A genetic strategy to eliminate self-activator baits prior to high-throughput yeast two-hybrid screens

Genetic Dissection of Budding Yeast PCNA Mutations Responsible for the Regulated Recruitment of Srs2 Helicase

DNA-damage tolerance (DDT) is a mechanism by which eukaryotes bypass replication-blocking lesions to resume DNA synthesis and maintain cell viability. In Saccharomyces cerevisiae, DDT is mediated by sequential ubiquitination and sumoylation of proliferating cell nuclear antigen (PCNA, encoded by POL30) at the K164 residue. Deletion of RAD5 or RAD18, encoding two ubiquitin ligases required for PCNA ubiquitination, results in severe DNA-damage sensitivity, which can be rescued by inactivation of SRS2 encoding a DNA helicase that inhibits undesired homologous recombination. In this study, we isolated DNA-damage resistant mutants from rad5Δ cells and found that one of them contained a pol30-A171D mutation, which could rescue both rad5Δ and rad18Δ DNA-damage sensitivity in a srs2-dependent and PCNA sumoylation-independent manner. Pol30-A171D abolished physical interaction with Srs2 but not another PCNA-interacting protein Rad30; however, Pol30-A171 is not located in the PCNA-Srs2 interface. The PCNA-Srs2 structure was analyzed to design and create mutations in the complex interface, one of which, pol30-I128A, resulted in phenotypes reminiscent of pol30-A171D. This study allows us to conclude that, unlike other PCNA-binding proteins, Srs2 interacts with PCNA through a partially conserved motif, and the interaction can be strengthened by PCNA sumoylation, which turns Srs2 recruitment into a regulated process. IMPORTANCE It is known that budding yeast PCNA sumoylation serves as a ligand to recruit a DNA helicase Srs2 through its tandem receptor motifs that prevent unwanted homologous recombination (HR) at replication forks, a process known as salvage HR. This study reveals detailed molecular mechanisms, in which constitutive PCNA-PIP interaction has been adapted to a regulatory event. Since both PCNA and Srs2 are highly conserved in eukaryotes, from yeast to human, this study may shed light to investigation of similar regulatory mechanisms.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses

Viral infections are one of the major causes of human diseases that cause yearly millions of deaths and seriously threaten global health, as we have experienced with the COVID-19 pandemic. Numerous approaches have been adopted to understand viral diseases and develop pharmacological treatments. Among them, the study of virus-host protein-protein interactions is a powerful strategy to comprehend the molecular mechanisms employed by the virus to infect the host cells and to interact with their components. Experimental protein-protein interactions described in the scientific literature have been systematically captured into several molecular interaction databases. These data are organized in structured formats and can be easily downloaded by users to perform further bioinformatic and network studies. Network analysis of available virus-host interactomes allow us to understand how the host interactome is perturbed upon viral infection and what are the key host proteins targeted by the virus and the main cellular pathways that are subverted. In this review, we give an overview of publicly available viral-human protein-protein interactions resources and the community standards, curation rules and adopted ontologies. A description of the main virus-human interactome available is provided, together with the main network analyses that have been performed. We finally discuss the main limitations and future challenges to assess the quality and reliability of protein-protein interaction datasets and resources.

The HTLV-1 viral oncoproteins Tax and HBZ reprogram the cellular mRNA splicing landscape

Viral infections are known to hijack the transcription and translation of the host cell. However, the extent to which viral proteins coordinate these perturbations remains unclear. Here we used a model system, the human T-cell leukemia virus type 1 (HTLV-1), and systematically analyzed the transcriptome and interactome of key effectors oncoviral proteins Tax and HBZ. We showed that Tax and HBZ target distinct but also common transcription factors. Unexpectedly, we also uncovered a large set of interactions with RNA-binding proteins, including the U2 auxiliary factor large subunit (U2AF2), a key cellular regulator of pre-mRNA splicing. We discovered that Tax and HBZ perturb the splicing landscape by altering cassette exons in opposing manners, with Tax inducing exon inclusion while HBZ induces exon exclusion. Among Tax- and HBZ-dependent splicing changes, we identify events that are also altered in Adult T cell leukemia/lymphoma (ATLL) samples from two independent patient cohorts, and in well-known cancer census genes. Our interactome mapping approach, applicable to other viral oncogenes, has identified spliceosome perturbation as a novel mechanism coordinated by Tax and HBZ to reprogram the transcriptome.

