Decoding ASXL3: A Novel Biomarker and Treatment for Neurodevelopmental Disorders

Overview

A recent study revealed that 33% of individuals with neurodevelopmental disorders (NDDs) carry ASXL3 mutations, although its function was previously unknown. To investigate, we used CRISPR to engineer three H9 embryonic stem-cell lines with distinct genotypes: wild-type (ASXL3 +/+), heterozygous (ASXL3 +/ -), and homozygous knockout (ASXL3 -/-). The successful generation of these cell lines was confirmed through PCR and DNA sequencing. Neural rosettes and organoids were derived from these lines to study growth and differentiation using imaging and RNA sequencing. ASXL3 -/- rosettes and organoids exhibited accelerated growth rates, with a higher proportion of Ki67 -positive proliferative cells, indicating an expansion of neural progenitors. However, ASXL3 -/- organoids showed impaired neuronal differentiation, particularly in layer five cortical neurons, with significantly fewer BCL11B -positive cells. Differential RNA sequencing further validated increased proliferation and reduced differentiation in ASXL3 -/- cells. These findings identified ASXL3 as a neuronal stem cell gate controller, regulating the balance between proliferation and differentiation and emphasizing its essential role in brain development. ASXL3 -/- cells were also established as a novel biomarker for NDDs. The loss of layer five neurons aligns with clinical deficits in communication and fine motor skills. To address these defects, we treated ASXL3 -/- cells with fibroblas t growth factor (FGF) at 10, 20, and 30 ng/ml concentrations. Treatment with 30 ng/ml FGF successfully rescued neuronal differentiation with 92% efficiency, representing the first -ever treatment for ASXL3 -associated defects. These findings offer critical i nsights into ASXL3’s role and its therapeutic potential in mitigating NDD symptoms. MSST Transformer: A Novel Multimodal Spatial-Spectral-Temporal Transformer for Time- series Hyperspectral Imaging in Plant Growth Modeling Michael Hua Cranbrook Kingswood School, Bloomfield Hills, MI Mentor Dr. Zichun Zhong, Wayne State University Controlled environmental agriculture provides innovative methods for cultivating plants to address challenges like food security, environmental sustainability, and urban agriculture. Precision control of environmental conditions has the potential to signif icantly improve plant growth. Accurate modeling of the interaction between the plant and its environment is essential for implementing precision control. Hyperspectral imaging has high potential for this purpose because it can capture detailed spectral inf ormation across a wide range of wavelengths from plants. However, the higher dimensionality, larger sizes, and complex nature of hyperspectral images pose a significant challenge if used for temporal studies of plant growth. Subtle variations in plant prop erties and physiological changes need to be identified and correlated to provide accurate plant growth assessment. This paper introduces a novel multimodal spatial -spectral- temporal transformer designed to analyze the space, spectrum, and time domains of t he hyperspectral images. The transformer is trained on hyperspectral images of growing plants, corresponding light treatments, and final biomass outcomes to learn a statistical model which captures the intrinsic relationship between plant growth patterns a nd environmental factors. Specifically, the spatial -spectral transformer divides the input image into 3D hyperspectral patches and utilizes a novel 3D -aware positional encoding and self -attention mechanisms to capture global dependencies among patches. Sub sequently, the temporal transformer models time-variant spatial -spectral representations and their long -range relationships. Finally, the multimodal cross-attention mechanism explores the interaction between environmental features and spatial -spectral-temporal representations from hyperspectral plant images, resulting in an accurate plant growth model. Comprehensive experiments demonstrate the effectiveness and superiority of the proposed network.

Competition history

• JSHS 2025 Category not listed

Resources

• National JSHS abstract book ↗

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