A reference map of the human protein interactome

Abstract

Global insights into cellular organization and function require comprehensive understanding of interactome networks. Similar to how a reference genome sequence revolutionized human genetics, a reference map of the human interactome network is critical to fully understand genotype-phenotype relationships. Here we present the first human "all-by-all" binary reference interactome map, or "HuRI". With ~53,000 highquality protein-protein interactions (PPIs), HuRI is approximately four times larger than the information curated from small-scale studies available in the literature. Integrating HuRI with genome, transcriptome and proteome data enables the study of cellular function within essentially any physiological or pathological cellular context. We demonstrate the use of HuRI in identifying specific subcellular roles of PPIs and protein function modulation via splicing during brain development. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI thus represents an unprecedented, systematic reference linking genomic variation to phenotypic outcomes.

Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome

“…These direct observations of interactome re-organization complement reports of co-variation within protein complexes as inferred from constituent protein expression across cell lines, tissues, and individuals (Ori et al, 2016;Romanov et al, 2019;Ryan et al, 2017). Such extensive context specificity also aligns with disease-specific interactions of the cystic fibrosis transmembrane receptor (Pankow et al, 2015) and with interactome reorganization across tissues (Luck et al, 2019;Skinnider et al, 2018) and upon interferon stimulation (Kerr et al, 2019).…”

“…Since our 293T and HCT116 networks afford complementary views of the interactome, analyzing them in tandem may improve their predictive power. Previously, we and others have shown that individual interaction networks can predict a protein's function, subcellular localization, and disease associations (Drew et al, 2017;Huttlin et al, 2015Huttlin et al, , 2017Luck et al, 2019;Menche et al, 2015). Combining predictions from two independent networks should enhance detection of true positives and minimize false positives.…”

Enriching human interactome with functional mutations to detect high-impact network modules underlying complex diseases

“…It integrates several databases and filters out low-quality and erroneous interactions. The other source is the Human Reference Protein Interactome Mapping Project (HuRI) [19]. HINT is a manually curated repository of PPIs mainly from literature, whereas HuRI is a primary source of experimentally validated PPIs.…”

“…Understanding the biological network is essential in studying a genetic disease, because such a disease is likely a result of the disruption and rewiring of the complex intracellular network, rather than a consequence of the dysfunction of a single gene [17]. Along with the increasing availability of high-throughput human protein interactomics data [18,19], computational approaches have been developed. In particular, network propagation has recently emerged as a prominent approach in network biology [20].…”

A unified framework for geneset network analysis

“…However, these experiments provide information about genes and proteins in isolation, whereas most biological functions and phenotypes are the result of interactions between them. Protein and gene interaction information are becoming rapidly available thanks to high-throughput screens [2], such as the yeast two hybrid system, and downstream annotation and sharing in public databases [3,4]. Thus, it is becoming obvious to use interaction data to map single gene information to biological pathways.…”