Widespread macromolecular interaction perturbations in human genetic disorders.

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.

Interactome networks and human disease.

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Proto-genes and de novo gene birth.

Novel protein-coding genes can arise either through re-organization of pre-existing genes or de novo. Processes involving re-organization of pre-existing genes, notably after gene duplication, have been extensively described. In contrast, de novo gene birth remains poorly understood, mainly because translation of sequences devoid of genes, or 'non-genic' sequences, is expected to produce insignificant polypeptides rather than proteins with specific biological functions. Here we formalize an evolutionary model according to which functional genes evolve de novo through transitory proto-genes generated by widespread translational activity in non-genic sequences. Testing this model at the genome scale in Saccharomyces cerevisiae, we detect translation of hundreds of short species-specific open reading frames (ORFs) located in non-genic sequences. These translation events seem to provide adaptive potential, as suggested by their differential regulation upon stress and by signatures of retention by natural selection. In line with our model, we establish that S. cerevisiae ORFs can be placed within an evolutionary continuum ranging from non-genic sequences to genes. We identify ~1,900 candidate proto-genes among S. cerevisiae ORFs and find that de novo gene birth from such a reservoir may be more prevalent than sporadic gene duplication. Our work illustrates that evolution exploits seemingly dispensable sequences to generate adaptive functional innovation.

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.

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, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining 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, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.

An inter-species protein-protein interaction network across vast evolutionary distance.

In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical-versus-functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an "inter-interactome" approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter-interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra-species networks. Although substantially reduced relative to intra-species networks, the levels of functional overlap in the yeast-human inter-interactome network uncover significant remnants of co-functionality widely preserved in the two proteomes beyond human-yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co-functionality. Such non-functional interactions, however, represent a reservoir from which nascent functional interactions may arise.

Next-generation sequencing to generate interactome datasets.

Next-generation sequencing has not been applied to protein-protein interactome network mapping so far because the association between the members of each interacting pair would not be maintained in en masse sequencing. We describe a massively parallel interactome-mapping pipeline, Stitch-seq, that combines PCR stitching with next-generation sequencing and used it to generate a new human interactome dataset. Stitch-seq is applicable to various interaction assays and should help expand interactome network mapping.

Identification of FAM111A as an SV40 host range restriction and adenovirus helper factor.

The small genome of polyomaviruses encodes a limited number of proteins that are highly dependent on interactions with host cell proteins for efficient viral replication. The SV40 large T antigen (LT) contains several discrete functional domains including the LXCXE or RB-binding motif, the DNA binding and helicase domains that contribute to the viral life cycle. In addition, the LT C-terminal region contains the host range and adenovirus helper functions required for lytic infection in certain restrictive cell types. To understand how LT affects the host cell to facilitate viral replication, we expressed full-length or functional domains of LT in cells, identified interacting host proteins and carried out expression profiling. LT perturbed the expression of p53 target genes and subsets of cell-cycle dependent genes regulated by the DREAM and the B-Myb-MuvB complexes. Affinity purification of LT followed by mass spectrometry revealed a specific interaction between the LT C-terminal region and FAM111A, a previously uncharacterized protein. Depletion of FAM111A recapitulated the effects of heterologous expression of the LT C-terminal region, including increased viral gene expression and lytic infection of SV40 host range mutants and adenovirus replication in restrictive cells. FAM111A functions as a host range restriction factor that is specifically targeted by SV40 LT.

Viral perturbations of host networks reflect disease etiology.

Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.

Host-pathogen interactome mapping for HTLV-1 and -2 retroviruses.

BACKGROUND: Human T-cell leukemia virus type 1 (HTLV-1) and type 2 both target T lymphocytes, yet induce radically different phenotypic outcomes. HTLV-1 is a causative agent of Adult T-cell leukemia (ATL), whereas HTLV-2, highly similar to HTLV-1, causes no known overt disease. HTLV gene products are engaged in a dynamic struggle of activating and antagonistic interactions with host cells. Investigations focused on one or a few genes have identified several human factors interacting with HTLV viral proteins. Most of the available interaction data concern the highly investigated HTLV-1 Tax protein. Identifying shared and distinct host-pathogen protein interaction profiles for these two viruses would enlighten how they exploit distinctive or common strategies to subvert cellular pathways toward disease progression. RESULTS: We employ a scalable methodology for the systematic mapping and comparison of pathogen-host protein interactions that includes stringent yeast two-hybrid screening and systematic retest, as well as two independent validations through an additional protein interaction detection method and a functional transactivation assay. The final data set contained 166 interactions between 10 viral proteins and 122 human proteins. Among the 166 interactions identified, 87 and 79 involved HTLV-1 and HTLV-2 -encoded proteins, respectively. Targets for HTLV-1 and HTLV-2 proteins implicate a diverse set of cellular processes including the ubiquitin-proteasome system, the apoptosis, different cancer pathways and the Notch signaling pathway. CONCLUSIONS: This study constitutes a first pass, with homogeneous data, at comparative analysis of host targets for HTLV-1 and -2 retroviruses, complements currently existing data for formulation of systems biology models of retroviral induced diseases and presents new insights on biological pathways involved in retroviral infection.

