Transcriptional profiling reveals the transcription factor networks regulating the survival of striatal neurons

• Published in: Cell death & disease Vol. 12; no. 3; p. 262
• Main Authors: Yang, Lin, Su, Zihao, Wang, Ziwu, Li, Zhenmeiyu, Shang, Zicong, Du, Heng, Liu, Guoping, Qi, Dashi, Yang, Zhengang, Xu, Zhejun, Zhang, Zhuangzhi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.03.2021; Springer Nature B.V; Nature Publishing Group
• Subjects: 13/1; 13/2; 38/39; 38/91; 631/378; 631/378/1934; 631/378/2571/1696; 64/60; 82/1; Animals; Antibodies; Apoptosis; Biochemistry; Biomedical and Life Sciences; Case-Control Studies; Cell Biology; Cell Culture; Corpus Striatum - pathology; Databases, Genetic; Embryogenesis; Eye Proteins - genetics; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Developmental; Gene Regulatory Networks; Genetic Predisposition to Disease; Homeobox Protein SIX3; Homeodomain Proteins - genetics; Humans; Huntington Disease - genetics; Huntington Disease - pathology; Huntington's disease; Huntingtons disease; Immunology; Learning algorithms; Life Sciences; Machine Learning; Mice; Mice, Knockout; Neostriatum; Nerve Tissue Proteins - genetics; Phenotype; RNA-Seq; Six3 gene; Transcription factors; Transcription Factors - genetics; Transcriptome; Transcriptomes; Transcriptomics
• ISSN: 2041-4889; 2041-4889
• DOI: 10.1038/s41419-021-03552-8

The striatum is structurally highly diverse, and its organ functionality critically depends on normal embryonic development. Although several studies have been conducted on the gene functional changes that occur during striatal development, a system-wide analysis of the underlying molecular changes is lacking. Here, we present a comprehensive transcriptome profile that allows us to explore the trajectory of striatal development and identify the correlation between the striatal development and Huntington’s disease (HD). Furthermore, we applied an integrative transcriptomic profiling approach based on machine learning to systematically map a global landscape of 277 transcription factor (TF) networks. Most of these TF networks are linked to biological processes, and some unannotated genes provide information about the corresponding mechanisms. For example, we found that the Meis2 and Six3 were crucial for the survival of striatal neurons, which were verified using conditional knockout (CKO) mice. Finally, we used RNA-Seq to speculate their downstream targets.