Tax and HBZ are two viral regulatory proteins encoded by the human T-cell leukemia virus type 1 (HTLV-1) via sense and antisense transcripts, respectively. Both proteins are known to drive oncogenic processes that culminate in a T-cell neoplasm, known as Adult T cell leukemia/lymphoma (ATLL). We measured the effects of Tax and HBZ on host gene expression pathway by analyzing the interactome with cellular transcriptional and post-transcriptional regulators, and the transcriptome and mRNA splicing of cell lines expressing either Tax or HBZ. We compared our results with data obtained from independent cohorts of Japanese and Afro-Caribbean patients, and identified common splicing changes that might represent clinically useful biomarkers for ATLL. Finally, we provide evidence that the viral protein Tax can reprogram initial steps of the T-cell transcriptome diversification by hijacking the U2AF complex, a key cellular regulator of pre-mRNA splicing.

Yeast Two-Hybrid Screening of Photoswitchable Protein-Protein Interaction Libraries

Although widely used in the detection and characterization of protein-protein interactions, Y2H screening has been under-used for the engineering of new optogenetic tools or the improvement of existing tools. Here we explore the feasibility of using Y2H selection and screening to evaluate libraries of photoswitchable protein-protein interactions. We targeted the interaction between circularly permuted photoactive yellow protein (cPYP) and its binding partner binder of PYP dark-state (BoPD) by mutating a set of four surface residues of cPYP that contribute to the binding interface. A library of ~10,000 variants was expressed in yeast together with BoPD in a Y2H format. An initial selection for the cPYP/BoPD interaction was performed using a range of concentrations of the cPYP chromophore. As expected, the majority (>90% of cPYP variants) no longer bound to BoPD. Replica plating was then used to evaluate the photoswitchability of the surviving clones. Photoswitchable cPYP variants with BoPD affinities equal to, or higher than, native cPYP were recovered in addition to variants with altered photocycles and binders that interacted with BoPD as apo-proteins. Y2H results reflected protein-protein interaction affinity, expression, photoswitchability, and chromophore uptake, and correlated well with results obtained both in vitro and in mammalian cells. Thus, by systematic variation of selection parameters, Y2H screens can be effectively used to generate new optogenetic tools for controlling protein-protein interactions for use in diverse settings.

Screening of Duck Tembusu Virus NS3 Interacting Host Proteins and Identification of Its Specific Interplay Domains

NS3 protein is a member of the non-structural protein of duck Tembusu virus (DTMUV), which contains three domains, each of which has serine protease, nucleotide triphosphatase, and RNA helicase activities, respectively. It performs a variety of biological functions that are involved in the regulation of the viral life cycle and host immune response. Based on the yeast two-hybrid system, we successfully transformed pGBKT7-NS3 bait plasmid into Y2H Gold, tested it to prove that it has no self-activation and toxicity, and then hybridized it with the prey yeast strain of the duck embryo fibroblast cDNA library for screening. After high-stringency selection, positive alignment with the National Center for Biotechnology Information database revealed nine potential interactive proteins: MGST1, ERCC4, WIF1, WDR75, ACBD3, PRDX1, RPS7, ND5, and LDHA. The most interesting one (PRDX1) was selected to be verified with full-length NS3 protein and its three domains S7/DEXDc/HELICc using yeast regressive verification and GST Pull-Down assay. It denoted that PRDX1 does indeed interact with HELICc domains of NS3. NS3 is involved in the RNA uncoiling process of viral replication, which may cause mitochondrial overload to create oxidative stress (OS) during DTMUV attack. We deduced that the HELICc domain binding partner PRDX1, which regulates the p38/mitogen-activated protein kinase pathway (p38/MAPK) to avert OS, causing apoptosis, making it possible for viruses to escape host immune responses.

Gateway-Compatible Yeast One-Hybrid and Two-Hybrid Assays

In the first section of this introduction, we provide background information for yeast two-hybrid (Y2H) assays that provide a genetic method for the identification and analysis of binary protein-protein interactions and that are complementary to biochemical methods such as immunoprecipitation. In the second section, we discuss yeast one-hybrid (Y1H) assays that provide a "gene-centered" (DNA-to-protein) genetic method to identify and study protein-DNA interactions between cis-regulatory elements and transcription factors (TFs). This method is complementary to "TF-centered" (protein-to-DNA) biochemical methods such as chromatin immunoprecipitation.

