SUMMARY Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ~14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ~30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a “broader” human interactome network than currently appreciated. The map also uncovers significant inter-connectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high quality interactome models will help “connect the dots” of the genomic revolution.

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships 1,2 . Here, we present a human "all-by-all" reference interactome map of human binary protein interactions, or "HuRI". With ~53,000 high-quality protein-protein interactions (PPIs), HuRI has approximately four times more such interactions than high-quality curated interactions from smallscale studies. Integrating HuRI with genome 3 , transcriptome 4 , and proteome 5 data enables the study of cellular function within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying specific subcellular roles of PPIs. Inferred tissuespecific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian Reprints and permissions information is available at http://www.nature.com/reprints.Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms

SUMMARY How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to “edgetic” alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

SUMMARY While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative ‘isoforms’ are functionally divergent (i.e., ‘functional alloforms’).

Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as 'nodes' and 'edges', respectively. Better understanding of genotype-to-phenotype relationships in human disease will require modeling of how disease-causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products ('node removal') and interaction-specific or edge-specific ('edgetic') alterations. Global computational analyses of B50 000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies.

Characterizing changes in protein-protein interactions associated with sequence variants (e.g. disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies where cost and time are major considerations. To this end, we have coupled AP to data-independent mass spectrometric acquisition (SWATH), and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. Here, we use AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes, and propose a scalable pipeline for systems biology studies.

FUS/TLS is a nucleic acid binding protein that, when mutated, can cause a subset of familial amyotrophic lateral sclerosis (fALS). Although FUS/TLS is normally located predominantly in the nucleus, the pathogenic mutant forms of FUS/TLS traffic to, and form inclusions in, the cytoplasm of affected spinal motor neurons or glia. Here we report a yeast model of human FUS/TLS expression that recapitulates multiple salient features of the pathology of the disease-causing mutant proteins, including nuclear to cytoplasmic translocation, inclusion formation, and cytotoxicity. Protein domain analysis indicates that the carboxyl-terminus of FUS/TLS, where most of the ALS-associated mutations are clustered, is required but not sufficient for the toxicity of the protein. A genome-wide genetic screen using a yeast over-expression library identified five yeast DNA/RNA binding proteins, encoded by the yeast genes ECM32, NAM8, SBP1, SKO1, and VHR1, that rescue the toxicity of human FUS/TLS without changing its expression level, cytoplasmic translocation, or inclusion formation. Furthermore, hUPF1, a human homologue of ECM32, also rescues the toxicity of FUS/TLS in this model, validating the yeast model and implicating a possible insufficiency in RNA processing or the RNA quality control machinery in the mechanism of FUS/TLS mediated toxicity. Examination of the effect of FUS/TLS expression on the decay of selected mRNAs in yeast indicates that the nonsense-mediated decay pathway is probably not the major determinant of either toxicity or suppression.

Classical ‘one-gene/one-disease’ models cannot fully reconcile with the increasingly appreciated prevalence of complicated genotype-to-phenotype associations in human disease. Genes and gene products function not in isolation but as components of intricate networks of macromolecules (DNA, RNA, or proteins) and metabolites linked through biochemical or physical interactions, represented in ‘interactome’ network models as ‘nodes’ and ‘edges’, respectively. Accordingly, mechanistic understanding of human disease will require understanding of how disease-causing mutations affect systems or interactome properties. The study of “edgetics” uncovers specific loss or gain of interactions (“edges”) to interpret genotype-to-phenotype relationships. We review how distinct genetic variants lead to distinct phenotypic outcomes through edgetic perturbations in interactome networks.