoting better human health.