Benchmarking yeast two-hybrid systems using the interactions of bacterial motility proteins

Structural insights into the disulfide isomerase and chaperone activity of TrbB of the F plasmid type IV secretion system

Bacteria have evolved elaborate mechanisms to thrive in stressful environments. F-like plasmids in gram-negative bacteria encode for a multi-protein Type IV Secretion System (T4SSF) that is functional for bacterial proliferation and adaptation through the process of conjugation. The periplasmic protein TrbB is believed to have a stabilizing chaperone role in the T4SSF assembly, with TrbB exhibiting disulfide isomerase (DI) activity. In the current report, we demonstrate that the deletion of the disordered N-terminus of TrbBWT, resulting in a truncation construct TrbB37-161, does not affect its catalytic in vitro activity compared to the wild-type protein (p = 0.76). Residues W37-K161, which include the active thioredoxin motif, are sufficient for DI activity. The N-terminus of TrbBWT is disordered as indicated by a structural model of GST-TrbBWT based on ColabFold-AlphaFold2 and Small Angle X-Ray Scattering data and 1H-15N Heteronuclear Single Quantum Correlation (HSQC) spectroscopy of the untagged protein. This disordered region likely contributes to the protein's dynamicity; removal of this region results in a more stable protein based on 1H-15N HSQC and Circular Dichroism Spectroscopies. Lastly, size exclusion chromatography analysis of TrbBWT in the presence of TraW, a T4SSF assembly protein predicted to interact with TrbBWT, does not support the inference of a stable complex forming in vitro. This work advances our understanding of TrbB's structure and function, explores the role of structural disorder in protein dynamics in the context of a T4SSF accessory protein, and highlights the importance of redox-assisted protein folding in the T4SSF.

Screening of genes encoding proteins that interact with ISG15: Probing a cDNA library from a snakehead fish cell line using a yeast two-hybrid system

Interferon-stimulated gene 15 (ISG15) regulates cellular life processes, including defense responses against infection by a variety of viral pathogens, by binding to target proteins. At present, various fish ISG15s have been identified, but the biological function of ISG15 in snakehead fish is still unclear. In this study, total RNA was extracted from snakehead fish cell line E11, ds cDNA was synthesized and purified using SMART technology, and the resulting cDNA library was screened by co-transforming yeast cells. The library titer was 4.28 × 10⁹ CFU/mL. Using snakehead ISG15 as the bait protein, the recombinant bait vector pGBKT7-ISG15 was constructed and transformed into the yeast strain Y2HGold. The toxicity and self-activation activity of the bait vector were detected on the deficient medium, and the prey proteins interacting with ISG15 were screened. In total, 19 interacting proteins of ISG15 were identified, including mitotic checkpoint protein BUB3, hypothetical protein SnRVgp6, elongation factor 1-beta, 60S ribosomal protein L9, dual specificity protein phosphatase 5-like, eukaryotic translation initiation factor 3 subunit I and ferritin. A yeast spotting assay further probed the interaction between ISG15 and DUSP5. These results increase our understanding of the interaction network of snakehead ISG15 and will aid in exploring the underlying mechanisms of snakehead ISG15 functions in the future.

Global mapping of Salmonella enterica-host protein-protein interactions during infection

Intracellular bacterial pathogens inject effector proteins to hijack host cellular processes and promote their survival and proliferation. To systematically map effector-host protein-protein interactions (PPIs) during infection, we generated a library of 32 Salmonella enterica serovar Typhimurium (STm) strains expressing chromosomally encoded affinity-tagged effectors and quantified PPIs in macrophages and epithelial cells. We identified 446 effector-host PPIs, 25 of which were previously described, and validated 13 by reciprocal co-immunoprecipitation. While effectors converged on the same host cellular processes, most had multiple targets, which often differed between cell types. We demonstrate that SseJ, SseL, and SifA modulate cholesterol accumulation at the Salmonella-containing vacuole (SCV) partially via the cholesterol transporter Niemann-Pick C1 protein. PipB recruits the organelle contact site protein PDZD8 to the SCV, and SteC promotes actin bundling by phosphorylating formin-like proteins. This study provides a method for probing host-pathogen PPIs during infection and a resource for interrogating STm effector mechanisms.

Towards a unified open access dataset of molecular interactions

The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.

