Subtyping the autisms using individualized protein network analysis

项目总结 自闭症是一种行为定义的诊断，在美国大约每59个儿童中就有一个受到影响。最新基因 研究表明，自闭症是一个大家庭的总称，指的是一个单独罕见的大家庭 常见的遗传性疾病，每个单独的基因只占总病例的一小部分。这 给试图开发基于生物的药物或治疗策略的研究人员带来了一个问题： 自闭症诊断实体范围太广，没有生物学意义，但大多数个体基因突变也是如此 很少允许足够的患者招募或商业上可行的药物开发。有一件急事 未得到满足的需求开发了一种亚型策略，可以将患者分配到少数生物学上的 可能符合靶向治疗策略的有意义的亚型。我们最近开发了一种 对蛋白质相互作用网络进行高维测量的新蛋白质组学策略 (大头针)。这些测量反映了自闭症发病机制的几个相关特征--突触内容，最近 神经元的活性和发育阶段。我们假设不同的遗传性自闭症集中在两个 特定的PIN并产生网络中断模式，这些模式虽然各自独特，但共享共同的 允许将PIN矩阵聚集成子类型的功能。重要的是，我们的集群方法允许 识别特定的信号转导节点，这些节点定义每个子类型，连接生物相关 我们建议的集群的信息。在已发表的概念验证工作中，我们能够将七个 不同的小鼠模型，并对以前未知的分子病理做出预测。在这里，我们 建议将这项工作扩展到人类神经元，使用从基因测序中提取的原始患者细胞 自闭症研究对象，发现可能的致病基因突变，或自闭症患者 进行了测序，但没有发现突变，或者年龄和性别匹配的典型发育中对照。这 这项工作将揭示不同已知和未知自闭症之间新的、生物学上相关的关系 遗传病因，并提供了同时识别患者亚群和潜在患者的机会 可以有效治疗每个已确定的亚群的药物靶点。

PROJECT SUMMARY Autism is a behaviorally-defined diagnosis that affects approximately 1 in 59 children in the US. Recent genetic studies have revealed that autism is an umbrella term for a large family of individually rare, collectively common genetic disorders, with each individual gene accounting for only a small portion of total cases. This presents a problem for researchers attempting to develop biologically-based drug or treatment strategies: the autism diagnostic entity is too broad to be biologically meaningful, but most individual genetic mutations are too rare to allow for sufficient patient recruitment or for commercially viable drug development. There is an urgent unmet need to develop a subtyping strategy that can assign patients into one of a small number of biologically meaningful subtypes that might be amenable to targeted treatment strategies. We have recently developed a novel proteomic strategy that makes high-dimensional measurements of protein-protein interaction networks (PINs). These measures reflect several relevant features of autism pathogenesis- synaptic content, recent activity, and developmental stage of the neuron. We postulate that different genetic autisms converge on two specific PINs and produce patterns of network disruption that, while individually unique, share common features that will allow clustering of PIN matrices into subtypes. Importantly, our clustering methods allow identification of specific signal transduction nodes that define each sub-type, linking biologically-relevant information with our proposed clusters. In published proof-of-concept work, we were able to cluster seven different mouse models and make predictions about previously unknown molecular pathologies. Here, we propose to extend this work to human neurons, using primary patient cells taken from genetically sequenced autistic research subjects with identified likely causative genetic mutations, or `idiopathic' autism patients who were sequenced but no mutation was identified, or age-and-sex-matched typically developing controls. This work will reveal new, biologically relevant relationships between autisms of different known and unknown genetic etiologies, and offers the opportunity to simultaneously identify sub-groups of patients and potential drug targets that may effectively treat each identified sub-group.