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

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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.