More information on the conference can be found here: Click this link
Large-scale protein-protein interaction networks in human neurons coalesce schizophrenia risk loci into unexpected pathways
Eugene Nacu (1,3), April Kim (1,2), Edyta Malolepsza (1,2), Taibo Li (1,2) William Crotty (1,3), Natalie Petrossian (1,3), Benjamin Tanenbaum (4), Stephan Ripke(1,2), Jake Jaffe (4), Monica Schenone (4), Mark Daly (1,2), Kevin Eggann(1,3), Kasper Lage (1,2)
1) Stanley Center for Psychiatric Research at the Broad Institute. 2) Massachusetts General Hospital. 3) Harvard Stem Cell Institute. 4) Broad Institute.
The recent genome-wide association studies in schizophrenia have revealed many risk loci encoding genes likely to be involved in this disorder and exciting glimpses of molecular pathways have emerged from the data (e.g., chromatin remodeling, calcium signaling, synaptic pruning and synaptic transmission). Such examples illustrate how some genes associated with schizophrenia interact at the level of proteins to form networks involved in diverse areas of neurobiology. However, most of the identified genes do not connect with each other in well-defined cellular pathways and it is clear that the disease also includes largely uncharted and incomplete networks that are probably unique to the human brain. This is a key bottleneck towards biological insight and therapeutic intervention. Here, we describe a large-scale approach to overcome some of these challenges by executing systematic interaction experiments in human neurons of proteins encoded in schizophrenia risk loci. First, our approach capitalizes on unbiased genetic data to choose corresponding proteins as the starting point of the protein interaction experiments. Second, we developed several parallel workflows, using both manual production and automated approaches on robots, to generate human upper layer cortical excitatory neurons from embryonic stem cells at scale (meaning routinely producing billions of cells). Third, we exploited state-of-the-art proteomics technologies to map quantitative interaction networks of the index proteins at high resolution. Fourth, we developed a new analytical platform (Genoppi) to quality control and integrate cell-type-specific protein interaction experiments and genome-wide association data to identify unexpected pathways relationships between risk loci. Our analysis shows that a large fraction of the high-quality and reproducible protein interactions we identify are unique to human neurons meaning that the interactions have not earlier been reported in the literature and are not identified in non-brain tissues. These observations illustrate the importance of executing the experiments in a human cell type of relevance to the trait being analyzed. Importantly, we uncover many unexpected pathway connections between schizophrenia risk loci. For example, our analysis reveals neuron-specific protein-protein interactions between calcium channels and the classic complement cascade at three different time points of neuronal differentiation. This observation would have been missed in other cell types and provides an unexpected link between calcium signaling and synaptic pruning in schizophrenia. More generally the experimental and analytical approaches we develop here for a neuropsychiatric disease could potentially be applied to functionally annotate loci and provide new pathway insights into other common complex traits.