A foundational neuronal protein network model unifying multimodal genetic, transcriptional, and proteomic perturbations in schizophrenia.
Pintacuda, G.; Hsu, Y.-H. H.; Palenikova, P.; Dubonyte, U.; Fornelos, N.; Chen, M.; Mena, D.; Biagini, J. C.; Botts, T.; Martorana, M.; Rebelo, D.; Ching, J. K. T.; Crouse, E.; Gebre, H.; Adiconis, X.; Haywood, N.; Simmons, S.; Weiwer, M.; Hawes, D.; Pietilainen, O.; Werge, T.; Li, K. W.; Smit, A. B. B.; Kirkeby, A.; Levin, J.; Nehme, R.; Lage, K.
Show abstract
Schizophrenia (SCZ) is a complex psychiatric disorder with a diverse genetic landscape, involving common regulatory variants, rare protein-coding mutations, structural genomic rearrangements, and transcriptional dysregulation. A critical challenge in developing rationally designed therapeutics is understanding how these various factors converge to disrupt cellular networks in the human brain, ultimately contributing to SCZ. Towards this aim, we generated multimodal data, including SCZ-specific protein-protein interactions in stem-cell-derived neuronal models and adult postmortem cortex, integrated with genetic and transcriptomic datasets from individuals with psychiatric disorders. We identified three distinct neuron-specific SCZ protein networks, or modules, significantly enriched for genetic and transcriptional perturbations associated with SCZ. The relevance of these modules was validated through whole-cell proteomics in patient-derived neurons, revealing their disruption in 22q11.2 deletion carriers diagnosed with SCZ. We demonstrated their therapeutic potential by showing that these modules are targets of GSK3 inhibition using phosphoproteomics. Our findings present a foundational model that integrates genetic, transcriptional, and proteomic perturbations in SCZ. This model provides a cohesive framework for understanding how polygenic and multimodal perturbations affect neuronal pathways in the human brain, as well as a data-driven pathway resource for identifying potential drug targets to reverse disruptions observed in these neuronal networks.
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