帕金森氏症的扣带皮层髓鞘形成网络中断

IF 1.9 4区 生物学 Q4 CELL BIOLOGY
Song Xie, Jiajun Yang, Shenghui Huang, Yuanlan Fan, Tao Xu, Jiangshuang He, Jiahao Guo, Xiang Ji, Zhibo Wang, Peijun Li, Jiangfan Chen, Yi Zhang
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引用次数: 6

摘要

扣带皮层是保守的边缘系统的一部分,被认为是情绪和认知控制的中心。越来越多的证据表明,扣带皮层的受累对帕金森病(PD)的认知障碍有重要意义。然而,关于扣带皮层在PD发病机制中的机制研究有限。本研究对帕金森病患者的扣带皮层进行了转录组学和调控网络分析。富集和聚类分析表明,参与膜电位调控和谷氨酸受体信号通路的基因表达上调。重要的是,髓磷脂基因和少突胶质细胞发育途径明显下调,表明PD扣带皮层髓鞘形成被破坏。细胞类型特异性特征显示,髓鞘少突胶质细胞是PD扣带皮层受损的主要细胞类型。此外,扣带皮层髓鞘形成通路的下调在另一个路易体痴呆(DLB)的独立RNAseq队列中得到了共享和验证。结合ATACseq数据,利用基于树的机器学习算法进一步构建了32个转录因子(tf)和差异表达基因(deg)中466个靶基因的基因调控网络(grn)。几个转录因子,包括Olig2、Sox8、Sox10、E2F1和NKX6-2,被强调为子网络中的关键节点,它们控制着许多与髓磷脂形成和胶质瘤发生相关的重叠下游靶标。此外,作者通过qpcr在两种PD小鼠模型中验证了deg的子集。值得注意的是,其中7个基因TOX3、NECAB2 NOS1、CAPN3、NR4A2、E2F1和FOXP2在PD或神经退行性疾病中有关联,值得作为新的候选基因进一步研究。总之,我们的研究结果为髓鞘再生作为治疗脱髓鞘后PD的一种有希望的新方法提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Disrupted myelination network in the cingulate cortex of Parkinson's disease

Disrupted myelination network in the cingulate cortex of Parkinson's disease

The cingulate cortex is part of the conserved limbic system, which is considered as a hub of emotional and cognitive control. Accumulating evidence suggested that involvement of the cingulate cortex is significant for cognitive impairment of Parkinson's disease (PD). However, mechanistic studies of the cingulate cortex in PD pathogenesis are limited. Here, transcriptomic and regulatory network analyses were conducted for the cingulate cortex in PD. Enrichment and clustering analyses showed that genes involved in regulation of membrane potential and glutamate receptor signalling pathway were upregulated. Importantly, myelin genes and the oligodendrocyte development pathways were markedly downregulated, indicating disrupted myelination in PD cingulate cortex. Cell-type-specific signatures revealed that myelinating oligodendrocytes were the major cell type damaged in the PD cingulate cortex. Furthermore, downregulation of myelination pathways in the cingulate cortex were shared and validated in another independent RNAseq cohort of dementia with Lewy bodies (DLB). In combination with ATACseq data, gene regulatory networks (GRNs) were further constructed for 32 transcription factors (TFs) and 466 target genes among differentially expressed genes (DEGs) using a tree-based machine learning algorithm. Several transcription factors, including Olig2, Sox8, Sox10, E2F1, and NKX6-2, were highlighted as key nodes in a sub-network, which control many overlapping downstream targets associated with myelin formation and gliogenesis. In addition, the authors have validated a subset of DEGs by qPCRs in two PD mouse models. Notably, seven of these genes,TOX3, NECAB2 NOS1, CAPN3, NR4A2, E2F1 and FOXP2, have been implicated previously in PD or neurodegeneration and are worthy of further studies as novel candidate genes. Together, our findings provide new insights into the role of remyelination as a promising new approach to treat PD after demyelination.

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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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