Guan Jinting, Zhuang Yan, Kang Yue, Ji Guoli
Department of Automation, Xiamen University, Xiamen, China.
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
Front Genet. 2022 May 11;13:865371. doi: 10.3389/fgene.2022.865371. eCollection 2022.
Human brain-related disorders, such as autism spectrum disorder (ASD), are often characterized by cell heterogeneity, as the cell atlas of brains consists of diverse cell types. There are commonality and specificity in gene expression among different cell types of brains; hence, there may also be commonality and specificity in dysregulated gene expression affected by ASD among brain cells. Moreover, as genes interact together, it is important to identify shared and cell-type-specific ASD-related gene modules for studying the cell heterogeneity of ASD. To this end, we propose integrative regularized non-negative matrix factorization (iRNMF) by imposing a new regularization based on integrative non-negative matrix factorization. Using iRNMF, we analyze gene expression data of multiple cell types of the human brain to obtain shared and cell-type-specific gene modules. Based on ASD risk genes, we identify shared and cell-type-specific ASD-associated gene modules. By analyzing these gene modules, we study the commonality and specificity among different cell types in dysregulated gene expression affected by ASD. The shared ASD-associated gene modules are mostly relevant to the functioning of synapses, while in different cell types, different kinds of gene functions may be specifically dysregulated in ASD, such as inhibitory extracellular ligand-gated ion channel activity in GABAergic interneurons and excitatory postsynaptic potential and ionotropic glutamate receptor signaling pathway in glutamatergic neurons. Our results provide new insights into the molecular mechanism and pathogenesis of ASD. The identification of shared and cell-type-specific ASD-related gene modules can facilitate the development of more targeted biomarkers and treatments for ASD.
人类大脑相关疾病，如自闭症谱系障碍（ASD），通常以细胞异质性为特征，因为大脑的细胞图谱由多种细胞类型组成。大脑不同细胞类型之间在基因表达上存在共性和特异性；因此，在受ASD影响的基因表达失调中，脑细胞之间可能也存在共性和特异性。此外，由于基因相互作用，识别共享的和细胞类型特异性的ASD相关基因模块对于研究ASD的细胞异质性很重要。为此，我们通过基于整合非负矩阵分解施加一种新的正则化来提出整合正则化非负矩阵分解（iRNMF）。使用iRNMF，我们分析人类大脑多种细胞类型的基因表达数据，以获得共享的和细胞类型特异性的基因模块。基于ASD风险基因，我们识别共享的和细胞类型特异性的ASD相关基因模块。通过分析这些基因模块，我们研究受ASD影响的基因表达失调中不同细胞类型之间的共性和特异性。共享的ASD相关基因模块大多与突触功能有关，而在不同细胞类型中，不同种类的基因功能在ASD中可能会特异性失调，例如GABA能中间神经元中的抑制性细胞外配体门控离子通道活性以及谷氨酸能神经元中的兴奋性突触后电位和离子型谷氨酸受体信号通路。我们的结果为ASD的分子机制和发病机制提供了新的见解。识别共享的和细胞类型特异性的ASD相关基因模块可以促进开发更有针对性的ASD生物标志物和治疗方法。