Spatiotemporal Restriction of FUSCA3 Expression by Class I BPCs Promotes Ovule Development and Coordinates Embryo and Endosperm Growth

Spatiotemporal regulation of gene expression is critical for proper developmental timing in plants and animals. The transcription factor FUSCA3 (FUS3) regulates developmental phase transitions by acting as a link between hormonal pathways in Arabidopsis (Arabidopsis thaliana). However, the mechanisms governing its spatiotemporal expression pattern are poorly understood. Here, we show that FUS3 is repressed in the ovule integuments and seed endosperm. FUS3 repression requires class I BASIC PENTACYSTEINE (BPC) proteins, which directly bind GA/CT cis-elements in FUS3 and restrict its expression pattern. During vegetative and reproductive development, FUS3 derepression in bpc1-1 bpc2 (bpc1/2) double mutant or misexpression in ProML1:FUS3 lines causes dwarf plants carrying defective flowers and aborted ovules. After fertilization, ectopic FUS3 expression in bpc1/2 endosperm or ProML1:FUS3 endosperm and endothelium increases endosperm nuclei proliferation and seed size, causing delayed or arrested embryo development. These phenotypes are rescued in bpc1/2 fus3-3 Finally, class I BPCs interact with FIS-PRC2 (FERTILIZATION-INDEPENDENT SEED-Polycomb Repressive Complex2), which represses FUS3 in the endosperm during early seed development. We propose that BPC1 and 2 promote the transition from reproductive to seed development by repressing FUS3 in ovule integuments. After fertilization, BPC1 and 2 and FIS-PRC2 repress FUS3 in the endosperm to coordinate early endosperm and embryo growth.

Screening Arrayed Libraries with DNA and Protein Baits to Identify Interacting Proteins

Molecular interactions are an integral part of the regulatory mechanisms controlling gene expression. The yeast one- and two-hybrid systems (Y1H/Y2H) have been widely used by many laboratories to detect DNA-protein (Y1H) and protein-protein interactions (Y2H). The development of efficient cloning systems have promoted the generation of large open reading frame (ORF) clone collections (libraries) for several organisms. Functional analyses of such large collections require the establishment of adequate protocols. Here, we describe a simple straightforward procedure for high-throughput screenings of arrayed libraries with DNA or protein baits that can be carried out by one person with minimal labor and not requiring robotics. The protocol can also be scaled up or down and is compatible with several library formats. Procedures to make yeast stocks for long-term storage (tube and microplate formats) are also provided.

Options and Considerations When Using a Yeast One-Hybrid System

Comprehensive mapping of protein-DNA interactions is essential to uncover the mechanisms involved in gene regulation. However, the data generated has been sparse given the number of regulatory elements and transcription factors (TFs) encoded in the genomes of metazoan organisms. Yeast one-hybrid (Y1H) assays provide a powerful "DNA-centered" method, complementary to "TF-centered" methods such as chromatin immunoprecipitation, to identify the TFs that can bind a DNA sequence of interest. Here, we present different technical variations that should be considered when using a Y1H system, including the type of DNA sequence to test, source of TF clones, as well as types of vectors and screening format. Finally, we discuss limitations of the assay and future challenges.

A Yeast 2-Hybrid Screen in Batch to Compare Protein Interactions

Screening for protein-protein interactions using the yeast 2-hybrid assay has long been an effective tool, but its use has largely been limited to the discovery of high-affinity interactors that are highly enriched in the library of interacting candidates. In a traditional format, the yeast 2-hybrid assay can yield too many colonies to analyze when conducted at low stringency where low affinity interactors might be found. Moreover, without a comprehensive and complete interrogation of the same library against different bait plasmids, a comparative analysis cannot be achieved. Although some of these problems can be addressed using arrayed prey libraries, the cost and infrastructure required to operate such screens can be prohibitive. As an alternative, we have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing a strategy termed DEEPN (Dynamic Enrichment for Evaluation of Protein Networks), which incorporates high-throughput DNA sequencing and computation to follow the evolution of a population of plasmids that encode interacting partners. Here, we describe customized reagents and protocols that allow a DEEPN screen to be executed easily and cost-effectively.

Making the Right Choice: Critical Parameters of the Y2H Systems

Two-hybrid methods remain among the most preferred choices for detecting protein-protein interactions (PPIs) and much of the PPI data in databases have been produced using yeast two-hybrid (Y2H) screens. The Y2H methods are extensively used to detect PPIs because of their scalability and accessibility. Several variants of Y2H methods have been developed and used by different research groups, increasing the accessibility of these methods and their applications in detecting different types of PPIs. However, the availability of variations on the same core methodology emphasizes the need to have a systematic comparison of available Y2H methods in the context of their applicability, coverage and efficiency. In this chapter, we discuss the key parameters of Y2H methods, namely proteins of interest, vectors, libraries, screening strategies, data analysis, and provide a flowchart that should help to decide which Y2H strategy is most appropriate for a protein interaction screen.

