ZIC1

Last update 23 Jan 2025

Basic Info

Synonyms

ZIC, Zic family member 1, ZIC1

+ [4]

Introduction

Acts as a transcriptional activator. Involved in neurogenesis. Plays important roles in the early stage of organogenesis of the CNS, as well as during dorsal spinal cord development and maturation of the cerebellum. Involved in the spatial distribution of mossy fiber (MF) neurons within the pontine gray nucleus (PGN). Plays a role in the regulation of MF axon pathway choice. Promotes MF migration towards ipsilaterally-located cerebellar territories. May have a role in shear flow mechanotransduction in osteocytes. Retains nuclear GLI1 and GLI3 in the cytoplasm. Binds to the minimal GLI-consensus sequence 5'-TGGGTGGTC-3' (By similarity).

100 Clinical Results associated with ZIC1

100 Translational Medicine associated with ZIC1

0 Patents (Medical) associated with ZIC1

450

Literatures (Medical) associated with ZIC1

01 Jan 2025·Nature Genetics

ZIC1 is a context-dependent medulloblastoma driver in the rhombic lip

Article

Author: Mack, Stephen C ; Mount, Christopher W ; Fong, Vernon ; Pollack, Ian F ; Northcott, Paul A ; Hendriske, Liam D ; Phillips, Joanna J ; Hamilton, Ronald L ; Tominaga, Teiji ; Richman, Cory M ; Daniels, Craig ; Garzia, Livia ; Morrissy, Sorana ; Saad, Ali G ; Cho, Byung-Kyu ; Dai, Shizhong ; Farooq, Hamza ; Abeysundara, Namal ; Lee, Ji-Yeoun ; Singh, Sheila K ; Lee, John J Y ; Palotta, Jonelle ; Leary, Sarah E S ; Gallo, Marco ; Lach, Boleslaw ; Rasnitsyn, Alex ; Loukides, James ; Bognár, László ; Chico Ponce de León, Fernando ; Das Gupta, Neha ; Visvanathan, Abhi ; Klekner, Almos ; Grajkowska, Wieslawa A ; Thompson, Reid C ; Kool, Marcel ; Luu, Betty ; Weiss, William A ; Batts, Melissa ; Bendel, Anne ; Millen, Kathleen J ; Liu, Miao ; You, Zhen ; Livingston, Bryn G ; Wang, Evan Y ; Kros, Johan M ; Cooper, Michael K ; Wang, Kyu-Chang ; Wu, Xiaochong ; Jabado, Nada ; Huang, L Frank ; Bailey, Swneke ; Lupien, Mathieu ; Suzuki, Hiromichi ; Haldipur, Parthiv ; Tao, Ran ; Korshunov, Andrey ; Kim, Seung-Ki ; Perezpeña-Diazconti, Mario ; Zadeh, Gelareh ; Perek-Polnik, Marta ; Cavalli, Florence M G ; Ra, Young-Shin ; Hadley, Jennifer ; Suarez, Raul ; Erickson, Anders W ; Ramaswamy, Vijay ; Kenney, Anna M ; Michealraj, Kulandaimanuvel Antony ; French, Pim J ; Taylor, Michael D ; Suva, Mario L

AbstractTranscription factors are frequent cancer driver genes, exhibiting noted specificity based on the precise cell of origin. We demonstrate that ZIC1 exhibits loss-of-function (LOF) somatic events in group 4 (G4) medulloblastoma through recurrent point mutations, subchromosomal deletions and mono-allelic epigenetic repression (60% of G4 medulloblastoma). In contrast, highly similar SHH medulloblastoma exhibits distinct and diametrically opposed gain-of-function mutations and copy number gains (20% of SHH medulloblastoma). Overexpression of ZIC1 suppresses the growth of group 3 medulloblastoma models, whereas it promotes the proliferation of SHH medulloblastoma precursor cells. SHH medulloblastoma ZIC1 mutants show increased activity versus wild-type ZIC1, whereas G4 medulloblastoma ZIC1 mutants exhibit LOF phenotypes. Distinct ZIC1 mutations affect cells of the rhombic lip in diametrically opposed ways, suggesting that ZIC1 is a critical developmental transcriptional regulator in both the normal and transformed rhombic lip and identifying ZIC1 as an exquisitely context-dependent driver gene in medulloblastoma.

