Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, Vancouver, BC, Canada.
Present Address: Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 2T9, Canada.
BMC Biol. 2024 May 23;22(1):121. doi: 10.1186/s12915-024-01919-9.
Huntington disease (HD) is a neurodegenerative disorder with complex motor and behavioural manifestations. The Q175 knock-in mouse model of HD has gained recent popularity as a genetically accurate model of the human disease. However, behavioural phenotypes are often subtle and progress slowly in this model. Here, we have implemented machine-learning algorithms to investigate behaviour in the Q175 model and compare differences between sexes and disease stages. We explore distinct behavioural patterns and motor functions in open field, rotarod, water T-maze, and home cage lever-pulling tasks.
In the open field, we observed habituation deficits in two versions of the Q175 model (zQ175dn and Q175FDN, on two different background strains), and using B-SOiD, an advanced machine learning approach, we found altered performance of rearing in male manifest zQ175dn mice. Notably, we found that weight had a considerable effect on performance of accelerating rotarod and water T-maze tasks and controlled for this by normalizing for weight. Manifest zQ175dn mice displayed a deficit in accelerating rotarod (after weight normalization), as well as changes to paw kinematics specific to males. Our water T-maze experiments revealed response learning deficits in manifest zQ175dn mice and reversal learning deficits in premanifest male zQ175dn mice; further analysis using PyMouseTracks software allowed us to characterize new behavioural features in this task, including time at decision point and number of accelerations. In a home cage-based lever-pulling assessment, we found significant learning deficits in male manifest zQ175dn mice. A subset of mice also underwent electrophysiology slice experiments, revealing a reduced spontaneous excitatory event frequency in male manifest zQ175dn mice.
Our study uncovered several behavioural changes in Q175 mice that differed by sex, age, and strain. Our results highlight the impact of weight and experimental protocol on behavioural results, and the utility of machine learning tools to examine behaviour in more detailed ways than was previously possible. Specifically, this work provides the field with an updated overview of behavioural impairments in this model of HD, as well as novel techniques for dissecting behaviour in the open field, accelerating rotarod, and T-maze tasks.
亨廷顿病（HD）是一种具有复杂运动和行为表现的神经退行性疾病。Q175 敲入小鼠模型作为人类疾病的一种遗传准确模型，近来受到了广泛关注。然而，该模型中的行为表型通常较为微妙且进展缓慢。在这里，我们使用机器学习算法来研究 Q175 模型中的行为，并比较性别和疾病阶段之间的差异。我们在旷场、转棒、水 T 迷宫和笼内杠杆拉动任务中探索了不同的行为模式和运动功能。
在旷场中，我们观察到两种 Q175 模型（zQ175dn 和 Q175FDN，在两种不同的背景品系上）存在习惯化缺陷，并且使用先进的机器学习方法 B-SOiD，我们发现雄性表现型 zQ175dn 小鼠的后肢站立行为异常。值得注意的是，我们发现体重对加速转棒和水 T 迷宫任务的表现有相当大的影响，并通过归一化体重进行了控制。表现型 zQ175dn 小鼠在加速转棒（体重归一化后）和雄性特异性的后肢运动学方面表现出缺陷。我们的水 T 迷宫实验显示表现型 zQ175dn 小鼠在反应学习中存在缺陷，在预表现型雄性 zQ175dn 小鼠中存在反转学习缺陷；使用 PyMouseTracks 软件进行的进一步分析使我们能够在该任务中描述新的行为特征，包括决策点时间和加速次数。在基于笼内杠杆拉动的评估中，我们发现雄性表现型 zQ175dn 小鼠存在显著的学习缺陷。一小部分小鼠还进行了电生理切片实验，结果显示雄性表现型 zQ175dn 小鼠自发兴奋性事件频率降低。
我们的研究揭示了 Q175 小鼠在性别、年龄和品系方面存在多种行为变化。我们的结果强调了体重和实验方案对行为结果的影响，以及机器学习工具在比以前更详细地检查行为方面的效用。具体来说，这项工作为该 HD 模型的行为缺陷提供了一个更新的概述，以及用于剖析旷场、加速转棒和 T 迷宫任务中行为的新方法。
