The Polycomb group protein Bmi1 is a key regulator of self-renewal of embryonic and adult central nervous system stem cells, and its overexpression has been shown to occur in several types of brain tumors. In a Cre/LoxP-based conditional transgenic mouse model, we show that fine-tuning of Bmi1 expression in embryonic neural stem cell (NSC) is sufficient to increase their proliferation and self-renewal potential both in vitro and in vivo. This is linked to downregulation of both the ink4a/ARF and the p21/Foxg1 axes. However, increased and ectopic proliferation induced by overexpression of Bmi1 in progenitors committed toward a neuronal lineage during embryonic cortical development, triggers apoptosis through a survivin-mediated mechanism and leads to reduced brain size. Postnatally, however, increased self-renewal capacity of neural stem/progenitor cells (NSPC) is independent of Foxg1 and resistance to apoptosis is observed in neural progenitors derived from NSC-overexpressing Bmi1. Neoplastic transformation is absent in mice-overexpressing Bmi1 aged up to 20 months. These studies provide strong evidence that fine tuning of Bmi1 expression is a viable tool to increase self-renewal capacity of NSCs both in vitro and in vivo without eliciting neoplastic transformation of these cells.
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Cancer Med
January 2025
Faculty of Medical Sciences, Neuroscience Research Center, Lebanese University, Hadath, Lebanon.
Background: Glioblastoma (GBM) is the most common primary brain tumor in adults and has a median survival of less than 15 months. Advancements in the field of epigenetics have expanded our understanding of cancer biology and helped explain the molecular heterogeneity of these tumors. B-cell-specific Moloney murine leukemia virus insertion site-1 (Bmi-1) is a member of the highly conserved polycomb group (PcG) protein family that acts as a transcriptional repressor of multiple genes, including those that determine cell proliferation and differentiation.
View Article and Find Full Text PDFJ Cancer
January 2025
School of Stomatology, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China.
Dev Growth Differ
December 2024
Department of Biochemistry & Molecular Biology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, China.
Transcription factors collaborate with epigenetic regulatory factors to orchestrate cardiac differentiation for heart development, but the underlying mechanism is not fully understood. Here, we report that GATA-6 induces cardiac differentiation but peroxisome proliferator-activated receptor α (PPARα) reverses GATA-6-induced cardiac differentiation, possibly because GATA-6/PPARα recruits the polycomb protein complex containing EZH2/Ring1b/BMI1 to the promoter of the cardiac-specific α-myosin heavy chain (α-MHC) gene and suppresses α-MHC expression, which ultimately inhibits cardiac differentiation. Furthermore, Ring1b ubiquitylates PPARα and GATA-6.
View Article and Find Full Text PDFAdv Sci (Weinh)
December 2024
Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Promoting tumor cell senescence arrests the cell cycle of tumor cells and activates the immune system to eliminate these senescent cells, thereby suppressing tumor growth. Nevertheless, PD-L1 positive senescent tumor cells resist immune clearance and possess the ability to secret various cytokines and inflammatory factors that stimulate the growth of tumor cells. Consequently, drugs capable of both triggering senescence in tumor cells and concurrently diminishing the expression of PD-L1 to counteract immune evasion are urgently needed.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
December 2024
Medical Research Center, Affiliated Hospital 2 of Nantong University, Nantong, China.
Objective: Energy homeostasis is modulated by the hypothalamic is essential for obesity progression, however, the gene expression profiling remains to be fully understood.
Methods: GEO datasets were downloaded from the GEO website and analyzed by the R packages to obtain the DEGs. And, the WGCNA analysis and PPI networks of co-expressed DEGs were designed using STRING to get key genes.
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