Pivotal

Pivotal Publications

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Luck et al. A reference map of the human binary protein interactome. Nature 2020; 580(7803):402-408

Abstract:
Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships1,2. Here we present a human 'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein-protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein-protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes.

Yang et al. Widespread Expansion of Protein Interaction
Capabilities by Alternative Splicing. Cell 2016; 164(4):805-17

Abstract:
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").

Sahni et al. Widespread Macromolecular Interaction Perturbations in Human Genetic Disorders. Cell 2015; 161(3): 647–660

Abstract:
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.

Rolland et al. A proteome-scale map of the human interactome network. Cell 2014; 159(5): 1212–1226

Abstract:
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.

Rozenblatt-Rosen et al. Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins. Nature 2012; 487:491-95.

Systematic interactome network mapping between viral proteins and host proteins, using a multidisciplinary approach at a scale never before attempted, identified novel candidate genes involved in several human diseases.

Abstract:
Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations associated with cancer predisposition and large numbers of somatic genomic alterations. However, it remains challenging to distinguish between background, or “passenger” and causal, or “driver” cancer mutations in these datasets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. To test the hypothesis that genomic variations and tumour viruses may cause cancer via related mechanisms, we systematically examined host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways that go awry in cancer, such as Notch signalling and apoptosis. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches result in increased specificity for cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate prioritization of cancer-causing driver genes so as to advance understanding of the genetic basis of human cancer.

Zhong et al. Edgetic perturbation models of human inherited disorders. Mol Syst Biol 2009; 5:321.

Introduced the edgetics model whereby network perturbations caused by disease mutations provide insight into genotype-to-phenotype relationships in human disease.

Abstract:
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 B50000 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.

Yu et al. High-quality binary protein interaction map of the yeast interactome network. Science 2008; 322: 104-110.

First implementation of an empirically controlled mapping framework to produce a high-confidence second-generation interactome map covering approximately 20% of all yeast binary interactions.

Abstract:
Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a “second-generation” high-quality, high-throughput Y2H data set covering ~20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.

Pujana et al. Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet 2007; 39:1338-49.

An original application of interactome network modeling to identify a novel candidate gene involved in breast cancer.

Abstract:
Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or ‘omic’) data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer– associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer- associated genes.

Goh et al. The human disease network. Proc Natl Acad Sci USA 2007; 104:8685-90.

The first global “atlas” of the intricacies of human Mendelian disorders from a network perspective. It showed that interactome networks can help elucidate genotype-phenotype relationships.

Abstract:
A network of disorders and disease genes linked by known disorder– gene associations offers a platform to explore in a single graph- theoretic framework all known phenotype and disease gene associ- ations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules. We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indi- cates that they are localized in the functional periphery of the network. A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral, a prediction we confirm for cancer genes.

Rual et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 2005; 437:1173-8.

A first binary interactome network mapped for human at the scale of the whole proteome

Abstract:
Systematic mapping of protein-protein interactions, or 'interactome' mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties, suggesting that a human interactomemap will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of approximately 8,100 currently available Gateway-cloned open reading frames and detected approximately 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of approximately 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by approximately 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.

Han et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 2004; 430: 88-93.

Using strategies to integrate interactome networks with co-expression networks, this paper devised the principle of dynamically organized modularity in yeast networks, a fundamental network parameter since corroborated by many groups for yeast and other species.

Abstract:
In apparently scale-free protein-protein interaction networks, or "interactome" networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the "hubs", interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: "party" hubs, which interact with most of their partners simultaneously, and "date" hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes--or modules--to each other, whereas party hubs function inside modules.

Li et al. A map of the interactome network of the metazoan C. elegans. Science 2004; 303:540-3.

An interactome network mapped at the scale of the whole proteome for a multicellular model organism exhibits intricate yet decipherable global properties.

Abstract:
To initiate studies on how protein-protein interaction (or “interactome”) networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains ∼5500 interactions. Topological and biological features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biological hypotheses.

Boulton et al. Combined functional genomic maps of the C. elegans DNA damage response. Science 2002; 295:127-31.

Developed original strategies to integrate interactome networks with functional networks such as co-expression or genetic interaction networks.

Abstract:
Many human cancers originate from defects in the DNA damage response (DDR). Although much is known about this process, it is likely that additional DDR genes remain to be discovered. To identify such genes, we used a strategy that combines protein-protein interaction mapping and large-scale phenotypic analysis in Caenorhabditis elegans. Together, these approaches identified 12 worm DDR orthologs and 11 novel DDR genes. One of these is the putative ortholog of hBCL3, a gene frequently altered in chronic lymphocytic leukemia. Thus, the combination of functional genomic mapping approaches in model organisms may facilitate the identification and characterization of genes involved in cancer and, perhaps, other human diseases.

Walhout et al. Protein interaction mapping in C. elegans using proteins involved in vulval development. Science 2000; 287:116-22.

This first interactome network mapped systematically for a multicellular model organism.

Abstract:
Protein interaction mapping using large-scale two-hybrid analysis has been proposed as a way to functionally annotate large numbers of uncharacterized proteins predicted by complete genome sequences. This approach was examined in Caenorhabditis elegans, starting with 27 proteins involved in vulval development. The resulting map reveals both known and new potential interactions and provides a functional annotation for approximately 100 uncharacterized gene products. A protein interaction mapping project is now feasible for C. elegans on a genome-wide scale and should contribute to the understanding of molecular mechanisms in this organism and in human diseases.