Base-resolution stratification of cancer mutations using functional variomics

A complete understanding of human cancer variants requires new methods to systematically and efficiently assess the functional effects of genomic mutations at a large scale. Here, we describe a set of tools to rapidly clone and stratify thousands of cancer mutations at base resolution. This protocol provides a massively parallel pipeline to achieve high stringency and throughput. The approach includes high-throughput generation of mutant clones by Gateway, confirmation of variant identity by barcoding and next-generation sequencing, and stratification of cancer variants by multiplexed interaction profiling. Compared with alternative site-directed mutagenesis methods, our protocol requires less sequencing effort and enables robust statistical calling of allele-specific effects. To ensure the precision of variant interaction profiling, we further describe two complementary methods-a high-throughput enhanced yeast two-hybrid (HT-eY2H) assay and a mammalian-cell-based Gaussia princeps luciferase protein-fragment complementation assay (GPCA). These independent assays with standard controls validate mutational interaction profiles with high quality. This protocol provides experimentally derived guidelines for classifying candidate cancer alleles emerging from whole-genome or whole-exome sequencing projects as 'drivers' or 'passengers'. For ∼100 genomic mutations, the protocol-including target primer design, variant library construction, and sequence verification-can be completed within as little as 2-3 weeks, and cancer variant stratification can be completed within 2 weeks.

Proteome-Scale Human Interactomics

Cellular functions are mediated by complex interactome networks of physical, biochemical, and functional interactions between DNA sequences, RNA molecules, proteins, lipids, and small metabolites. A thorough understanding of cellular organization requires accurate and relatively complete models of interactome networks at proteome scale. The recent publication of four human protein-protein interaction (PPI) maps represents a technological breakthrough and an unprecedented resource for the scientific community, heralding a new era of proteome-scale human interactomics. Our knowledge gained from these and complementary studies provides fresh insights into the opportunities and challenges when analyzing systematically generated interactome data, defines a clear roadmap towards the generation of a first reference interactome, and reveals new perspectives on the organization of cellular life.

A Modified Reverse One-Hybrid Screen Identifies Transcriptional Activation Domains in PHYTOCHROME-INTERACTING FACTOR 3

Transcriptional activation domains (TADs) are difficult to predict and identify, since they are not conserved and have little consensus. Here, we describe a yeast-based screening method that is able to identify individual amino acid residues involved in transcriptional activation in a high throughput manner. A plant transcriptional activator, PIF3 (phytochrome interacting factor 3), was fused to the yeast GAL4-DNA-binding Domain (BD), driving expression of the URA3 (Orotidine 5'-phosphate decarboxylase) reporter, and used for negative selection on 5-fluroorotic acid (5FOA). Randomly mutagenized variants of PIF3 were then selected for a loss or reduction in transcriptional activation activity by survival on FOA. In the process, we developed a strategy to eliminate false positives from negative selection that can be used for both reverse-1- and 2-hybrid screens. With this method we were able to identify two distinct regions in PIF3 with transcriptional activation activity, both of which are functionally conserved in PIF1, PIF4, and PIF5. Both are collectively necessary for full PIF3 transcriptional activity, but neither is sufficient to induce transcription autonomously. We also found that the TAD appear to overlap physically with other PIF3 functions, such as phyB binding activity and consequent phosphorylation. Our protocol should provide a valuable tool for identifying, analyzing and characterizing novel TADs in eukaryotic transcription factors, and thus potentially contribute to the unraveling of the mechanism underlying transcriptional activation.

A combined binary interaction and phenotypic map of C. elegans cell polarity proteins

The establishment of cell polarity is an essential process for the development of multicellular organisms and the functioning of cells and tissues. Here, we combine large-scale protein interaction mapping with systematic phenotypic profiling to study the network of physical interactions that underlies polarity establishment and maintenance in the nematode Caenorhabditis elegans. Using a fragment-based yeast two-hybrid strategy, we identified 439 interactions between 296 proteins, as well as the protein regions that mediate these interactions. Phenotypic profiling of the network resulted in the identification of 100 physically interacting protein pairs for which RNAi-mediated depletion caused a defect in the same polarity-related process. We demonstrate the predictive capabilities of the network by showing that the physical interaction between the RhoGAP PAC-1 and PAR-6 is required for radial polarization of the C. elegans embryo. Our network represents a valuable resource of candidate interactions that can be used to further our insight into cell polarization.

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.

Real-time monitoring of relative peptide-protein interaction strengths in the yeast two-hybrid system

The yeast two-hybrid (Y2H) system is one of the most technically straightforward, effective, and widely used tools for the discovery of the binary peptide or protein interactions. However, its exceptional detection sensitivity poses a serious challenge for affinity ranking and hence prioritizing the resultant large number of putative interactors for follow-up analyses. To overcome this apparent bottleneck, we describe here a novel yeast growth curve-based interaction-monitoring approach that permits semiautomatic quantification, comparison, and statistically ascertained scoring of a large collection of Y2H interactions under real-time conditions. Initially, we conducted a proof-of-concept test of five literature-validated peptide-protein interactions with known affinities in the low μM range, and subsequently used the method to classify 88 novel vitamin D receptor-binding peptides derived from high-throughput screening of a highly diverse artificial peptide aptamer library. Based on our in-depth data evaluation, we conclude that real-time monitoring of clone growth as a measure of relative binding strength offers a facile, cost-effective, accurate, reproducible, and further adaptable complement to standard Y2H-derived clone management.

Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays

Protein interactions mediate essentially all biological processes and analysis of protein-protein interactions using both large-scale and small-scale approaches has contributed fundamental insights to the understanding of biological systems. In recent years, interactome network maps have emerged as an important tool for analyzing and interpreting genetic data of complex phenotypes. Complementary experimental approaches to test for binary, direct interactions, and for membership in protein complexes are used to explore the interactome. The two approaches are not redundant but yield orthogonal perspectives onto the complex network of physical interactions by which proteins mediate biological processes. In recent years, several publications have demonstrated that interactions from high-throughput experiments can be equally reliable as the high quality subset of interactions identified in small-scale studies. Critical for this insight was the introduction of standardized experimental benchmarking of interaction and validation assays using reference sets. The data obtained in these benchmarking experiments have resulted in greater appreciation of the limitations and the complementary strengths of different assays. Moreover, benchmarking is a central element of a conceptual framework to estimate interactome sizes and thereby measure progress toward near complete network maps. These estimates have revealed that current large-scale data sets, although often of high quality, cover only a small fraction of a given interactome. Here, I review the findings of assay benchmarking and discuss implications for quality control, and for strategies toward obtaining a near-complete map of the interactome of an organism.

Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system

The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.

Gene-centered yeast one-hybrid assays

Transcription is regulated by sequence-specific transcription factors (TFs) that bind to short genomic DNA elements that can be located in promoters, enhancers and other cis-regulatory modules. Determining which TFs bind where requires techniques that enable the ab initio identification of TF-DNA interactions. These techniques can either be "TF-centered" (protein-to-DNA), where regions of DNA bound by a TF of interest are identified, or "gene-centered" (DNA-to-protein), where TFs that bind a DNA sequence of interest are identified. Here, we describe gene-centered yeast one-hybrid (Y1H) assays. Briefly, in Y1H assays, a DNA fragment is cloned upstream of two different reporters, and these reporter constructs are integrated into the genome of a yeast strain. Next, plasmids expressing TFs as hybrid proteins (hence the name of the assay) fused with the strong transcriptional activation domain (AD) of the yeast TF Gal4 are introduced into the yeast strain. When a TF interacts with the DNA fragment of interest, the AD moiety activates reporter expression in yeast regardless of whether the TF is an activator or repressor in vivo. Sequencing the plasmid in the colonies that exhibit reporter activation reveals the identity of the TFs that can bind the DNA fragment. We have shown Y1H to be a robust method for detecting interactions between a variety of DNA elements and multiple families of TFs.

A stringent yeast two-hybrid matrix screening approach for protein-protein interaction discovery

The yeast two-hybrid (Y2H) system is currently one of the most important techniques for protein-protein interaction (PPI) discovery. Here, we describe a stringent three-step Y2H matrix interaction approach that is suitable for systematic PPI screening on a proteome scale. We start with the identification and elimination of autoactivating strains that would lead to false-positive signals and prevent the identification of interactions. Nonautoactivating strains are used for the primary PPI screen that is carried out in quadruplicate with arrayed preys. Interacting pairs of baits and preys are identified in a pairwise retest step. Only PPI pairs that pass the retest step are regarded as potentially biologically relevant interactions and are considered for further analysis.

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.

Protein-protein interaction networks and subnetworks in the biology of disease

The main goal of systems medicine is to provide predictive models of the patho-physiology of complex diseases as well as define healthy states. The reason is clear--we hope accurate models will ultimately lead to more specific and sensitive markers of disease that will help clinicians better stratify their patient populations and optimize treatment plans. In addition, we expect that these models will define novel targets for combating disease. However, for many complex diseases, particularly at the clinical level, it is becoming increasingly clear that one or a few genomic variations alone (e.g., simple models) cannot adequately explain the multiple phenotypes related to disease states, or the variable risks that attend disease progression. We suggest that models that account for the activities of many interacting proteins will explain a wider range of variability inherent in these phenotypes. These models, which encompass protein interaction networks dysregulated for specific diseases and specific patient sub-populations, will be constructed by integrating protein interaction data with multiple types of other relevant cellular information. Protein interaction databases are thus playing an increasingly important role in systems biology approaches to the study of disease. They present us with a static, but highly functional view of the cellular state, and thus give us a better understanding of not only the normal phenotype, but also the overall disease phenotype at the level of the whole organism when certain interactions become dysregulated.

