4.8 Article

Multiregional profiling of the brain transmembrane proteome uncovers novel regulators of depression

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SCIENCE ADVANCES
卷 7, 期 30, 页码 -

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AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.abf0634

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资金

  1. Shanghai Tech University
  2. National Program on Key Basic Research Project of China [2018YFA0507004]
  3. National Natural Science Foundation of China [31971362, 81970878, 31771130, 1861128023, 31922029, 61890951, 61890950]
  4. Innovative Research Team of High-level Local Universities in Shanghai

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This study presents a new proteomic approach aided by deep learning models to comprehensively profile transmembrane protein families in multiple mouse brain regions. It highlights discrepancies between messenger RNA and protein distribution, predicts an endogenous GPCR interaction network in the brain, and reveals the transmembrane proteome remodeling landscape in a mouse depression model, leading to the identification of previously unknown GPCR regulators of depressive-like behaviors.
Transmembrane proteins play vital roles in mediating synaptic transmission, plasticity, and homeostasis in the brain. However, these proteins, especially the G protein-coupled receptors (GPCRs), are underrepresented in most large-scale proteomic surveys. Here, we present a new proteomic approach aided by deep learning models for comprehensive profiling of transmembrane protein families in multiple mouse brain regions. Our multiregional proteome profiling highlights the considerable discrepancy between messenger RNA and protein distribution, especially for region-enriched GPCRs, and predicts an endogenous GPCR interaction network in the brain. Furthermore, our new approach reveals the transmembrane proteome remodeling landscape in the brain of a mouse depression model, which led to the identification of two previously unknown GPCR regulators of depressive-like behaviors. Our study provides an enabling technology and rich data resource to expand the understanding of transmembrane proteome organization and dynamics in the brain and accelerate the discovery of potential therapeutic targets for depression treatment.

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