BraInMap Elucidates the Macromolecular Connectivity Landscape of Mammalian Brain

Connectivity webs mediate the unique biology of the mammalian brain. Yet, while cell circuit maps are increasingly available, knowledge of their underlying molecular networks remains limited. Here, we applied multi-dimensional biochemical fractionation with mass spectrometry and machine learning to survey endogenous macromolecules across the adult mouse brain. We defined a global “interactome” comprising over one thousand multi-protein complexes. These include hundreds of brain-selective assemblies that have distinct physical and functional attributes, show regional and cell-type specificity, and have links to core neurological processes and disorders. Using reciprocal pull-downs and a transgenic model, we validated a putative 28-member RNA-binding protein complex associated with amyotrophic lateral sclerosis, suggesting a coordinated function in alternative splicing in disease progression. This brain interaction map (BraInMap) resource facilitates mechanistic exploration of the unique molecular machinery driving core cellular processes of the central nervous system. It is publicly available and can be explored here https://www.bu.edu/dbin/cnsb/mousebrain/. [Display omitted] •BraInMap is a global proteomic survey of over 1,000 multi-protein brain complexes•Near-native complex identification by CF-MS and reconstruction by computer learning•Technique interrogates complexes in normal and pathophysiological context•Allows study of functional modules that are adversely affected in neurological diseases In this ground-breaking work, Pourhaghighi et al. have carried out a survey of over one thousand multi-protein complex interactions in the mouse brain using a platform they have named BraInMap (for brain interaction map). This approach uses computer learning to reconstruct protein interactions from brain tissues that have been extensively purified. This important resource will allow neuroscientists to explore important neurobiological questions and identify pathways that are adversely affected in disease.