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Uncovering Phenotypic Expansion in AXIN2-Related Disorders through Precision Animal Modeling.

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    New AXIN2 gene variants cause developmental disorders beyond oligodontia-colorectal cancer syndrome (ODCRCS). These pathogenic variants expand the known phenotype, impacting development and showing context-dependent effects on WNT signaling in model organisms.

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    Area of Science:

    • Genetics and Developmental Biology
    • Molecular Biology
    • Human Disease Genetics

    Background:

    • Heterozygous pathogenic variants in AXIN2 gene are linked to oligodontia-colorectal cancer syndrome (ODCRCS).
    • ODCRCS is characterized by missing teeth, colorectal cancer, and sometimes sparse hair/eyebrows.

    Purpose of the Study:

    • To identify and characterize novel AXIN2 variants and their associated phenotypes.
    • To investigate the functional impact of these variants on AXIN2 protein and WNT signaling.

    Main Methods:

    • Identified four individuals with de novo heterozygous AXIN2 variants.
    • Utilized structural modeling to predict variant effects on tankyrase binding.
    • Employed prime editing in mouse embryos and Drosophila models to assess in vivo and cell-specific functions.

    Main Results:

    • Identified two de novo AXIN2 variants (c.196G>A, p.(Glu66Lys) and c.199G>A, p.(Gly67Arg)) expanding the disorder phenotype.
    • Affected individuals presented with global developmental delay, microcephaly, and limb, ophthalmologic, and renal abnormalities in addition to ODCRCS features.
    • Mouse models showed perinatal lethality with skeletal abnormalities, while Drosophila models exhibited context-dependent gain- and loss-of-function activities.

    Conclusions:

    • Specific AXIN2 variants in the tankyrase-binding domain are pathogenic, causing a broader spectrum of congenital anomalies.
    • Variant pathogenicity is linked to disrupted tankyrase binding, affecting AXIN2 stability and WNT signaling.
    • The study highlights context-dependent functional effects of AXIN2 variants and proposes modeling strategies for variant of uncertain significance resolution.