Mechanisms of action of Coxiella burnetii effectors inferred from host-pathogen protein interactions

Coxiella burnetii is an obligate Gram-negative intracellular pathogen and the etiological agent of Q fever. Successful infection requires a functional Type IV secretion system, which translocates more than 100 effector proteins into the host cytosol to establish the infection, restructure the intracellular host environment, and create a parasitophorous vacuole where the replicating bacteria reside. We used yeast two-hybrid (Y2H) screening of 33 selected C. burnetii effectors against whole genome human and murine proteome libraries to generate a map of potential host-pathogen protein-protein interactions (PPIs). We detected 273 unique interactions between 20 pathogen and 247 human proteins, and 157 between 17 pathogen and 137 murine proteins. We used orthology to combine the data and create a single host-pathogen interaction network containing 415 unique interactions between 25 C. burnetii and 363 human proteins. We further performed complementary pairwise Y2H testing of 43 out of 91 C. burnetii-human interactions involving five pathogen proteins. We used the combined data to 1) perform enrichment analyses of target host cellular processes and pathways, 2) examine effectors with known infection phenotypes, and 3) infer potential mechanisms of action for four effectors with uncharacterized functions. The host-pathogen interaction profiles supported known Coxiella phenotypes, such as adapting cell morphology through cytoskeletal re-arrangements, protein processing and trafficking, organelle generation, cholesterol processing, innate immune modulation, and interactions with the ubiquitin and proteasome pathways. The generated dataset of PPIs-the largest collection of unbiased Coxiella host-pathogen interactions to date-represents a rich source of information with respect to secreted pathogen effector proteins and their interactions with human host proteins.

A Comparison of Two-Hybrid Approaches for Detecting Protein-Protein Interactions

Two-hybrid systems are one of the most popular, preferred, cost effective, and scalable in vivo genetic approaches for screening protein-protein interactions. A number of variants of yeast and bacterial two-hybrid systems exist, rendering them ideal for modern, flexible proteomics-driven studies. For mapping protein interactions at genome scales (that is, constructing an interactome), the yeast two-hybrid system has been extensively tested and is preferred over bacterial two-hybrid systems, given that users have created more resources such as a variety of vectors and other modifications. Each system has its own advantages and limitations and thus needs to be compared directly. For instance, the bacterial two-hybrid method seems a better fit than the yeast two-hybrid system to screen membrane-associated proteins. In this chapter, we provide detailed protocols for yeast and bacterial two-hybrid systems as well as a comparison of outcomes for each approach using our own and published data.

Yeast One- and Two-Hybrid High-Throughput Screenings Using Arrayed Libraries

Since their original description more than 25 years ago, the yeast one- and two-hybrid systems (Y1H/Y2H) have been used by many laboratories to detect DNA-protein (Y1H) and protein-protein interactions (Y2H). These systems use yeast cells (Saccharomyces cerevisiae) as a eukaryotic "test tube" and are amenable for most labs in the world. The development of highly efficient cloning methods has fostered the generation of large collections of open reading frames (ORFs) for several organisms that have been used for yeast screenings. Here, we describe a simple mating based method for high-throughput screenings of arrayed ORF libraries with DNA (Y1H) or protein (Y2H) baits not requiring robotics. One person can easily carry out this protocol in approximately 10 h of labor spread over 5 days. It can also be scaled down to test one-to-one (few) interactions, scaled up (i.e., robotization) and is compatible with several library formats (i.e., 96, 384-well microtiter plates).

Arabidopsis TCP Transcription Factors Interact with the SUMO Conjugating Machinery in Nuclear Foci

In Arabidopsis more than 400 proteins have been identified as SUMO targets, both in vivo and in vitro. Among others, transcription factors (TFs) are common targets for SUMO conjugation. Here we aimed to exhaustively screen for TFs that interact with the SUMO machinery using an arrayed yeast two-hybrid library containing more than 1,100 TFs. We identified 76 interactors that foremost interact with the SUMO conjugation enzyme SCE1 and/or the SUMO E3 ligase SIZ1. These interactors belong to various TF families, which control a wide range of processes in plant development and stress signaling. Amongst these interactors, the TCP family was overrepresented with several TCPs interacting with different proteins of the SUMO conjugation cycle. For a subset of these TCPs we confirmed that the catalytic site of SCE1 is essential for this interaction. In agreement, TCP1, TCP3, TCP8, TCP14, and TCP15 were readily SUMO modified in an E. coli sumoylation assay. Strikingly, these TCP-SCE1 interactions were found to redistribute these TCPs into nuclear foci/speckles, suggesting that these TCP foci represent sites for SUMO (conjugation) activity.

