Developmental Temporal Patterns and Molecular Network Features in the Transcriptome of Rat Spinal Cord. (November 2021)

• Record Type: Journal Article
• Authors: Yang, Jian; Zhao, Lili; Yi, Sheng; Ding, Fei; Yang, Yumin; Liu, Yan; Wang, Yongjun; Liu, Mei; Xue, Chengbin; Xu, Lian; Gong, Leilei; Wang, Xinghui; Zhang, Yu; Yu, Bin; Ming, Guo-li; Gu, Xiaosong
• Is Part Of: Engineering. Volume 7:Number 11(2021)
• Page Start: 1592
• Page End: 1602
• Publication Date: 2021-11
• Subjects: Development -- Gene expression pattern -- Innate immunity -- Spinal cord -- Transcriptome; Engineering -- Periodicals; Engineering -- China -- Periodicals; 620.005
• DOI: 10.1016/j.eng.2021.10.001
• Languages: English
• ISSNs: 2095-8099

Abstract

Abstract: The molecular network features of spinal cord development that are integral to tissue engineering remain poorly understood in placental mammals, especially in terms of their relationships with vital biological processes such as regeneration. Here, using a large-scale temporal transcriptomic analysis of rat spinal cord from the embryonic stage to adulthood, we show that fluctuating RNA expression levels reflect highly active transcriptional regulation, which may initiate spinal cord patterning. We also demonstrate that microRNAs (miRNAs) and transcriptional factors exhibit a mosaic profile based on their expression patterns, while differential alternative splicing events reveal that alternative splicing may be a driving force for the development of the node of Ranvier. Our study also supports the existence of a negative correlation between innate immunity and intrinsic growth capacity. Epigenetic modifications appear to perform their respective regulatory functions at different stages of development, while guanine nucleotide-binding protein (G protein)-coupled receptors (including olfactory receptors (ORs)) may perform pleiotropic roles in axonal growth. This study provides a valuable resource for investigating spinal cord development and complements the increasing number of single-cell datasets. These findings also provide a genetic basis for the development of novel tissue engineering strategies.