No abstract

Proteomics is the large-scale study of the total protein content and their overall function within a cell through multiple facets of research. Advancements in proteomic methods have moved past the simple quantification of proteins to the identification of post-translational modifications (PTMs) and the ability to probe interactions between these proteins, spatially and temporally. Increased sensitivity and resolution of mass spectrometers and sample preparation protocols have drastically reduced the large amount of cells required and the experimental variability that had previously hindered its use in studying human neurological disorders. Proteomics offers a new perspective to study the altered molecular pathways and networks that are associated with autism spectrum disorders (ASD). The differences between the transcriptome and proteome, combined with the various types of post-translation modifications that regulate protein function and localization, highlight a novel level of research that has not been appropriately investigated. In this review, we will discuss strategies using proteomics to study ASD and other neurological disorders, with a focus on how these approaches can be combined with induced pluripotent stem cell (iPSC) studies. Proteomic analysis of iPSC-derived neurons have already been used to measure changes in the proteome caused by patient mutations, analyze changes in PTMs that resulted in altered biological pathways, and identify potential biomarkers. Further advancements in both proteomic techniques and human iPSC differentiation protocols will continue to push the field towards better understanding ASD disease pathophysiology. Proteomics using iPSC-derived neurons from individuals with ASD offers a window for observing the altered proteome, which is necessary in the future development of therapeutics against specific targets. Autism spectrum disorders

Autism spectrum disorder (ASD) is a genetically heterogeneous disorder. Sequencing studies have identified hundreds of risk genes for autism spectrum disorder (ASD), but the signaling networks of genes at the protein level remain largely unexplored, which can provide insight into previously unknown individual and convergent disease pathways in the brain. To address this gap, we used neuron-specific proximity-labeling proteomics (BioID) to identify protein-protein interaction (PPI) networks of 41 ASD-risk genes. Network analysis revealed the combined 41 risk gene PPI network map had more shared connectivity between unrelated ASD-risk genes than represented in existing public databases. We identified common pathways between established and uncharacterized risk genes, including synaptic transmission, mitochondrial/metabolic processes, Wnt signaling pathways, ion channel activity and MAPK signaling. Investigation of the mitochondrial and metabolic network using gene knockouts revealed a functional hub in neurons for multiple risk genes not previously associated with this pathway. Further, we identified that the uncharacterized ASD-risk gene PPP2R5D localizes to the synapse, which is disrupted by patient de novo missense mutations. Investigation of de novo missense variants of additional synaptic ASD-risk genes demonstrated that changes in PPI networks can capture synaptic transmission deficits. The neuronal 41 ASD-risk gene PPI network map also revealed enrichment for an additional 112 ASD-risk genes and human brain cell types implicated in ASD pathology. Interestingly, clustering of ASD-risk genes based on their PPI network connectivity identified multiple gene groups that correlate mutation-type to clinical behavior scores. Together, our data reveal that using PPI networks to map ASD risk genes can identify previously unknown individual and convergent neuronal signaling networks, provide a method to assess the impact of patient variants, and reveal new biological insight into disease mechanisms.