The Shigella flexneri OmpA amino acid residues 188EVQ190 are essential for the interaction with the virulence factor PhoN2

Shigella flexneri is an intracellular pathogen that deploys an arsenal of virulence factors promoting host cell invasion, intracellular multiplication and intra- and inter-cellular dissemination. We have previously reported that the interaction between apyrase (PhoN2), a periplasmic ATP-diphosphohydrolase, and the C-terminal domain of the outer membrane (OM) protein OmpA is likely required for proper IcsA exposition at the old bacterial pole and thus for full virulence expression of Shigella flexneri (Scribano et al., 2014). OmpA, that is the major OM protein of Gram-negative bacteria, is a multifaceted protein that plays many different roles both in the OM structural integrity and in the virulence of several pathogens. Here, by using yeast two-hybrid technology and by constructing an in silico 3D model of OmpA from S. flexneri 5a strain M90T, we observed that the OmpA residues ₁₈₈EVQ₁₉₀ are likely essential for PhoN2-OmpA interaction. The ₁₈₈EVQ₁₉₀ amino acids are located within a flexible region of the OmpA protein that could represent a scaffold for protein-protein interaction.

Mapping the Protein-Protein Interactome Networks Using Yeast Two-Hybrid Screens

The yeast two-hybrid system (Y2H) is a powerful method to identify binary protein-protein interactions in vivo. Here we describe Y2H screening strategies that use defined libraries of open reading frames (ORFs) and cDNA libraries. The array-based Y2H system is well suited for interactome studies of small genomes with an existing ORFeome clones preferentially in a recombination based cloning system. For large genomes, pooled library screening followed by Y2H pairwise retests may be more efficient in terms of time and resources, but multiple sampling is necessary to ensure comprehensive screening. While the Y2H false positives can be efficiently reduced by using built-in controls, retesting, and evaluation of background activation; implementing the multiple variants of the Y2H vector systems is essential to reduce the false negatives and ensure comprehensive coverage of an interactome.

A yeast two-hybrid approach for probing protein-protein interactions at the centrosome

As a large, nonmembrane bound organelle, the centrosome must rely heavily on protein-protein interactions to assemble itself in the cytoplasm and perform its functions as a microtubule-organizing center. Therefore, to understand how this organelle is built and functions, one must understand the protein-protein interactions made by each centrosome protein. Unfortunately, the highly interconnected nature of the centrosome, combined with its predicted unstructured, coil-rich proteins, has made the use of many standard approaches to studying protein-protein interactions very challenging. The yeast-two hybrid (Y2H) system is well suited for studying the centrosome and is an important complement to other biochemical approaches. In this chapter we describe how to carry out a directed Y2H screen to identify the direct interactions between a given centrosome protein and a library of others. Specifically, we detail using a bioinformatics-based approach (structure prediction programs) to subdivide proteins and screen for interactions using an array-based Y2H approach. We also describe how to use the interaction information garnered from this screen to generate mutations to disrupt specific interactions using mutagenic-PCR and a "reverse" Y2H screen. Finally, we discuss how information from such a screen can be integrated into existing models of centrosome assembly and how it can initiate and guide extensive in vitro and in vivo experimentation to test these models.