01 Nov 2024·Ageing Research Reviews

Advances in understanding biomarkers and treating neurological diseases – Role of the cerebellar dysfunction and emerging therapies

Review

Author: Sivalingam, Azhagu Madhavan

Cerebellar dysfunction is increasingly recognized as a critical factor in various neurological diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). Research has revealed distinct cerebellar atrophy patterns in conditions such as AD and multiple system atrophy, and studies in mice have highlighted its impact on motor control and cognitive functions. Emerging research into autism spectrum disorder (ASD) has identified key targets, such as elevated levels of chemokine receptors and ZIC family genes. Biomarkers, including cerebrospinal fluid (CSF), genetic markers, and advances in AI and bioinformatics, are enhancing early diagnosis and personalized treatment across neurodegenerative disorders. Notable advancements include improved diagnostic tools, gene therapy, and novel clinical trials. Despite progress, challenges such as the bloodbrain barrier and neuroinflammation persist. Current therapies for AD, PD, HD, and ALS, including antisense oligonucleotides and stem cell treatments, show promise but require further investigation. A comprehensive approach that integrates diagnostic methods and innovative therapies is essential for effective management and improved patient outcomes.

01 Nov 2024·The Journal of Pathology

DNA methylation as a new tool for the differential diagnosis between T‐LBL and lymphocyte‐rich thymoma

Article

Author: Badoual, Cécile ; Spicuglia, Salvatore ; Girard, Nicolas ; Molina, Thierry Jo ; Balducci, Estelle ; Jamain, Stéphane ; Macintyre, Elizabeth ; Touzart, Aurore ; Pinton, Antoine ; Bruneau, Julie ; Garnier, Nathalie ; Andrieu, Guillaume P ; Besse, Benjamin ; Latapie, Violaine ; Chalabreysse, Lara ; Smith, Charlotte ; Asnafi, Vahid ; Gibault, Laure ; Latiri, Mehdi ; Marx, Alexander ; Plass, Christoph ; Belhocine, Mohamed ; de Montpreville, Vincent Thomas

AbstractT‐lymphoblastic lymphoma (T‐LBL) and thymoma are two rare primary tumors of the thymus deriving either from T‐cell precursors or from thymic epithelial cells, respectively. Some thymoma subtypes (AB, B1, and B2) display numerous reactive terminal deoxynucleotidyl transferase‐positive (TdT+) T‐cell precursors masking epithelial tumor cells. Therefore, the differential diagnosis between T‐LBL and TdT+ T‐lymphocyte‐rich thymoma could be challenging, especially in the case of needle biopsy. To distinguish between T‐LBL and thymoma‐associated lymphoid proliferations, we analyzed the global DNA methylation using two different technologies, namely MeDIP array and EPIC array, in independent samples series [17 T‐LBLs compared with one TdT+ lymphocyte‐rich thymoma (B1 subtype) and three normal thymi, and seven lymphocyte‐rich thymomas compared with 24 T‐LBLs, respectively]. In unsupervised principal component analysis (PCA), T‐LBL and thymoma samples clustered separately. We identified differentially methylated regions (DMRs) using MeDIP‐array and EPIC‐array datasets and nine overlapping genes between the two datasets considering the top 100 DMRs including ZIC1, TSHZ2, CDC42BPB, RBM24, C10orf53, and MACROD2. In order to explore the DNA methylation profiles in larger series, we defined a classifier based on these six differentially methylated gene promoters, developed an MS‐MLPA assay, and demonstrated a significant differential methylation between thymomas (hypomethylated; n = 48) and T‐LBLs (hypermethylated; n = 54) (methylation ratio median 0.03 versus 0.66, respectively; p < 0.0001), with MACROD2 methylation status the most discriminating. Using a machine learning strategy, we built a prediction model trained with the EPIC‐array dataset and defined a cumulative score taking into account the weight of each feature. A score above or equal to 0.4 was predictive of T‐LBL and conversely. Applied to the MS‐MLPA dataset, this prediction model accurately predicted diagnoses of T‐LBL and thymoma. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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ZIC1

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