美国哈佛医学院Sandeep Robert Datta研究团队通过Motion Sequencing（MoSeq）揭示了药物对行为的影响。2020年9月21日，《自然-神经科学》发表了这项成果。

研究人员利用MoSeq有效地分析了一组包涵近700只小鼠给予神经活性和精神活性药物引起的行为差异及相似性。MoSeq识别出各个药物的特征“音节”，研究人员利用这一发现揭示了自闭症谱系障碍小鼠模型中既定治疗方法和候选治疗方法的特定靶向和脱靶作用。

这些结果表明，MoSeq可以有效的整合大规模行为数据，并验证了行为基本模块化的功能，这表明行为“音节”代表了一类新的可药物化靶点。

据了解，了解基因、药物和神经回路如何影响行为需要有效地分析有关复杂行为数据集中相似性和差异性的信息。Motion Sequencing（MoSeq）是一种受伦理学启发而产生的行为分析算法，可识别三维鼠标肢体语言（称为“音节”）的模块化组件。

附：英文原文

Title: Revealing the structure of pharmacobehavioral space through motion sequencing

Author: Alexander B. Wiltschko, Tatsuya Tsukahara, Ayman Zeine, Rockwell Anyoha, Winthrop F. Gillis, Jeffrey E. Markowitz, Ralph E. Peterson, Jesse Katon, Matthew J. Johnson, Sandeep Robert Datta

Issue&Volume: 2020-09-21

Abstract: Understanding how genes, drugs and neural circuits influence behavior requires the ability to effectively organize information about similarities and differences within complex behavioral datasets. Motion Sequencing (MoSeq) is an ethologically inspired behavioral analysis method that identifies modular components of three-dimensional mouse body language called ‘syllables’. Here, we show that MoSeq effectively parses behavioral differences and captures similarities elicited by a panel of neuroactive and psychoactive drugs administered to a cohort of nearly 700 mice. MoSeq identifies syllables that are characteristic of individual drugs, a finding we leverage to reveal specific on- and off-target effects of both established and candidate therapeutics in a mouse model of autism spectrum disorder. These results demonstrate that MoSeq can meaningfully organize large-scale behavioral data, illustrate the power of a fundamentally modular description of behavior and suggest that behavioral syllables represent a new class of druggable target.

DOI: 10.1038/s41593-020-00706-3

Source: https://www.nature.com/articles/s41593-020-00706-3