Chromosome segregation proteins of Vibrio cholerae as transcription regulators

ABSTRACT Bacterial ParA and ParB proteins are best known for their contribution to plasmid and chromosome segregation, but they may also contribute to other cell functions. In segregation, ParA interacts with ParB, which binds to parS centromere-analogous sites. In transcription, plasmid Par proteins can serve as repressors by specifically binding to their own promoters and, additionally, in the case of ParB, by spreading from a parS site to nearby promoters. Here, we have asked whether chromosomal Par proteins can likewise control transcription. Analysis of genome-wide ParB1 binding in Vibrio cholerae revealed preferential binding to the three known parS1 sites and limited spreading of ParB1 beyond the parS1 sites. Comparison of wild-type transcriptomes with those of ΔparA1, ΔparB1, and ΔparAB1 mutants revealed that two out of 20 genes (VC0067 and VC0069) covered by ParB1 spreading are repressed by both ParB1 and ParA1. A third gene (VC0076) at the outskirts of the spreading area and a few genes further away were also repressed, particularly the gene for an outer membrane protein, ompU (VC0633). Since ParA1 or ParB1 binding was not evident near VC0076 and ompU genes, the repression may require participation of additional factors. Indeed, both ParA1 and ParB1 proteins were found to interact with several V. cholerae proteins in bacterial and yeast two-hybrid screens. These studies demonstrate that chromosomal Par proteins can repress genes unlinked to parS and can do so without direct binding to the cognate promoter DNA. IMPORTANCE Directed segregation of chromosomes is essential for their maintenance in dividing cells. Many bacteria have genes (par) that were thought to be dedicated to segregation based on analogy to their roles in plasmid maintenance. It is becoming clear that chromosomal par genes are pleiotropic and that they contribute to diverse processes such as DNA replication, cell division, cell growth, and motility. One way to explain the pleiotropy is to suggest that Par proteins serve as or control other transcription factors. We tested this model by determining how Par proteins affect genome-wide transcription activity. We found that genes implicated in drug resistance, stress response, and pathogenesis were repressed by Par. Unexpectedly, the repression did not involve direct Par binding to cognate promoter DNA, indicating that the repression may involve Par interactions with other regulators. This pleiotropy highlights the degree of integration of chromosomal Par proteins into cellular control circuitries.

A second-generation protein-protein interaction network of Helicobacter pylori

Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein-protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.

Systems biology approaches for discovering biomarkers for traumatic brain injury

The rate of traumatic brain injury (TBI) in service members with wartime injuries has risen rapidly in recent years, and complex, variable links have emerged between TBI and long-term neurological disorders. The multifactorial nature of TBI secondary cellular response has confounded attempts to find cellular biomarkers for its diagnosis and prognosis or for guiding therapy for brain injury. One possibility is to apply emerging systems biology strategies to holistically probe and analyze the complex interweaving molecular pathways and networks that mediate the secondary cellular response through computational models that integrate these diverse data sets. Here, we review available systems biology strategies, databases, and tools. In addition, we describe opportunities for applying this methodology to existing TBI data sets to identify new biomarker candidates and gain insights about the underlying molecular mechanisms of TBI response. As an exemplar, we apply network and pathway analysis to a manually compiled list of 32 protein biomarker candidates from the literature, recover known TBI-related mechanisms, and generate hypothetical new biomarker candidates.

The protein interaction network of bacteriophage lambda with its host, Escherichia coli

Although most of the 73 open reading frames (ORFs) in bacteriophage λ have been investigated intensively, the function of many genes in host-phage interactions remains poorly understood. Using yeast two-hybrid screens of all lambda ORFs for interactions with its host Escherichia coli, we determined a raw data set of 631 host-phage interactions resulting in a set of 62 high-confidence interactions after multiple rounds of retesting. These links suggest novel regulatory interactions between the E. coli transcriptional network and lambda proteins. Targeted host proteins and genes required for lambda infection are enriched among highly connected proteins, suggesting that bacteriophages resemble interaction patterns of human viruses. Lambda tail proteins interact with both bacterial fimbrial proteins and E. coli proteins homologous to other phage proteins. Lambda appears to dramatically differ from other phages, such as T7, because of its unusually large number of modified and processed proteins, which reduces the number of host-virus interactions detectable by yeast two-hybrid screens.

The evolutionary dynamics of protein-protein interaction networks inferred from the reconstruction of ancient networks

Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.

Yeast two-hybrid analysis of jasmonate signaling proteins

Protein-protein interaction studies are crucial to unravel how jasmonate (JA) signals are transduced. Among the different techniques available, yeast two-hybrid (Y2H) is commonly used within the JA research community to identify proteins belonging to the core JA signaling module. The technique is based on the reconstitution of a transcriptional activator that drives the reporter gene expression upon protein-protein interactions. The method is sensitive and straightforward and can be adapted for different approaches. In this chapter, we provide a detailed protocol to perform targeted Y2H assays to test known proteins and/or protein domains for direct interaction in a pairwise manner and present the possibility to study ternary protein complexes through Y3H.