Gene-centered regulatory network mapping

The Caenorhabditis elegans hermaphrodite is a complex multicellular animal that is composed of 959 somatic cells. The C. elegans genome contains ∼20,000 protein-coding genes, 940 of which encode regulatory transcription factors (TFs). In addition, the worm genome encodes more than 100 microRNAs and many other regulatory RNA and protein molecules. Most C. elegans genes are subject to regulatory control, most likely by multiple regulators, and combined, this dictates the activation or repression of the gene and corresponding protein in the relevant cells and under the appropriate conditions. A major goal in C. elegans research is to determine the spatiotemporal expression pattern of each gene throughout development and in response to different signals, and to determine how this expression pattern is accomplished. Gene regulatory networks describe physical and/or functional interactions between genes and their regulators that result in specific spatiotemporal gene expression. Such regulators can act at transcriptional or post-transcriptional levels. Here, I will discuss the methods that can be used to delineate gene regulatory networks in C. elegans. I will mostly focus on gene-centered yeast one-hybrid (Y1H) assays that are used to map interactions between non-coding genic regions, such as promoters, and regulatory TFs. The approaches discussed here are not only relevant to C. elegans biology, but can also be applied to other model organisms and humans.

Filtering and interpreting large-scale experimental protein-protein interaction data

Rarely acting in isolation, it is invariably the physical associations among proteins that define their biological activity, necessitating the study of the cellular meshwork of protein-protein interactions (PPI) before a full appreciation of gene function can be achieved. The past few years have seen a marked expansion in the both the sheer volume and number of organisms for which high-quality interaction data is available, with high-throughput interaction screening and detection techniques showing consistent improvement both in scale and sensitivity. Although techniques for large-scale PPI mapping are increasingly being applied to new organisms, including human, there is a corresponding need to rigorously evaluate, benchmark, and impartially filter the results. This chapter explores methods for PPI dataset evaluation, including a survey of previous techniques applied by landmark studies in the field and a discussion of promising new experimental approaches. We further outline practical suggestions and useful tools for interpreting newly generated PPI data. As the majority of large-scale experimental data has been generated for the budding yeast S. cerevisiae, most of the techniques and datasets described are from the perspective of this model unicellular eukaryote; however, extensions to other organisms including mammals are mentioned where possible.

The bait compatibility index: computational bait selection for interaction proteomics experiments

Protein interaction network maps have been generated for multiple species, making use of large-scale methods such as yeast two-hybrid (Y2H) and affinity purification mass spectrometry (AP-MS). These methods take fundamentally different approaches toward characterizing protein networks, and the resulting data sets provide complementary views of the protein interactome. The specific determinants of the outcome of Y2H and AP-MS experiments, in terms of detection of interacting proteins are, however, poorly understood. Here we show that a statistical model built using sequence- and annotation-based features of bait proteins is able to identify bait features that are significant determinants of the outcome of interaction proteomics experiments. We show that bait features are able to explain in part the disparities observed between Y2H and AP-MS constructed networks and can be used to derive the "bait compatibility index", a numeric score that assesses the compatibility of bait proteins with each technology. Aside from understanding the bias and limitations of interaction proteomics, our approach provides a rational, data-driven method for prioritization of baits for interaction proteomics experiments, an essential requirement for future proteome-wide applications of these technologies.

High-quality binary interactome mapping

Physical interactions mediated by proteins are critical for most cellular functions and altogether form a complex macromolecular "interactome" network. Systematic mapping of protein-protein, protein-DNA, protein-RNA, and protein-metabolite interactions at the scale of the whole proteome can advance understanding of interactome networks with applications ranging from single protein functional characterization to discoveries on local and global systems properties. Since the early efforts at mapping protein-protein interactome networks a decade ago, the field has progressed rapidly giving rise to a growing number of interactome maps produced using high-throughput implementations of either binary protein-protein interaction assays or co-complex protein association methods. Although high-throughput methods are often thought to necessarily produce lower quality information than low-throughput experiments, we have recently demonstrated that proteome-scale interactome datasets can be produced with equal or superior quality than that observed in literature-curated datasets derived from large numbers of small-scale experiments. In addition to performing all experimental steps thoroughly and including all necessary controls and quality standards, careful verification of all interacting pairs and validation tests using independent, orthogonal assays are crucial to ensure the release of interactome maps of the highest possible quality. This chapter describes a high-quality, high-throughput binary protein-protein interactome mapping pipeline that includes these features.

