FB2026_01 , released March 12, 2026
FB2026_01 , released March 12, 2026
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Citation
Jevitt, A., Huang, Y.C., Zhang, S.M., Chatterjee, D., Wang, X.F., Xie, G.Q., Deng, W.M. (2021). Modeling Notch-Induced Tumor Cell Survival in the Drosophila Ovary Identifies Cellular and Transcriptional Response to Nuclear NICD Accumulation.  Cells 10(9): 2222.
FlyBase ID
FBrf0251412
Publication Type
Research paper
Abstract
Notch is a conserved developmental signaling pathway that is dysregulated in many cancer types, most often through constitutive activation. Tumor cells with nuclear accumulation of the active Notch receptor, NICD, generally exhibit enhanced survival while patients experience poorer outcomes. To understand the impact of NICD accumulation during tumorigenesis, we developed a tumor model using the Drosophila ovarian follicular epithelium. Using this system we demonstrated that NICD accumulation contributed to larger tumor growth, reduced apoptosis, increased nuclear size, and fewer incidents of DNA damage without altering ploidy. Using bulk RNA sequencing we identified key genes involved in both a pre- and post- tumor response to NICD accumulation. Among these are genes involved in regulating double-strand break repair, chromosome organization, metabolism, like raptor, which we experimentally validated contributes to early Notch-induced tumor growth. Finally, using single-cell RNA sequencing we identified follicle cell-specific targets in NICD-overexpressing cells which contribute to DNA repair and negative regulation of apoptosis. This valuable tumor model for nuclear NICD accumulation in adult Drosophila follicle cells has allowed us to better understand the specific contribution of nuclear NICD accumulation to cell survival in tumorigenesis and tumor progression.
PubMed ID
PubMed Central ID
PMC8465586 (PMC) (EuropePMC)
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Secondary IDs
    Language of Publication
    English
    Additional Languages of Abstract
    Parent Publication
    Publication Type
    Journal
    Abbreviation
    Cells
    Title
    Cells
    ISBN/ISSN
    2073-4409
    Data From Reference
    Alleles (4)
    Genes (4)
    Human Disease Models (1)
    Insertions (1)
    Transgenic Constructs (3)