Development and application of a DNA microarray-based yeast two-hybrid system

The yeast two-hybrid (Y2H) system is the most widely applied methodology for systematic protein–protein interaction (PPI) screening and the generation of comprehensive interaction networks. We developed a novel Y2H interaction screening procedure using DNA microarrays for high-throughput quantitative PPI detection. Applying a global pooling and selection scheme to a large collection of human open reading frames, proof-of-principle Y2H interaction screens were performed for the human neurodegenerative disease proteins huntingtin and ataxin-1. Using systematic controls for unspecific Y2H results and quantitative benchmarking, we identified and scored a large number of known and novel partner proteins for both huntingtin and ataxin-1. Moreover, we show that this parallelized screening procedure and the global inspection of Y2H interaction data are uniquely suited to define specific PPI patterns and their alteration by disease-causing mutations in huntingtin and ataxin-1. This approach takes advantage of the specificity and flexibility of DNA microarrays and of the existence of solid-related statistical methods for the analysis of DNA microarray data, and allows a quantitative approach toward interaction screens in human and in model organisms.

A comparison and optimization of yeast two-hybrid systems

Two-hybrid (Y2H) assays are available in a variety of different versions, including bacterial, yeast, and mammalian systems. However, even when done exclusively in yeast, multiple different host strains, vectors, reporter genes, or protocols can be used. Here we systematically compare protein-protein interactions (PPIs) from several previously published Y2H datasets. PPIs of a human gold-standard dataset were generated by Y2H assays as well as other methods such as LUMIER or protein fragment complementation assays (PCAs). Different Y2H methods detect substantially different subsets of these PPIs, even when protocols are standardized. In order to maximize the number of interactions found and to minimize the number of false positive interactions we recommend to combine multiple vectors and protocols. While the combined results of all 18 methods detected about 92% of a gold-standard interaction set, a combination of just three Y2H assays detected up to 78% of these protein pairs, or up to 83% when a fourth assay was included. These findings indicate that three or four separate assays may be sufficient to detect the majority of protein-protein interactions in many systems.

Efficient prediction of co-complexed proteins based on coevolution

The prediction of the network of protein-protein interactions (PPI) of an organism is crucial for the understanding of biological processes and for the development of new drugs. Machine learning methods have been successfully applied to the prediction of PPI in yeast by the integration of multiple direct and indirect biological data sources. However, experimental data are not available for most organisms. We propose here an ensemble machine learning approach for the prediction of PPI that depends solely on features independent from experimental data. We developed new estimators of the coevolution between proteins and combined them in an ensemble learning procedure.

We applied this method to a dataset of known co-complexed proteins in Escherichia coli and compared it to previously published methods. We show that our method allows prediction of PPI with an unprecedented precision of 95.5% for the first 200 sorted pairs of proteins compared to 28.5% on the same dataset with the previous best method.

A close inspection of the best predicted pairs allowed us to detect new or recently discovered interactions between chemotactic components, the flagellar apparatus and RNA polymerase complexes in E. coli.

Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments

Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., co-occurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability. The scoring methods and platforms described include: CompPASS, SAINT, Decontaminator, MINT, IntAct, STRING, and FunCoup. References to related work are provided throughout in order to provide a concise but thorough introduction to a rapidly growing interdisciplinary field of investigation.

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.

Negative protein-protein interaction datasets derived from large-scale two-hybrid experiments

Negative protein-protein interaction datasets are needed for training and evaluation of interaction prediction methods, as well as validation of high-throughput interaction discovery experiments. In large-scale two-hybrid assays, the direct interaction of a large number of protein pairs is systematically probed. We present a simple method to harness two-hybrid data to obtain negative protein-protein interaction datasets, which we validated using other available experimental data. The method identifies interactions that were likely tested but not observed in a two-hybrid screen. For each negative interaction, a confidence score is defined as the shortest-path length between the two proteins in the interaction network derived from the two-hybrid experiment. We show that these high-quality negative datasets are particularly important when a specific biological context is considered, such as in the study of protein interaction specificity. We also illustrate the use of a negative dataset in the evaluation of the InterPreTS interaction prediction method.

Yeast one-hybrid assays: a historical and technical perspective

Since its development about two decades ago, the yeast one-hybrid (Y1H) assay has become an important technique for detecting physical interactions between sequence-specific regulatory transcription factor proteins (TFs) and their DNA target sites. Multiple versions of the Y1H methodology have been developed, each with technical differences and unique advantages. We will discuss several of these technical variations in detail, and also provide some ideas for how Y1H assays can be further improved.

Studying protein complexes by the yeast two-hybrid system

Protein complexes are typically analyzed by affinity purification and subsequent mass spectrometric analysis. However, in most cases the structure and topology of the complexes remains elusive from such studies. Here we investigate how the yeast two-hybrid system can be used to analyze direct interactions among proteins in a complex. First we tested all pairwise interactions among the seven proteins of Escherichia coli DNA polymerase III as well as an uncharacterized complex that includes MntR and PerR. Four and seven interactions were identified in these two complexes, respectively. In addition, we review Y2H data for three other complexes of known structure which serve as "gold-standards", namely Varicella Zoster Virus (VZV) ribonucleotide reductase (RNR), the yeast proteasome, and bacteriophage lambda. Finally, we review an Y2H analysis of the human spliceosome which may serve as an example for a dynamic mega-complex.