A protein domain-based interactome network for C. elegans early embryogenesis

Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.

High-quality binary protein interaction map of the yeast interactome network

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 approximately 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.

Coverage and error models of protein-protein interaction data by directed graph analysis

Using a directed graph model for bait to prey systems and a multinomial error model, we assessed the error statistics in all published large-scale datasets for Saccharomyces cerevisiae and characterized them by three traits: the set of tested interactions, artifacts that lead to false-positive or false-negative observations, and estimates of the stochastic error rates that affect the data. These traits provide a prerequisite for the estimation of the protein interactome and its modules.

Yeast two-hybrid contributions to interactome mapping

Interactome mapping, the systematic identification of protein interactions within an organism, promises to facilitate systems-level studies of biological processes. Using in vitro technologies that measure specific protein interactions, static maps are being generated that include many of the protein networks that occur in vivo. Most of the binary protein interaction data currently available was generated by large-scale yeast two-hybrid screens. Recent efforts to map interactions in model organisms and in humans illustrate the promise and some of the limitations of the two-hybrid approach. Although these maps are incomplete and include false positives, they are proving useful as a framework around which to elaborate and model the in vivo interactome.

Counter-selectable marker for bacterial-based interaction trap systems

Counter-selectable markers can be used in two-hybrid systems to search libraries for a protein or compound that interferes with a macromolecular interaction or to identify macromolecules from a population that cannot mediate a particular interaction. In this report, we describe the adaptation of the yeast URA3/5-FOA counter-selection system for use in bacterial interaction trap experiments. Two different URA3 reporter systems were developed that allow robust counter-selection: (i) a single copy F′ episome reporter and (ii) a co-cistronic HIS3-URA3 reporter vector. The HIS3-URA3 reporter can be used for either positive or negative selections in appropriate bacterial strains. These reagents extend the utility of the bacterial two-hybrid system as an alternative to its yeast-based counterpart.

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.

Effect of sampling on topology predictions of protein-protein interaction networks

Currently available protein-protein interaction (PPI) network or 'interactome' maps, obtained with the yeast two-hybrid (Y2H) assay or by co-affinity purification followed by mass spectrometry (co-AP/MS), only cover a fraction of the complete PPI networks. These partial networks display scale-free topologies--most proteins participate in only a few interactions whereas a few proteins have many interaction partners. Here we analyze whether the scale-free topologies of the partial networks obtained from Y2H assays can be used to accurately infer the topology of complete interactomes. We generated four theoretical interaction networks of different topologies (random, exponential, power law, truncated normal). Partial sampling of these networks resulted in sub-networks with topological characteristics that were virtually indistinguishable from those of currently available Y2H-derived partial interactome maps. We conclude that given the current limited coverage levels, the observed scale-free topology of existing interactome maps cannot be confidently extrapolated to complete interactomes.

Interactome: gateway into systems biology

Protein-protein interactions are fundamental to all biological processes, and a comprehensive determination of all protein-protein interactions that can take place in an organism provides a framework for understanding biology as an integrated system. The availability of genome-scale sets of cloned open reading frames has facilitated systematic efforts at creating proteome-scale data sets of protein-protein interactions, which are represented as complex networks or 'interactome' maps. Protein-protein interaction mapping projects that follow stringent criteria, coupled with experimental validation in orthogonal systems, provide high-confidence data sets immanently useful for interrogating developmental and disease mechanisms at a system level as well as elucidating individual protein function and interactome network topology. Although far from complete, currently available maps provide insight into how biochemical properties of proteins and protein complexes are integrated into biological systems. Such maps are also a useful resource to predict the function(s) of thousands of genes.

A bacterial one-hybrid system for determining the DNA-binding specificity of transcription factors

The DNA-binding specificities of transcription factors can be used to computationally predict cis-regulatory modules (CRMs) that regulate gene expression. However, the absence of specificity data for the majority of transcription factors limits the widespread implementation of this approach. We have developed a bacterial one-hybrid system that provides a simple and rapid method to determine the DNA-binding specificity of a transcription factor. Using this technology, we successfully determined the DNA-binding specificity of seven previously characterized transcription factors and one novel transcription factor, the Drosophila melanogaster factor Odd-skipped. Regulatory targets of Odd-skipped were successfully predicted using this information, demonstrating that the data produced by the bacterial one-hybrid system are relevant to in vivo function.

