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Co-expression networks uncover regulation of splicing and transcription markers of disease

机译：共表达网络揭示疾病剪接和转录标志物的调节

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Gene co-expression networks based on gene expression data are usually used to capture biologically significant patterns, enabling the discovery of biomarkers and interpretation of regulatory relationships. However, the coordination of numerous splicing changes within and across genes can exert a substantial impact on the function of these genes. This is particularly impactful in studies of the properties of the nervous system, which can be masked in the networks that only assess the correlation between gene expression levels. A bioinformatics approach was developed to uncover the role of alternative splicing and associated transcriptional networks using RNA-seq profiles. Data from 40 samples, including control and two treatments associated with sensitivity to stimuli across two central nervous system regions that can present differential splicing, were explored. The gene expression and relative isoform levels were integrated into a transcriptome-wide matrix, and then Graphical Lasso was applied to capture the interactions between genes and isoforms. Next, functional enrichment analysis enabled the discovery of pathways dysregulated at the isoform or gene levels and the interpretation of these interactions within a central nervous region. In addition, a Bayesian biclustering strategy was used to reconstruct treatment-specific networks from gene expression profile, allowing the identification of hub molecules and visualization of highly connected modules of isoforms and genes in specific conditions. Our bioinformatics approach can offer comparable insights into the discovery of biomarkers and therapeutic targets for a wide range of diseases and conditions.
机译：基于基因表达数据的基因共表达网络通常用于捕获生物显着的模式，从而能够发现生物标志物和对监管关系的解释。然而，在基因内和跨基因内的许多剪接变化的协调可以对这些基因的功能产生显着影响。这对于神经系统性质的研究特别有影响，这可以在网络中掩蔽仅评估基因表达水平之间的相关性的网络中。开发了一种生物信息学方法，以利用RNA-SEQ型材揭示替代剪接和相关转录网络的作用。探讨了来自40个样品的数据，包括控制和两种与刺激敏感性的敏感性相关的治疗，这些治疗在两个中枢神经系统区域中可以呈现差异剪接的刺激。基因表达和相对同种型水平整合到转录组宽的基质中，然后施加图形套索以捕获基因和同种型之间的相互作用。接下来，功能性富集分析使得在同种型或基因水平下发现的途径和中枢神经区域内这些相互作用的解释。此外，贝叶斯双板策略用于重建来自基因表达谱的治疗特异性网络，允许在特定条件下识别集线器分子和高度连接的同种型模块和基因的可视化。我们的生物信息学方法可以为发现生物标志物和治疗目标的广泛疾病和病症提供相当的见解。

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