Gene Expression Core

发育转录网络的输入是随着时间的推移，胚胎每个细胞核中转录因子蛋白的不同浓度。输出是每个细胞核中目标基因的转录水平。通过对胚胎染色进行简单的目视检查以揭示基因表达模式，可以发现许多基因的表达水平在细胞之间以高度复杂的时空方式存在差异（例如Program Overview[1]中的图4）。因此，为了能够模拟转录因子的组合如何协调产生如此复杂的输出模式，有必要在细胞分辨率下定量因子蛋白和靶mRNA的3D表达。为此，我们开发了基于图像分析的

The inputs for developmental transcription networks are the differential concentrations of transcription factor proteins in each nucleus of the embryo over time. The outputs are the levels of transcription of target genes in each nucleus. Simple visual inspection of embryos stained to reveal gene expression patterns shows that many genes' expression levels differ from cell to cell in a highly complex spatial and temporal manner (e.g. Fig. 4 in the Program Overview [1]). Therefore, to be able to model how combinations of transcription factors coordinate to produce such complex output patterns, it is essential to quantitate factor protein and target mRNA expression in 3D with cellular resolution. To this end, we have developed image analysis based methods, described below, that provided the first description of morphology and quantitative gene expression of a complete embryo with cellular resolution in a computationally analyzable format, a VirtualEmbryo [2-5]. Project 2 will generate hundreds of transgenic fly lines in which CRMs bearing point mutations in transcription factor recognition sites are placed upstream of a proximal promoter/reporter gene construct. It is likely (and highly desirable for testing our models) that most of these mutations will lead to modest (several fold) changes in occupancy of 1-3 transcription factors and concomitant quantitative changes in parts of the expression patterns driven by the CRMs. To be able to measure these transcription changes, it will be necessary to use our image analysis pipeline to quantitate the spatial and temporal patterns of reporter gene expression driven by both the original and the mutated forms of these CRMs. We will do this in this Aim.