Interactome modeling

A long-term goal of the field of interactome modeling is to understand how global and local properties of complex macromolecular networks impact on observable biological properties, and how changes in such properties can lead to human diseases. The information available at this stage of development of the field provides strong evidence for the existence of such interesting global and local properties, but also demonstrates that many more datasets will be needed to provide accurate models with increasingly predictive capacity. This review focuses on an early attempt at mapping a multicellular interactome network and on the lessons learned from that attempt.

Increasing specificity in high-throughput yeast two-hybrid experiments

Since its inception, the yeast two-hybrid (Y2H) system has proven to be an efficient system to identify novel protein-protein interactions. However, Y2H screens are sometimes criticized for generating high rates of false-positives. Minimizing false-positive interactions is especially important in proteome wide high-throughput (HT) Y2H. Here, we summarize various approaches that reduce false-positives in HT-Y2H projects. We evaluated the potential of examining putative positives after removing the prey encoding plasmid by negative selection. We found that this method reliably identifies false-positives caused by spontaneous conversion of baits into auto-activators and provides significant time-savings in HT screens. In addition, we present a method to eliminate an important source of false-positives: contaminating prey plasmids. Y2H interactors can be wrongly identified due to the presence of two or more different plasmids in the cells of a single yeast colony. Of these independent plasmids, only one encodes a genuine interactor. Contaminating plasmids are eliminated by extended culture of yeast cells under positive selection for the interaction, allowing the identification of the true interaction partner.

Application of expression genomics in drug development and genomic medicine

Expressed sequence tags and genome sequencing have virtually uncovered all the genes and transcripts in the human genome. Functional genomic analysis of these genes will undoubtedly reveal their physiological roles in cells in the near future. Coupled with the advent of DNA microarray, cellular response to perturbation can now be examined at genome‐wide levels in a single analysis, yielding clues to the network of pathways and interacting pathways that underlie a comprehensive systems response to exposure to small molecule perturbants. This post‐genomic information will be the foundation for knowledge‐based in silico systems biology. It is envisioned that querying such relational databases will generate testable hypotheses as well as revealing information on networks of regulatory genes and pathways that could further fuel and shape molecular and drug target discovery, and ushering in a new era in genomic medicine. In this review, we will first discuss examples of laboratory results generated from high‐throughput microarray analyses that illuminated previously unrecognized networks of regulatory genes and pathways that point to the mechanisms of action for the tumor promoter 12‐O‐tetradecanoyl‐phorbol‐13‐acetate, and the green tea polyphenol, epi‐gallocatechin gallate. We will then focus on the application of gene expression profiles in genomic medicine for predicting treatment response in cancer, using clinical specimens obtained from patients. These studies and others point to an increasing trend in modern biology in which high‐throughput genome scale comprehensive biological information is incorporated into databases for constructing and reconstructing pathways and networks of interacting pathways that constitute a physiological response to environmental perturbation at the organism or systems levels. Drug Dev. Res. 62:124–133, 2004. © 2004 Wiley‐Liss, Inc.

Identification of critical domains and putative partners for the Caenorhabditis elegans spindle component LIN-5

Successful cell division requires proper assembly, placement and functioning of the spindle apparatus that segregates the chromosomes. The Caenorhabditis elegans gene lin-5 encodes a novel coiled-coil component of the spindle required for spindle positioning and chromosome segregation. To gain further insights into lin-5 function, we screened for dominant suppressors of the partial loss-of-function phenotype associated with the mutation lin-5(ev571ts ), and isolated 68 suppressing mutations. Eight out of the ten suppressors sequenced contained intragenic missense mutations immediately upstream of the lesion in lin-5(ev571ts ). These probably help to stabilize protein-protein interactions mediated by the coiled-coil domain. This domain was found to be required for binding to several putative LIN-5 interacting (LFI) proteins identified in yeast two-hybrid screens. Interestingly, interaction with the coiled-coil protein LFI-1 was specifically reduced by the lin-5(ev571ts ) mutation and restored by a representative intragenic suppressor mutation. Immunostaining experiments showed that LIN-5 and LFI-1 may co-localize around the kinetochore microtubules during metaphase, indicating potential interaction in vivo. The coiled-coil domain of LIN-5 was also found to mediate homodimerization, while the C-terminal region of LIN-5 was sufficient for interaction with GPR-1, a recently identified component of a LIN-5 spindle-regulatory complex. A single amino-acid substitution in the N-terminal region of LIN-5, encoded by the e1457 allele, abolished all LIN-5 interactions. Taken together, our results indicate that the spindle functions of LIN-5 depend on interactions with multiple protein partners, and that these interactions are mediated through several different domains of LIN-5.

