Department of Automation, Xiamen University, Xiamen, China.
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
J Transl Med. 2021 Jan 6;19(1):20. doi: 10.1186/s12967-020-02690-5.
Genome-wide association studies have identified genetic variants associated with the risk of brain-related diseases, such as neurological and psychiatric disorders, while the causal variants and the specific vulnerable cell types are often needed to be studied. Many disease-associated genes are expressed in multiple cell types of human brains, while the pathologic variants affect primarily specific cell types. We hypothesize a model in which what determines the manifestation of a disease in a cell type is the presence of disease module comprised of disease-associated genes, instead of individual genes. Therefore, it is essential to identify the presence/absence of disease gene modules in cells.
To characterize the cell type-specificity of brain-related diseases, we construct human brain cell type-specific gene interaction networks integrating human brain nucleus gene expression data with a referenced tissue-specific gene interaction network. Then from the cell type-specific gene interaction networks, we identify significant cell type-specific disease gene modules by performing statistical tests.
Between neurons and glia cells, the constructed cell type-specific gene networks and their gene functions are distinct. Then we identify cell type-specific disease gene modules associated with autism spectrum disorder and find that different gene modules are formed and distinct gene functions may be dysregulated in different cells. We also study the similarity and dissimilarity in cell type-specific disease gene modules among autism spectrum disorder, schizophrenia and bipolar disorder. The functions of neurons-specific disease gene modules are associated with synapse for all three diseases, while those in glia cells are different. To facilitate the use of our method, we develop an R package, CtsDGM, for the identification of cell type-specific disease gene modules.
The results support our hypothesis that a disease manifests itself in a cell type through forming a statistically significant disease gene module. The identification of cell type-specific disease gene modules can promote the development of more targeted biomarkers and treatments for the disease. Our method can be applied for depicting the cell type heterogeneity of a given disease, and also for studying the similarity and dissimilarity between different disorders, providing new insights into the molecular mechanisms underlying the pathogenesis and progression of diseases.
全基因组关联研究已经确定了与脑相关疾病（如神经和精神疾病）风险相关的遗传变异，而因果变异和特定的易损细胞类型通常需要研究。许多疾病相关基因在人类大脑的多种细胞类型中表达，而病理变异主要影响特定的细胞类型。我们假设一种模型，即决定疾病在细胞类型中表现的是由疾病相关基因组成的疾病模块的存在，而不是单个基因。因此，识别细胞中疾病基因模块的存在/缺失至关重要。
为了描述与大脑相关的疾病的细胞类型特异性，我们构建了人类大脑细胞类型特异性基因相互作用网络，该网络整合了人类大脑核基因表达数据和参考组织特异性基因相互作用网络。然后，我们通过执行统计检验从细胞类型特异性基因相互作用网络中识别出显著的细胞类型特异性疾病基因模块。
神经元和神经胶质细胞之间，构建的细胞类型特异性基因网络及其基因功能是不同的。然后，我们确定了与自闭症谱系障碍相关的细胞类型特异性疾病基因模块，发现不同的基因模块在不同的细胞中形成，不同的基因功能可能被失调。我们还研究了自闭症谱系障碍、精神分裂症和双相情感障碍之间细胞类型特异性疾病基因模块的相似性和差异性。神经元特异性疾病基因模块的功能与所有三种疾病的突触相关，而胶质细胞中的功能则不同。为了便于使用我们的方法，我们开发了一个 R 包 CtsDGM，用于识别细胞类型特异性疾病基因模块。
这些结果支持我们的假设，即疾病通过形成具有统计学意义的疾病基因模块在细胞类型中表现出来。识别细胞类型特异性疾病基因模块可以促进针对该疾病的更具针对性的生物标志物和治疗方法的发展。我们的方法可用于描述给定疾病的细胞类型异质性，也可用于研究不同疾病之间的相似性和差异性，为疾病发病机制和进展的分子机制提供新的见解。
