An atlas of protein-protein associations of human tissues prioritizes candidate disease genes

Abstract

Proteins that interact together participate in the same cellular process and influence the same organismal traits. Despite the progress in mapping protein-protein interactions we lack knowledge of how they differ between tissues. Due to coordinated (post)transcriptional control, protein complex members have highly correlated abundances that are predictive of functional association. Here, we have compiled 7873 proteomic samples measuring protein levels in 11 human tissues and use these to define an atlas with tissue-specific protein associations. This method recapitulates known protein complexes and the larger structural organization of the cell. Interactions of stable protein complexes are well preserved across tissues, while signaling and metabolic interactions show larger variation. Further, we find that less than 18% of differences between tissues are estimated to be due to differences in gene expression while cell-type specific cellular structures, such as synaptic components, represent a significant driver of differences between tissues. We further supported the brain protein association network through co-fractionation experiments in synaptosomes, curation of brain derived pull-down data and AlphaFold2 models. Together these results illustrate how this brain specific protein interaction network can functionally prioritize candidate genes within loci linked to brain disorders.

Single-cell proteomics of Arabidopsis leaf mesophyll identifies drought stress-related proteins

“…One of the main advantages of single-cell proteomics is that it can provide context-dependent insights into protein-protein relationships on a global scale without the need for intermediate affinity reagents or for exogenous expression of tagged baits 49–52 . Protein covariation across large-scale proteomic studies are implicit indicators of protein-protein relationships 53–55 . However, the averaging of protein measurements in bulk proteomic analyses weakens these relationships.…”