We recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.

我们最近将支链氨基酸转移酶 1 ( BCAT1 ) 功能障碍与运动障碍帕金森病 (PD) 联系起来，并发现线虫中 RNAi 介导的神经元bcat-1敲低会导致异常的痉挛样“卷曲”行为年龄。在这里，我们报告了基于机器学习的工作流程的开发及其在发现潜在的帕金森病新疗法中的应用。除了简化量化和保持较低的数据开销之外，我们简单的分段训练量化平台还可以在训练卷积神经网络时对静态图像进行全自动评分。我们训练了一个高度可靠的神经网络来检测和分类蠕虫姿势，以便进行高通量卷曲分析，而无需用户干预或事后检查。在对 50 种 FDA 批准的药物进行的概念验证筛选中，enasidenib、ethosuximide、二甲双胍和 nitisinone 被确定为潜在的 PD 晚期干预药物的候选药物。这些发现表明我们的高通量平台可用于对蠕虫姿势进行自动评分，特别是发现 PD 的潜在候选治疗方法。