Neuronal network screening and predictive profiling of pharmacological efficacy in a rat model of autism spectrum disorder

自闭症谱系障碍（asd）和智力障碍（id）是复杂的、异质性的疾病，其潜在的细胞和电路病理生理机制尚不清楚。然而，已知单基因突变可以解释很大比例的个体出现ASD并同时发生中度至重度ID的病例。在临床上，焦虑症和恐惧症在自闭症谱系障碍患者中非常普遍，并经常被用于诊断。跨膜蛋白神经胶质素-3编码基因突变与ASD和ID高度相关。最近，我们报道了缺乏Nlgn3基因（Nlgn3-/y）的大鼠表现出强壮、恐惧/焦虑相关的表型2。这种行为异常与大脑中与适当生存行为协调密切相关的区域——导水管周围灰质（PAG）有关。我们已经证明，与野生型对照相比，Nlgn3-/y大鼠PAG背侧的神经元本质上是高度兴奋的，这种细胞异常可能是观察到的飞行-冻结行为不平衡的基础2。据我们所知，这是第一次将PAG异常与ASD联系起来，因此强调了提高对神经发育障碍中涉及的脑回路的理解的迫切需要，以便开发改进的、精确的治疗工具。因此，本项目的目的1是进一步了解Nlgn3-/y大鼠异常恐惧反应的神经回路机制。通过同时记录PAG和一系列解剖相关结构的深层脑LFP，以及脑电图（EEG），学生将研究PAG内在的超兴奋性如何在大规模恐惧网络相互作用中表现出来，以及这与行为反应之间的关系。深层脑LFP和表面脑电图信号之间的功能联系/相关性也将被量化，以提供可翻译的生物标志物。ASD患者的药物治疗研究在成功与否上存在变数。这种治疗结果的差异是临床上的一个主要问题。因此，本项目的目的2是检查Arbaclofen（一种强效GABA B激动剂）治疗对Nlgn3-/y大鼠观察到的稳健表型的影响。为了做到这一点，药物将在开发过程中的不同时间点应用，系统或局部大脑区域（例如PAG），同时测量其对神经网络活动和行为反应的影响。Arbacolfen治疗ASD3的不同疗效突出了对靶向方法的需求，以识别具有更高治疗反应可能性的个体。卡罗林斯卡学院的Kristoffer mamatnson博士最近的研究表明，即时的大脑信号变异性可以可靠地预测社交焦虑患者的精神治疗结果，社交焦虑是一种常见的ASD合发症。因此，目的3是测试神经网络生物标志物如何有效地预测药物疗效。为此，该学生将首先研究LFP/EEG信号变异性是否可用于预测Nlgn3-/y大鼠模型中的Arbaclofen治疗结果。重要的是，学生将有机会将这些分析扩展到其他单基因ASD大鼠模型中可用的一系列行为和药物治疗数据。该项目的结果将加深我们对Nlgn3-/y大鼠模型中观察到的行为缺陷背后的大脑网络的理解，并为ASD和ID患者亚群的靶向药物治疗提供见解。这些发现将推动分析方法的发展，以优化生物数据到预测模型的转换。

Autism spectrum disorders (ASDs) and intellectual disabilities (IDs) are complex, heterogeneous disorders with poorly understood underlying cellular and circuit pathophysiology. Monogenic mutations are, however, known to account for a large proportion of cases where individuals present with ASD and co-occurring moderate to severe ID. Clinically, anxiety disorders and phobias are highly prevalent in individuals with ASD and are often used to inform diagnosis1.Mutations in the gene encoding for transmembrane protein neuroligin-3 are highly correlative with ASD and ID. Recently, we reported that rats lacking the Nlgn3 gene (Nlgn3-/y) exhibit robust, fear/anxiety related phenotypes2. This behavioral abnormality has been linked to a region of the brain critically involved in the orchestration of appropriate survival behaviors, the periaqueductal gray (PAG). We have shown that neurons in the dorsal part of the PAG are intrinsically hyper-excitable in Nlgn3-/y rats compared to wild type controls and that this cellular abnormality may underlie the imbalance in flight-freeze behaviors observed2. To our knowledge, this is the first time that PAG abnormality has been linked to ASD and as such highlights the critical need for improved understanding of brain circuitry involved in neurodevelopmental disorders to allow development of improved, precision therapeutic tools.Therefore, Aim 1 of this project is to further understand the neural circuit mechanisms driving aberrant fear responses in Nlgn3-/y rats. By simultaneously recording deep-brain LFP from PAG and a range of anatomically linked structures, alongside electroencephalography (EEG), the student will examine how intrinsic PAG hyper-excitability manifests in large-scale fear network interaction and how this correlates to behavioral responses. Functional links/correlations between deep brain LFP and surface EEG signals will also be quantified to deliver translatable biomarkers.Pharmacological treatment studies in people with ASD have met been variable in success3. This variation is treatment outcome is a major issue clinically. Therefore, Aim 2 of this project is to examine the effect of Arbaclofen (a potent GABA B agonist) treatment on observed, robust phenotypes in Nlgn3-/y rats. To do this, the drug will be applied at various time points during development, either systemically or to local brain regions (e.g. PAG) while measuring its impact upon neural network activity and behavioral responses.The variable efficacy of Arbacolfen treatment in ASD3 highlights the unmet need for targeted approaches to identifying individuals with higher likelihood of treatment responsivity. Recent work from Dr Kristoffer Månsson (Karolinska Institutet) has revealed that moment-to-moment brain signal variability can reliably predict psychiatric treatment outcomes in patients with social anxiety4, a common ASD co-morbidity. Therefore, Aim 3 is to test how effectively neuronal network biomarkers can predict pharmacological efficacy. To do this, the student will first investigate whether LFP/EEG signal variability can be used to predict Arbaclofen treatment outcomes in the Nlgn3-/y rat model. Importantly, the student will have opportunity to expand the application of these analyses to a range of behavioral and pharmacological treatment data available from other monogenic rat models of ASD.The results of this project will deepen our understanding of brain networks underlying the behavioural deficits observed in Nlgn3-/y rat model and give insight into targeted drug treatment for subpopulations of people with ASD and ID. The findings will drive development of analytical methods to optimize the transformation of biological data into predictive models.