替代拼接事件作为运动学习缺陷的外围生物标志物,由不良的产前环境引起
Dipankar J Dutta1, Junko Sasaki1,2, Ankush Bansal1
1Center for Neuroscience Research, Children's National Hospital, Washington, DC 20010.
概括
淋巴细胞RNA中的替代拼接 (AS) 提供了有前途的生物标志物,用于预测不良怀孕导致的神经行为缺陷. 深度学习有效地识别了这些AS事件,有助于对受影响儿童的早期诊断和干预.
科学领域:
- 神经科学是一个神经科学.
- 遗传学 遗传学 是一个
- 计算生物学 计算生物学
背景情况:
- 来自不良怀孕的儿童的神经行为缺陷缺乏准确的预测生物标志物.
- 孕妇在怀孕期间的饮酒和糖尿病会导致严重的神经发育问题.
- 预测赤字严重程度需要可靠的生物签名和识别工具.
研究的目的:
- 确定外围生物标志物,用于预测不良怀孕后代的运动学习缺陷.
- 评估深度学习模型在识别这些生物标志物的有效性.
- 探索神经行为缺陷的潜在分子机制.
主要方法:
- 从小鼠模型中分析淋巴细胞RNA中的替代拼接 (AS) 模式.
- 开发和培训一个深度学习模型来预测运动学习缺陷.
- 应用沙普利值分析来解释深度学习模型的预测.
- 使用AlphaFold2.2.进行基因本体学和结构功能分析.
主要成果:
- 淋巴细胞RNAAS模式的显著变化作为运动学习缺陷的精确外围生物标志物.
- 一个深度学习模型确定了29个常见的AS事件在产前酒精暴露 (PAE) 和糖尿病母亲 (OMD) 的后代中作为优越的预测因素.
- 沙普利值分析阐明了特定的AS事件对运动学习缺陷的贡献.
- 在PAE和OMD中,AS模式显示出相反的方向,表明差异性RNA结合蛋白表达.
结论:
- 淋巴细胞RNA的替代拼接是发现神经行为缺陷的外围生物标志物的宝贵资源.
- 深度学习为识别这些生物标志物提供了有效的计算工具.
- 这种方法有可能诊断和管理暴露于各种不良怀孕条件下的儿童的神经发育问题.
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