Wang Li-Juan, Li Xiao-Xiao, Hou Jie, Song Xin-Hua, Xie Wen-Hai, Shen Liang
Zibo Key Laboratory of New Drug Development of Neurodegenerative Diseases, Shandong Provincial Research Center for Bioinformatics Engineering and Technique, Institute of Biomedical Research, Shandong University of Technology, Zibo, China.
School of Life Sciences, Shandong University of Technology, Zibo, China.
Front Genet. 2020 Sep 4;11:563798. doi: 10.3389/fgene.2020.563798. eCollection 2020.
cell fate reprogramming has emerged as a new method for understanding cell plasticity and as potential treatment for tissue regeneration. Highly efficient and precise reprogramming requires fully understanding of the transcriptomes which function within different cell types. Here, we adopt weighted gene co-expression network analysis (WGCNA) to explore the molecular mechanisms of self-renewal in several well-known stem cell types, including embryonic stem cells (ESC), primordial germ cells (PGC), spermatogonia stem cells (SSC), neural stem cells (NSC), mesenchymal stem cells (MSC), and hematopoietic stem cells (HSC). We identified 37 core genes that were up-regulated in all of the stem cell types examined, as well as stem cell correlated gene co-expression networks. The validation of the co-expression genes revealed a continued protein-protein interaction network that included 823 nodes and 3113 edges. Based on the topology, we identified six densely connected regions within the continued protein-protein interaction network. The SSC specific genes , , and bridged four densely connected regions that consisted primarily of HSC-, NSC-, and MSC-correlated genes. The expression levels of identified stem cell related transcription factors were confirmed consistent with bioinformatics prediction in ESCs and NSCs by qPCR. Exploring the mechanisms underlying adult stem cell self-renewal will aid in the understanding of stem cell pool maintenance and will promote more accurate and efficient strategies for tissue regeneration and repair.
细胞命运重编程已成为一种理解细胞可塑性的新方法以及组织再生的潜在治疗手段。高效且精确的重编程需要充分了解在不同细胞类型中发挥作用的转录组。在此，我们采用加权基因共表达网络分析（WGCNA）来探究几种知名干细胞类型（包括胚胎干细胞（ESC）、原始生殖细胞（PGC）、精原干细胞（SSC）、神经干细胞（NSC）、间充质干细胞（MSC）和造血干细胞（HSC））自我更新的分子机制。我们鉴定出在所有检测的干细胞类型中均上调的37个核心基因以及与干细胞相关的基因共表达网络。对共表达基因的验证揭示了一个包含823个节点和3113条边的持续蛋白质 - 蛋白质相互作用网络。基于拓扑结构，我们在持续的蛋白质 - 蛋白质相互作用网络中确定了六个紧密连接的区域。SSC特异性基因 、 和 连接了四个主要由与HSC、NSC和MSC相关的基因组成的紧密连接区域。通过qPCR证实，在胚胎干细胞和神经干细胞中，所鉴定的干细胞相关转录因子的表达水平与生物信息学预测一致。探索成体干细胞自我更新的潜在机制将有助于理解干细胞库的维持，并将促进更准确、高效的组织再生和修复策略。
