Dissecting the transcriptional network governing differentiation of periderm

我们对口面部裂隙(OFC)的致病机制的了解受到以下事实的限制 这种疾病的可遗传风险的一半以上被分配给特定的基因。迈向识别 在沙门氏菌外显体和全基因组中检测到的许多无关序列中的病理序列变异 对于患有这种疾病的患者，了解支配 相关组织的发育，包括口腔周皮，是必不可少的。我们提出了一种系统生物学 周皮GRN的分析方法。在过去使用这种方法使我们能够识别出三部小说 OFC风险基因。我们将利用两种模式生物，斑马鱼和老鼠，因为周皮 分化GRN似乎是高度保守的。斑马鱼的周皮很早就分化了。 胚胎发生，极大地方便了遗传扰动分析的执行和解释。鼠标，打开 另一方面，它的优势是它的头面部解剖结构更接近人类。在目标1中，我们 将使用最先进的网络推理算法确定斑马鱼周皮分化GRN， NetProphet 2。该工具执行共表达分析和差异表达分析。输入 数据集将包括我们将从4个功能丧失(LOF)胚胎中生成的RNA-seq表达谱 已知参与该GRN的关键转录因子(Tf)。我们还将确定直接的基因联系 这些是周皮GRN中的关键转录因子。最后，我们将测试周皮GRN的一个新的候选成员， TEAD，通过在斑马鱼身上进行LOF测试，从而利用这一模型系统的优势。在《目标2》中，我们 将推导出小鼠口腔周皮分化的GRN，也使用了NetProphet算法。输入数据集 将包括从野生型小鼠胚胎的腭架分离的周皮的表达谱，以及 来自三个关键转录因子的杂合突变体：IRF6、GRH13和TFAP2A。对于每种突变的基因类型，都有 周皮分化异常的证据。我们还将通过以下方法确定小鼠周皮增强剂候选者 从Krt17-GFP转基因胚胎中分离GFP阳性和阴性细胞，对两者进行ATAC-SEQ 在腭架和鼻腔的细胞上进行H3K27Ac芯片序列分析。在目标1中，我们将 还要确定关键的转录因子的直接基因联系。我们将训练一个关于味觉的机器学习算法 周皮增强子，并使用所得到的评分函数来优先排序与OFC相关的SNPs 对于那些可能直接影响OFC风险的基因，表达在周皮中。最后，我们将进行等位基因- 具体的记者分析了来自三个基因座的最高候选SNPs。预期的结果是更深层次的 了解控制周皮分化的特定转录因子和顺式调节元件。这 将产生广泛的影响，因为它将使人类遗传学家能够优先考虑 来自OFC的全外显子组和基因组测序分析。

Our understanding of the pathogenic mechanisms for orofacial clefting (OFC) is limited by the fact that less than half of the heritable risk for this disorder has been assigned to specific genes. Towards identifying pathological sequence variants among the many irrelevant ones detected in exomes and whole genomes of patients with this disorder, an understanding of the gene regulatory networks (GRNs) that govern the development of relevant tissues, including the oral periderm, is essential. We propose a systems biology approach to analyzing the periderm GRN. Using this approach in the past enabled us to identify three novel OFC risk genes. We will utilize two model organisms, zebrafish and mouse, because the periderm differentiation GRN appears to be highly conserved. In zebrafish, the periderm differentiates very early in embryogenesis, greatly facilitating the execution and interpretation of genetic perturbation analyses. Mouse, on the other hand, has the advantage that its craniofacial anatomy is more similar to that of humans. In Aim 1, we will determine the zebrafish periderm differentiation GRN using a state-of-the-art network inference algorithm, NetProphet 2. This tool carries out both a coexpression analysis and a differential expression analysis. Input data sets will include RNA-seq expression profiles we will generate from loss-of-function (LOF) embryos for 4 key transcription factors (TF) known to participate in this GRN. We will also identify the direct gene linkages of these key TFs in the periderm GRN. Finally, we will test a novel candidate member of the periderm GRN, Tead, by carrying out LOF tests in zebrafish, thereby exploiting the strength of this model system. In Aim 2 we will deduce the murine oral periderm differentiation GRN, also using the NetProphet algorithm. Input datasets will include expression profiles of periderm isolated from the palate shelves of wild-type mouse embryos, and from heterozygous mutants of three key TFs: Irf6, Grhl3 and Tfap2a. For each of the mutant genotypes there is evidence of abnormal periderm differentiation. We will also identify murine periderm enhancer candidates by sorting GFP-positive and -negative cells from Krt17-gfp transgenic embryos, performing ATAC-seq on both populations, and H3K27Ac ChIP-seq on cells from palate shelves and the nasal cavity. As in Aim 1, we will also identify the direct gene linkages of the key TFs. We will train a machine learning algorithm on palate periderm enhancers, and use the resulting scoring function to prioritize OFC-associated SNPs near genes that are expressed in periderm for those that are likely to directly affect risk for OFC. Finally, we will perform allele- specific reporter assays on the top candidate SNPs from each of three loci. The expected outcome is a deeper understanding of the specific TFs and cis-regulatory elements that control differentiation of the periderm. This will have a broad impact because it will enable human geneticists to prioritize candidate risk variants that emerge from whole-exome and -genome sequencing analyses of OFC.