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A foundational neuronal protein network model unifying multimodal genetic, transcriptional, and proteomic perturbations in schizophrenia

Greta Pintacuda, Yu-Han H Hsu, Petra Páleníková, Ugne Dubonyte, Nadine Fornelos, Miao Chen, Daya Mena, Julia C Biagini, Travis Botts, Makayla Martorana, Danzel Rebelo, Joshua K T Ching, Ethan Crouse, Hilena Gebre, Xian Adiconis, Nathan Haywood, Sean Simmons, Michel Weïwer, Derek Hawes, Olli PietilainenThomas Werge, Ka Wan Li, August B Smit, Agnete Kirkeby, Joshua Z Levin, Ralda Nehme, Kasper Lage

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.

Original languageEnglish
DOIs
Publication statusE-pub ahead of print - 6 May 2025
SeriesmedRxiv : the preprint server for health sciences

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