Large-scale RACE approach for proactive experimental definition of C. elegans ORFeome.

Although a highly accurate sequence of the Caenorhabditis elegans genome has been available for 10 years, the exact transcript structures of many of its protein-coding genes remain unsettled. Approximately two-thirds of the ORFeome has been verified reactively by amplifying and cloning computationally predicted transcript models; still a full third of the ORFeome remains experimentally unverified. To fully identify the protein-coding potential of the worm genome including transcripts that may not satisfy existing heuristics for gene prediction, we developed a computational and experimental platform adapting rapid amplification of cDNA ends (RACE) for large-scale structural transcript annotation. We interrogated 2000 unverified protein-coding genes using this platform. We obtained RACE data for approximately two-thirds of the examined transcripts and reconstructed ORF and transcript models for close to 1000 of these. We defined untranslated regions, identified new exons, and redefined previously annotated exons. Our results show that as much as 20% of the C. elegans genome may be incorrectly annotated. Many annotation errors could be corrected proactively with our large-scale RACE platform.

'Edgetic' perturbation of a C. elegans BCL2 ortholog.

Genes and gene products do not function in isolation but within highly interconnected 'interactome' networks, modeled as graphs of nodes and edges representing macromolecules and interactions between them, respectively. We propose to investigate genotype-phenotype associations by methodical use of alleles that lack single interactions, while retaining all others, in contrast to genetic approaches designed to eliminate gene products completely. We describe an integrated strategy based on the reverse yeast two-hybrid system to isolate and characterize such edge-specific, or 'edgetic', alleles. We established a proof of concept with CED-9, a Caenorhabditis elegans BCL2 ortholog. Using ced-9 edgetic alleles, we uncovered a new potential functional link between apoptosis and a centrosomal protein. This approach is amenable to higher throughput and is particularly applicable to interactome network analysis in organisms for which transgenesis is straightforward.

Literature-curated protein interaction datasets.

High-quality datasets are needed to understand how global and local properties of protein-protein interaction, or 'interactome', networks relate to biological mechanisms, and to guide research on individual proteins. In an evaluation of existing curation of protein interaction experiments reported in the literature, we found that curation can be error-prone and possibly of lower quality than commonly assumed.

An experimentally derived confidence score for binary protein-protein interactions.

Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.

An empirical framework for binary interactome mapping.

Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.

Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network.

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins

Interactome mapping of the phosphatidylinositol 3-kinase-mammalian target of rapamycin pathway identifies deformed epidermal autoregulatory factor-1 as a new glycogen synthase kinase-3 interactor.

The phosphatidylinositol 3-kinase-mammalian target of rapamycin (PI3K-mTOR) pathway plays pivotal roles in cell survival, growth, and proliferation downstream of growth factors. Its perturbations are associated with cancer progression, type 2 diabetes, and neurological disorders. To better understand the mechanisms of action and regulation of this pathway, we initiated a large scale yeast two-hybrid screen for 33 components of the PI3K-mTOR pathway. Identification of 67 new interactions was followed by validation by co-affinity purification and exhaustive literature curation of existing information. We provide a nearly complete, functionally annotated interactome of 802 interactions for the PI3K-mTOR pathway. Our screen revealed a predominant place for glycogen synthase kinase-3 (GSK3) A and B and the AMP-activated protein kinase. In particular, we identified the deformed epidermal autoregulatory factor-1 (DEAF1) transcription factor as an interactor and in vitro substrate of GSK3A and GSK3B. Moreover, GSK3 inhibitors increased DEAF1 transcriptional activity on the 5-HT1A serotonin receptor promoter. We propose that DEAF1 may represent a therapeutic target of lithium and other GSK3 inhibitors used in bipolar disease and depression.

Isoform discovery by targeted cloning, 'deep-well' pooling and parallel sequencing.

Describing the 'ORFeome' of an organism, including all major isoforms, is essential for a system-level understanding of any species; however, conventional cloning and sequencing approaches are prohibitively costly and labor-intensive. We describe a potentially genome-wide methodology for efficiently capturing new coding isoforms using reverse transcriptase (RT)-PCR recombinational cloning, 'deep-well' pooling and a next-generation sequencing platform. This ORFeome discovery pipeline will be applicable to any eukaryotic species with a sequenced genome.

Towards a proteome-scale map of the human protein-protein interaction network.

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 interactome map 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.

VirusMINT: a viral protein interaction database.

Understanding the consequences on host physiology induced by viral infection requires complete understanding of the perturbations caused by virus proteins on the cellular protein interaction network. The VirusMINT database (http://mint.bio.uniroma2.it/virusmint/) aims at collecting all protein interactions between viral and human proteins reported in the literature. VirusMINT currently stores over 5000 interactions involving more than 490 unique viral proteins from more than 110 different viral strains. The whole data set can be easily queried through the search pages and the results can be displayed with a graphical viewer. The curation effort has focused on manuscripts reporting interactions between human proteins and proteins encoded by some of the most medically relevant viruses: papilloma viruses, human immunodeficiency virus 1, Epstein-Barr virus, hepatitis B virus, hepatitis C virus, herpes viruses and Simian virus 40.

Edgetic perturbation models of human inherited disorders.

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 approximately 50,000 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.