Alternative splicing events as peripheral biomarkers for motor learning deficit caused by adverse prenatal environments.
Alternative splicing events as peripheral biomarkers for motor learning deficit caused by adverse prenatal environments.
复制标题
选择性剪接事件作为不良产前环境引起的运动学习缺陷的外周生物标志物。
DOI：
10.1073/pnas.2304074120
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发表时间：
2023
影响因子：
11.1
通讯作者：
Torii,M
中科院分区：
文献类型：
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作者：
Dutta,DipankarJ;Sasaki,Junko;Bansal,Ankush;Sugai,Keiji;Yamashita,Satoshi;Li,Guojiao;Lazarski,Christopher;Wang,Li;Sasaki,Toru;Yamashita,Chiho;Carryl,Heather;Suzuki,Ryo;Odawara,Masato;ImamuraKawasawa,Yuka;Rakic,Pasko;Torii,M
因不良妊娠而出生的儿童神经行为缺陷的严重程度，如母亲饮酒和糖尿病，并不总是与逆境的持续时间和强度相关。因此，用于准确预测神经行为缺陷严重程度的生物学标志以及用于可靠识别此类生物标志物的强大工具具有迫切的临床需求。在这里，我们证明了后代淋巴细胞RNA选择性剪接(AS)模式的显著变化可以作为产前酒精暴露(PAE)和糖尿病母亲(OMD)后代小鼠运动学习缺陷的准确外周生物标志物。一个经过适当训练的深度学习模型确定了29个PAE和OMD共同的事件，作为运动学习障碍的更好的预测指标，而不是PAE或OMD特有的事件。Shapley-Value分析，一种博弈论算法，破译了训练有素的深度学习模型在其输入、事件和输出、运动学习表现之间的学习关联。深度学习模型输入的Shapley值确定了29个常见AS事件对运动学习障碍的相对贡献。使用Alphafold2算法的基因本体论和预测性结构-功能分析支持了这些分子在早期大脑发育和功能中关键作用的现有证据。大多数AS事件在PAE和OMD中的方向相反，可能是由于PAE和OMD中RNA结合蛋白的差异表达所致。综上所述，本研究假设淋巴细胞RNA是丰富的资源，深度学习是发现不同不良妊娠儿童神经行为缺陷的外周生物标志物的有效工具。
Severity of neurobehavioral deficits in children born from adverse pregnancies, such as maternal alcohol consumption and diabetes, does not always correlate with the adversity’s duration and intensity. Therefore, biological signatures for accurate prediction of the severity of neurobehavioral deficits, and robust tools for reliable identification of such biomarkers, have an urgent clinical need. Here, we demonstrate that significant changes in the alternative splicing (AS) pattern of offspring lymphocyte RNA can function as accurate peripheral biomarkers for motor learning deficits in mouse models of prenatal alcohol exposure (PAE) and offspring of mother with diabetes (OMD). An aptly trained deep-learning model identified 29 AS events common to PAE and OMD as superior predictors of motor learning deficits than AS events specific to PAE or OMD. Shapley-value analysis, a game-theory algorithm, deciphered the trained deep-learning model’s learnt associations between its input, AS events, and output, motor learning performance. Shapley values of the deep-learning model’s input identified the relative contribution of the 29 common AS events to the motor learning deficit. Gene ontology and predictive structure–function analyses, using Alphafold2 algorithm, supported existing evidence on the critical roles of these molecules in early brain development and function. The direction of most AS events was opposite in PAE and OMD, potentially from differential expression of RNA binding proteins in PAE and OMD. Altogether, this study posits that AS of lymphocyte RNA is a rich resource, and deep-learning is an effective tool, for discovery of peripheral biomarkers of neurobehavioral deficits in children of diverse adverse pregnancies.