School of Engineering, Dali University, Dali, Yunnan, China.
Beijing CapitalBio Pharma Technology Co.,Ltd., Beijing, China.
BMC Bioinformatics. 2024 Sep 27;25(1):307. doi: 10.1186/s12859-024-05933-0.
Autism spectrum disorder (ASD) is a class of complex neurodevelopment disorders with high genetic heterogeneity. Long non-coding RNAs (lncRNAs) are vital regulators that perform specific functions within diverse cell types and play pivotal roles in neurological diseases including ASD. Therefore, exploring lncRNA regulation would contribute to deciphering ASD molecular mechanisms. Existing computational methods utilize bulk transcriptomics data to identify lncRNA regulation in all of samples, which could reveal the commonalities of lncRNA regulation in ASD, but ignore the specificity of lncRNA regulation across various cell types.
Here, we present Cycle (Cell type-specific lncRNA regulatory network) to construct the landscape of cell type-specific lncRNA regulation in ASD. We have found that each ASD cell type is unique in lncRNA regulation, and more than one-third and all cell type-specific lncRNA regulatory networks are characterized as scale-free and small-world, respectively. Across 17 ASD cell types, we have discovered 19 rewired and 11 stable modules, along with eight rewired and three stable hubs within the constructed cell type-specific lncRNA regulatory networks. Enrichment analysis reveals that the discovered rewired and stable modules and hubs are closely related to ASD. Furthermore, more similar ASD cell types tend to be connected with higher strength in the constructed cell similarity network. Finally, the comparison results demonstrate that Cycle is a potential method for uncovering cell type-specific lncRNA regulation.
Overall, these results illustrate that Cycle is a promising method to model the landscape of cell type-specific lncRNA regulation, and provides insights into understanding the heterogeneity of lncRNA regulation between various ASD cell types.
自闭症谱系障碍（ASD）是一类具有高度遗传异质性的复杂神经发育障碍。长链非编码 RNA（lncRNA）是重要的调节因子，在不同的细胞类型中发挥特定功能，并在包括 ASD 在内的神经疾病中发挥关键作用。因此，探索 lncRNA 的调控作用将有助于破译 ASD 的分子机制。现有的计算方法利用批量转录组学数据来识别所有样本中的 lncRNA 调控，这可以揭示 ASD 中 lncRNA 调控的共性，但忽略了不同细胞类型中 lncRNA 调控的特异性。
在这里，我们提出 Cycle（细胞类型特异性 lncRNA 调控网络）来构建 ASD 中细胞类型特异性 lncRNA 调控的全景图。我们发现，每个 ASD 细胞类型在 lncRNA 调控方面都是独特的，超过三分之一和所有细胞类型特异性 lncRNA 调控网络分别具有无标度和小世界的特征。在 17 种 ASD 细胞类型中，我们发现了 19 个重连和 11 个稳定模块，以及在构建的细胞类型特异性 lncRNA 调控网络中 8 个重连和 3 个稳定的枢纽。富集分析表明，发现的重连和稳定模块和枢纽与 ASD 密切相关。此外，构建的细胞相似性网络中具有更高连接强度的 ASD 细胞类型更为相似。最后，比较结果表明 Cycle 是一种发现细胞类型特异性 lncRNA 调控的潜在方法。
总的来说，这些结果表明 Cycle 是一种建模细胞类型特异性 lncRNA 调控全景的有前途的方法，并为理解各种 ASD 细胞类型之间 lncRNA 调控的异质性提供了新的视角。
