Zheng Zhenzhen, Christley Scott, Chiu William T, Blitz Ira L, Xie Xiaohui, Cho Ken W Y, Nie Qing
Department of Mathematics, University of California, Irvine, CA 92697, USA.
BMC Syst Biol. 2014 Jan 8;8:3. doi: 10.1186/1752-0509-8-3.
During embryogenesis, signaling molecules produced by one cell population direct gene regulatory changes in neighboring cells and influence their developmental fates and spatial organization. One of the earliest events in the development of the vertebrate embryo is the establishment of three germ layers, consisting of the ectoderm, mesoderm and endoderm. Attempts to measure gene expression in vivo in different germ layers and cell types are typically complicated by the heterogeneity of cell types within biological samples (i.e., embryos), as the responses of individual cell types are intermingled into an aggregate observation of heterogeneous cell types. Here, we propose a novel method to elucidate gene regulatory circuits from these aggregate measurements in embryos of the frog Xenopus tropicalis using gene network inference algorithms and then test the ability of the inferred networks to predict spatial gene expression patterns.
We use two inference models with different underlying assumptions that incorporate existing network information, an ODE model for steady-state data and a Markov model for time series data, and contrast the performance of the two models. We apply our method to both control and knockdown embryos at multiple time points to reconstruct the core mesoderm and endoderm regulatory circuits. Those inferred networks are then used in combination with known dorsal-ventral spatial expression patterns of a subset of genes to predict spatial expression patterns for other genes. Both models are able to predict spatial expression patterns for some of the core mesoderm and endoderm genes, but interestingly of different gene subsets, suggesting that neither model is sufficient to recapitulate all of the spatial patterns, yet they are complementary for the patterns that they do capture.
The presented methodology of gene network inference combined with spatial pattern prediction provides an additional layer of validation to elucidate the regulatory circuits controlling the spatial-temporal dynamics in embryonic development.
在胚胎发育过程中，一个细胞群体产生的信号分子指导邻近细胞的基因调控变化，并影响它们的发育命运和空间组织。脊椎动物胚胎发育最早的事件之一是形成三个胚层，即外胚层、中胚层和内胚层。在不同胚层和细胞类型中进行体内基因表达测量的尝试通常因生物样本（即胚胎）中细胞类型的异质性而变得复杂，因为单个细胞类型的反应混合在对异质细胞类型的总体观察中。在此，我们提出一种新方法，利用基因网络推理算法从热带爪蟾胚胎的这些总体测量中阐明基因调控回路，然后测试推断网络预测空间基因表达模式的能力。
我们使用两种具有不同潜在假设并纳入现有网络信息的推理模型，一种用于稳态数据的常微分方程模型和一种用于时间序列数据的马尔可夫模型，并对比这两种模型的性能。我们在多个时间点将我们的方法应用于对照胚胎和基因敲低胚胎，以重建核心中胚层和内胚层调控回路。然后将那些推断网络与一部分基因已知的背腹空间表达模式结合起来，以预测其他基因的空间表达模式。两种模型都能够预测一些核心中胚层和内胚层基因的空间表达模式，但有趣的是针对不同的基因子集，这表明没有一个模型足以概括所有的空间模式，但它们对于所捕获的模式是互补的。
所提出的基因网络推理与空间模式预测相结合的方法为阐明控制胚胎发育中时空动态的调控回路提供了额外的验证层面。