Systematic analysis of dimeric E3-RING interactions reveals increased combinatorial complexity in human ubiquitination networks

Ubiquitination controls the stability or function of many human proteins, thereby regulating a wide range of physiological processes. In most cases the combinatorial pattern of protein interactions that facilitate substrate recognition or modification remain unclear. Moreover, the efficiency of ubiquitination reactions can be altered by the formation of homo- and heterotypic E3-RING complexes. To establish the prevalence and nature of binary E3-RING/E3-RING interactions systematic yeast two-hybrid screens were performed to test 7269 potential interactions between 124 human E3-RING proteins. These studies identified 228 dimeric interactions between 100 E3-RINGs, of which 205 were novel. Complementary co-immunoprecipitation studies were performed to test predicted network interactions, showing a high correlation (64%) with primary yeast two-hybrid data. This data was integrated with known E3-RING interactions, tissue expression profiles and proteomic ubiquitination datasets to facilitate identification of subnetworks in which E3-RING dimerization events have the potential to alter network structure. These results reveal a widespread yet selective pattern of E3-RING dimerization events, which have the potential to confer further combinatorial complexity within human ubiquitination cascades.

Analysis of protein-protein interactions using high-throughput yeast two-hybrid screens

The yeast two-hybrid (Y2H) system is a powerful tool to identify binary protein-protein interactions. Here, we describe array-based two-hybrid methods that use defined libraries of open reading frames (ORFs) and pooled prey library screenings that use random genomic or cDNA libraries. The array-based Y2H system is well-suited for interactome studies of existing ORFeomes or subsets thereof, preferentially in a recombination-based cloning system. Array-based Y2H screens efficiently reduce false positives by using built-in controls, retesting, and evaluation of background activation. Hands-on time and the amount of used resources grow exponentially with the number of tested proteins; this is a disadvantage for large genome sizes. For large genomes, random library screen may be more efficient in terms of time and resources, but not as comprehensive as array screens, and it requires significant sequencing capacity. Furthermore, multiple variants of the Y2H vector systems detect markedly different subsets of interactions in the same interactome. Hence, only multiple variations of the Y2H systems ensure comprehensive coverage of an interactome.

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.

Matrix-based yeast two-hybrid screen strategies and comparison of systems

Today, matrix-based screens are used primarily for smaller and medium-size clone collections in combination with automation and cloning techniques that allow for reliable and fast interaction screening. Matrix-based yeast two-hybrid screens are an alternative to library-based screens. However, intermediary forms are possible too and we compare both strategies, including a detailed discussion of matrix-based screens. Recent improvement of matrix screens (also called array screens) uses various pooling strategies as well as novel vectors that increase their efficiency while decreasing false-negative rates and increasing reliability.

The TOPLESS interactome: a framework for gene repression in Arabidopsis

Transcription factors activate or repress target gene expression or switch between activation and repression. In animals and yeast, Groucho/Tup1 corepressor proteins are recruited by diverse transcription factors to induce context-specific transcriptional repression. Two groups of Groucho/Tup1-like corepressors have been described in plants. LEUNIG and LEUNIG_HOMOLOG constitute one group and TOPLESS (TPL) and the four TPL-related (TPR) corepressors form the other. To discover the processes in which TPL and the TPR corepressors operate, high-throughput yeast two-hybrid approaches were used to identify interacting proteins. We found that TPL/TPR corepressors predominantly interact directly with specific transcription factors, many of which were previously implicated in transcriptional repression. The interacting transcription factors reveal that the TPL/TPR family has been coopted multiple times to modulate gene expression in diverse processes, including hormone signaling, stress responses, and the control of flowering time, for which we also show biological validation. The interaction data suggest novel mechanisms for the involvement of TPL/TPR corepressors in auxin and jasmonic acid signaling. A number of short repression domain (RD) sequences have previously been identified in Arabidopsis (Arabidopsis thaliana) transcription factors. All known RD sequences were enriched among the TPL/TPR interactors, and novel TPL-RD interactions were identified. We show that the presence of RD sequences is essential for TPL/TPR recruitment. These data provide a framework for TPL/TPR-dependent transcriptional repression. They allow for predictions about new repressive transcription factors, corepressor interactions, and repression mechanisms and identify a wide range of plant processes that utilize TPL/TPR-mediated gene repression.

