Integrative modeling defines the Nova splicing-regulatory network and its combinatorial controls: Exon array data

The control of RNA alternative splicing is critical for generating biological diversity. Despite emerging genome-wide technologies to study RNA complexity, reliable and comprehensive RNA-regulatory networks have not been defined. Here we used Bayesian networks to probabilistically model diverse datasets and predict the target networks of specific regulators. We applied this strategy to identify ~700 alternative splicing events directly regulated by the neuron-specific factor Nova in the mouse brain, integrating RNA-binding data, splicing microarray data, Nova-binding motifs, and evolutionary signatures. The resulting integrative network revealed combinatorial regulation by Nova and the neuronal splicing factor Fox, interplay between phosphorylation and splicing, and potential links to neurologic disease. Thus we have developed a general approach to understanding mammalian RNA regulation at the systems level. RNA from the whole brain or spinal cord of 4 wild type and 4 Nova1/2 double KO (dKO) E18.5 CD1 mice. One array per biological replicate.RNA可变剪接的调控对于产生生物多样性至关重要。尽管目前已涌现出可用于研究RNA复杂性的全基因组技术，但可靠且全面的RNA调控网络仍未被明确界定。本研究借助贝叶斯网络（Bayesian networks）对多组数据集进行概率建模，以此预测特定调控因子的靶标网络。我们将该策略应用于小鼠脑中由神经元特异性因子Nova直接调控的约700个可变剪接事件的鉴定工作，整合了RNA结合数据、剪接微阵列数据、Nova结合基序以及进化特征。所得到的整合型调控网络揭示了Nova与神经元剪接因子Fox的组合调控模式、磷酸化与剪接之间的相互作用，以及与神经系统疾病的潜在关联。由此，我们开发出一种可用于系统层面解析哺乳动物RNA调控机制的通用方法。实验材料取自4只野生型与4只Nova1/2双敲除（double KO，简称dKO）E18.5胎龄CD1小鼠的全脑或脊髓组织，每个生物学重复对应一张微阵列芯片。