A strategy for constructing large protein interaction maps using the yeast two-hybrid system: regulated expression arrays and two-phase mating

Maps representing the binary interactions among proteins have become valuable tools for understanding how proteins work together to mediate biological processes. One of the most effective methods for detecting biologically important protein interactions has been the yeast two-hybrid system. Here we present an efficient two-hybrid strategy to facilitate construction of protein interaction maps on a genome-wide scale. The strategy begins with two arrays of yeast expressing known proteins fused to either a DNA binding domain (BD), or a transcription activation domain (AD). The fusion proteins are conditionally expressed using regulated promoters that can be repressed during construction and amplification of the yeast arrays. Interaction assays are conducted in two phases. In the first phase, small pools of AD strains are mated with the array of BD strains. In the second phase, individual BD strains are mated with appropriate subsets of the AD array corresponding to positive pools in the first phase. This strategy has several advantages over previously described approaches, including the ability to detect interactions with proteins that inhibit yeast growth or that activate transcription as BD fusions. Moreover, by minimizing the number of mating operations and sequencing reactions needed to test large sets of binary interactions, this strategy is more efficient than either matrix or library screening approaches. We also present a three-dimensional pooling scheme to further increase the efficiency of large-scale two-hybrid analyses.

Using the yeast interaction trap and other two-hybrid-based approaches to study protein-protein interactions

The detection of physical interaction between two or more molecules of interest can be facilitated if the act of association between the interactive partners leads to the production of a readily observed biological or physical readout. Many interacting molecule pairs (X, Y) can be made to induce such a readout if X and Y are each fused to defined protein elements with desired properties. For example, in the yeast forward two-hybrid system, X is synthesized as a translational fusion to a DNA-binding domain (DBD), Y is synthesized as a fusion to a transcriptional activation domain (AD), and coexpression of DBD-X and AD-Y induces transcription of easily scored responsive reporters. Other approaches use paradigms based on the artificial production of two, hybrid, molecules, but substitute a variety of readouts including the repression of transcription, activation of signal transduction pathways, or reconstitution of a disrupted enzymatic activity. In this article, we summarize a number of two-hybrid-based approaches, and detail the use of the forward yeast two-hybrid system in a screen to identify novel interacting partners for a protein of interest.

High-throughput yeast two-hybrid assays for large-scale protein interaction mapping

Protein-protein interactions play fundamental roles in many biological processes. Hence, protein interaction mapping is becoming a well-established functional genomics approach to generate functional annotations for predicted proteins that so far have remained uncharacterized. The yeast two-hybrid system is currently one of the most standardized protein interaction mapping techniques. Here, we describe the protocols for a semiautomated, high-throughput, Gal4-based yeast two-hybrid system.

Genetic analysis of the Escherichia coli FtsZ.ZipA interaction in the yeast two-hybrid system. Characterization of FtsZ residues essential for the interactions with ZipA and with FtsA

The recruitment of ZipA to the septum by FtsZ is an early, essential step in cell division in Escherichia coli. We have used polymerase chain reaction-mediated random mutagenesis in the yeast two-hybrid system to analyze this interaction and have identified residues within a highly conserved sequence at the C terminus of FtsZ as the ZipA binding site. A search for suppressors of a mutation that causes a loss of interaction (ftsZ(D373G)) identified eight different changes at two residues within this sequence. In vitro, wild type FtsZ interacted with ZipA with a high affinity in an enzyme-linked immunosorbent assay, whereas FtsZ(D373G) failed to interact. Two mutant proteins examined restored this interaction significantly. In vivo, the alleles tested are significantly more toxic than the wild type ftsZ and cannot complement a deletion. We have shown that a fusion, which encodes the last 70 residues of FtsZ in the two-hybrid system, is sufficient for the interaction with FtsA and ZipA. However, when the wild type sequence is compared with one that encodes FtsZ(D373G), no interaction was seen with either protein. Mutations surrounding Asp-373 differentially affected the interactions of FtsZ with ZipA and FtsA, indicating that these proteins bind the C terminus of FtsZ differently.

The continued evolution of two-hybrid screening approaches in yeast: how to outwit different preys with different baits

The original two-hybrid system, an experimental approach designed to detect protein interactions, exploited the modular nature of many transcription factors. It has provided the intellectual and technical seed for the evolution of an array of innovative approaches, the application of which broadens the scope of experimentally feasible questions to include the interaction of proteins with diverse binding partners. The available array of modified and alternative approaches facilitates the analysis of complex cellular machinery and signaling networks that rely on multiple protein interactions. Such advances have facilitated the functional analysis of proteins on the genome level, a feat considered untenable a decade ago.

Protein interaction mapping in C. elegans using proteins involved in vulval development

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.