Dissecting the transcriptional network governing differentiation of periderm

我们对口面裂（OFC）的致病机制的理解受到以下事实的限制： 这种疾病的遗传风险有一半以上是由特定基因引起的。为确定 在外显子组和全基因组中检测到的许多不相关的病理序列变异中， 对于患有这种疾病的患者，了解控制这种疾病的基因调控网络（GRNs）， 包括口腔牙周组织在内的相关组织的发育至关重要。我们提出一个系统生物学 分析周期性GRN的方法。在过去使用这种方法使我们能够确定三个新的 OFC风险基因。我们将利用两种模式生物，斑马鱼和小鼠，因为 分化GRN似乎是高度保守的。在斑马鱼中，周突分化非常早， 胚胎发生，极大地促进遗传扰动分析的执行和解释。鼠标，打开 另一方面，其优势在于其颅面解剖结构与人类更相似。目标1： 将使用最先进的网络推理算法来确定斑马鱼围产期分化GRN， 第二章.该工具进行共表达分析和差异表达分析。输入 数据集将包括RNA-seq表达谱，我们将从功能丧失（LOF）胚胎中产生4 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， 这些关键的TF在周期性GRN中。最后，我们将测试一个新的候选成员的周期性GRN， Tead，通过在斑马鱼中进行LOF测试，从而利用该模型系统的优势。在目标2中， 也将使用NetProphet算法推导小鼠口腔周分化GRN。输入数据集 将包括从野生型小鼠胚胎的腭架分离的周突蛋白的表达谱，和 来自三个关键TF的杂合突变体：Irf 6、Grhl 3和Tfap 2a。对于每种突变基因型， 异常的周皮分化的证据。我们还将通过以下方法鉴定鼠周肽增强子候选物： 从Krt 17-gfp转基因胚胎中分选GFP阳性和GFP阴性细胞， 群体，和H3 K27 Ac ChIP-seq对来自腭架和鼻腔的细胞。如目标1所示，我们将 还确定了关键转录因子的直接基因连锁。我们将在palate上训练机器学习算法 的增强子，并使用所得的评分功能，优先考虑OFC相关的SNP附近的基因， 对于那些可能直接影响OFC风险的因素，以周期表示。最后，我们将执行等位基因- 对来自三个基因座中的每一个的最佳候选SNP进行特异性报告基因测定。预期的结果是更深层次的 理解控制周突分化的特定TF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候选风险变体， OFC的全外显子组和全基因组测序分析。

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.