1/2 Cross modal integration of molecular and physiological networks in ASD

遗传学方法已经成功地识别了常见和罕见的原因遗传因素 增加自闭症谱系障碍(ASD)的风险，为机械性疾病提供了一个重要的起点 神经生物学研究。然而，要对ASD有一个完整的机制理解，请访问 分子、细胞和电路层面面临着巨大的挑战，例如极端的遗传异质性。 以及缺乏用于连接不同层次的神经系统分析的因果框架 在模型系统或患者中发挥作用。近十年前，我们认为基因和蛋白质网络 将提供一个组织框架，以了解不同类型的精神疾病遗传风险 统一的背景和信息疾病建模；确实，现在有大量证据支持 胎儿中期皮质发育过程中主要效应风险基因的趋同。此外，相关泛函 基因组研究，包括那些具有ASD(DUP)15q11-13主要基因形式的人，显示出共同的模式 死后ASD脑内转录和染色质失调，进一步支持生物学 融合。这种情况在哪里发生，如何发生，以及它反映了什么生物学机制(S)，目前尚不清楚。至 为了解决这个问题，我们提出了一个雄心勃勃的项目，解决了在确定因果关系方面的几个主要挑战 ASD的遗传风险与中枢神经系统结构和功能之间的联系。在这个多PI-U01中提出的工作 涉及一个由来自加州大学洛杉矶分校和斯坦福大学的四名首席调查人员和联合调查人员组成的团队，他们具有 有必要使用最先进的方法来执行这项工作，包括开发和 描述人类大脑发育、干细胞、生理学、基因组学、物理学和 行为。通过密切合作，我们将开发和分析基于体外人类干细胞的模型 从诱导的多能干细胞分化并组装成有组织的3D脑培养 被称为人类前脑球体(HFS)。这些HF包含正在开发的 前脑，包括前体细胞、放射状胶质细胞、皮质中间神经元、谷氨酸能神经元和非反应性神经元 星形胶质细胞，形成功能性突触。我们将模拟六个主要影响ASD风险基因座对HFS的影响 使用分子、基因组和生理分析来评估每个分析级别的融合。我们会 利用体外模拟的相同基因，对三种啮齿动物模型进行了生理学比较 目的是整合表型以开发预测模型并与体内啮齿动物模型进行比较。 我们将分析分子变化和基本细胞和突触特征与潜能的关系 控制派生的HFS中的紧急或动态网络特征，并将这些特征与HFS ASD风险突变，并基于网络模型预测测试因果关系的子集。完成 这些目标将使我们更清楚地理解模型系统的力量和局限性 计算模型，同时揭示了ASD不同遗传形式的潜在收敛区域。

Genetic approaches have been successful in identifying causal genetic factors, both common and rare, that contribute to risk for autism spectrum disorder (ASD), providing a crucial starting point for mechanistic neurobiological investigations. However, moving towards an integrated mechanistic understanding of ASD at a molecular, cellular, and circuit level faces substantial challenges, such as extreme genetic heterogeneity and the lack of causal frameworks with which to connect different levels of analysis of nervous system function in model systems or patients. Nearly a decade ago, we reasoned that gene and protein networks would provide an organizing framework for understanding heterogeneous psychiatric disease genetic risk in a unified context and inform disease modeling; indeed there is now substantial evidence supporting convergence of major effect risk genes during mid-fetal cortical development. Furthermore, related functional genomic studies, including in those with a major gene form of ASD (dup)15q11-13, show shared patterns of transcriptional and chromatin dysregulation in post-mortem ASD brain, further supporting biological convergence. Where and how this occurs, and what biological mechanism(s) it reflects is not known. To address this, we propose an ambitious project that addresses several major challenges in establishing causal linkages between genetic risk and CNS structure and function in ASD. The work proposed in this multi-PI U01 involves a team of four principal investigators and co-investigators from UCLA and Stanford with the expertise necessary to perform this work using state of the art methodologies, ranging from developing and characterizing in vitro models of human brain development, stem cells, physiology, genomics, physics, and behavior. Through close collaboration, we will develop and analyze in vitro human stem cell based models that are differentiated from induced pluripotent stem cells and assembled into organized 3D brain cultures called human forebrain spheroids (hFS). These hFS contain the major cell classes of the developing forebrain, including progenitors, radial glia, cortical interneurons, glutamatergic neurons, and non-reactive astrocytes, and form functional synapses. We will model the effects of six major effect ASD risk loci in hFS with molecular, genomic, and physiological analyses to assess convergence at each level of analysis. We will also conduct comparisons of physiology using three rodent models based on the same genes modeled in vitro with the aim of integrating phenotypes to develop predictive models and compare with in vivo rodent models. We will analyze the relationship of molecular alterations and basic cellular and synaptic features with potential emergent or dynamic network features in control-derived hFS and compare these features with hFS harboring ASD risk mutations and test a subset of causal relationships based on network model predictions. Completion of these aims will lead to a more clear understanding of the power and limitations of model systems and computational models, while uncovering potential areas of convergence in different genetic forms of ASD.