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)和智力残疾(ID)是一种复杂的、不同种类的疾病，其潜在的细胞和电路病理生理学知之甚少。然而，已知的单基因突变在ASD患者中占很大比例，并同时存在中到重度ID。临床上，焦虑障碍和恐惧症在ASD患者中非常普遍，并经常被用于诊断。1.编码跨膜蛋白Neurigin-3的基因突变与ASD和ID高度相关。最近，我们报道了缺乏Nlgn3基因(Nlgn3-/y)的大鼠表现出健壮、恐惧/焦虑相关的表型2。这种行为异常与大脑的一个区域有关，该区域关键地参与了适当生存行为的协调，即中脑导水管周围灰质(PAG)。我们已经证明，与野生型对照相比，Nlgn3-/y大鼠PAG背侧部分的神经元本质上高度兴奋，这种细胞异常可能是观察到的飞行冻结行为失衡的原因。据我们所知，这是第一次将PAG异常与ASD联系在一起，因此突出了改善对神经发育障碍所涉及的大脑回路的了解的迫切需要，以允许开发改进的、精确的治疗工具。因此，本项目的目标1是进一步了解Nlgn3-/y大鼠异常恐惧反应的神经回路机制。通过同时记录来自PAG的大脑深层LFP和一系列解剖连接的结构，以及脑电(EEG)，学生将研究PAG的内在超兴奋性如何在大规模恐惧网络互动中表现出来，以及这种超兴奋性与行为反应的相关性。大脑深层LFP和表面EEG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记。ASD患者的药物治疗研究在成功3中得到了不同程度的满足。这种变化是治疗结果在临床上的一个主要问题。因此，本项目的目标2是检测阿巴氯芬(一种有效的GABA B激动剂)对Nlgn3-/y大鼠观察到的、健壮的表型的影响。为此，该药物将在发育过程中的不同时间点应用，无论是系统应用还是局部大脑区域(例如PAG)，同时衡量其对神经网络活动和行为反应的影响。在ASD3中，Arbacolfen治疗的不同疗效突显了对识别具有更高治疗响应性可能性的个人的靶向方法的未得到满足的需求。卡罗林斯卡研究所的克里斯托弗·曼松博士最近的研究表明，大脑信号的瞬间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自闭症共同发病。因此，目标3是测试神经网络生物标记物如何有效地预测药理疗效。为此，学生将首先调查LFP/EEG信号变异性是否可以用来预测Nlgn3/y大鼠模型的阿巴氯芬治疗结果。重要的是，学生将有机会将这些分析的应用扩展到从ASD的其他单基因大鼠模型中获得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，并深入了解针对ASD和ID患者亚群的靶向药物治疗。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.