Enhanced yeast one-hybrid assays for high-throughput gene-centered regulatory network mapping

A major challenge in systems biology is to understand the gene regulatory networks that drive development, physiology and pathology. Interactions between transcription factors and regulatory genomic regions provide the first level of gene control. Gateway-compatible yeast one-hybrid (Y1H) assays present a convenient method to identify and characterize the repertoire of transcription factors that can bind a DNA sequence of interest. To delineate genome-scale regulatory networks, however, large sets of DNA fragments need to be processed at high throughput and high coverage. Here we present enhanced Y1H (eY1H) assays that use a robotic mating platform with a set of improved Y1H reagents and automated readout quantification. We demonstrate that eY1H assays provide excellent coverage and identify interacting transcription factors for multiple DNA fragments in a short time. eY1H assays will be an important tool for mapping gene regulatory networks in Caenorhabditis elegans and other model organisms as well as in humans.

An integrated approach to elucidate the intra-viral and viral-cellular protein interaction networks of a gamma-herpesvirus

Genome-wide yeast two-hybrid (Y2H) screens were conducted to elucidate the molecular functions of open reading frames (ORFs) encoded by murine γ-herpesvirus 68 (MHV-68). A library of 84 MHV-68 genes and gene fragments was generated in a Gateway entry plasmid and transferred to Y2H vectors. All possible pair-wise interactions between viral proteins were tested in the Y2H assay, resulting in the identification of 23 intra-viral protein-protein interactions (PPIs). Seventy percent of the interactions between viral proteins were confirmed by co-immunoprecipitation experiments. To systematically investigate virus-cellular protein interactions, the MHV-68 Y2H constructs were screened against a cellular cDNA library, yielding 243 viral-cellular PPIs involving 197 distinct cellar proteins. Network analyses indicated that cellular proteins targeted by MHV-68 had more partners in the cellular PPI network and were located closer to each other than expected by chance. Taking advantage of this observation, we scored the cellular proteins based on their network distances from other MHV-68-interacting proteins and segregated them into high (Y2H-HP) and low priority/not-scored (Y2H-LP/NS) groups. Significantly more genes from Y2H-HP altered MHV-68 replication when their expression was inhibited with siRNAs (53% of genes from Y2H-HP, 21% of genes from Y2H-LP/NS, and 16% of genes randomly chosen from the human PPI network; p<0.05). Enriched Gene Ontology (GO) terms in the Y2H-HP group included regulation of apoptosis, protein kinase cascade, post-translational protein modification, transcription from RNA polymerase II promoter, and IκB kinase/NFκB cascade. Functional validation assays indicated that PCBP1, which interacted with MHV-68 ORF34, may be involved in regulating late virus gene expression in a manner consistent with the effects of its viral interacting partner. Our study integrated Y2H screening with multiple functional validation approaches to create γ-herpes viral-viral and viral-cellular protein interaction networks.

Persistent infections by the herpesviruses Epstein Barr virus (EBV) and Kaposi's sarcoma herpesvirus (KSHV) are associated with tumor formation. To better understand how these and other related viruses interact with their host cells to promote virus replication and cause disease, we studied murine gamma-herpesvirus 68 (MHV-68). MHV-68 belongs to the same group of herpesviruses as EBV and KSHV, but has the advantage of being able to replicate efficiently in cell culture. Our study used genome-wide screens to identify 23 protein-protein interactions between the 80 MHV-68 proteins. Several of these interactions are likely to be important for assembling new viruses. We also discovered 243 interactions between MHV-68 and cellular proteins. To help prioritize cellular proteins for follow up studies, we developed a new computational tool to analyze our data. Proteins with high priority scores were more likely to affect viral replication than low priority proteins. Among the cellular proteins that had the greatest effect on MHV-68 replication was PCBP1, which negatively regulated MHV-68 late gene expression. This study identified many novel cellular proteins involved in MHV-68 replication and established a method to identify important proteins from high-throughput virus-cellular protein-protein interaction data sets.

The protein interaction map of bacteriophage lambda

Background

Bacteriophage lambda is a model phage for most other dsDNA phages and has been studied for over 60 years. Although it is probably the best-characterized phage there are still about 20 poorly understood open reading frames in its 48-kb genome. For a complete understanding we need to know all interactions among its proteins. We have manually curated the lambda literature and compiled a total of 33 interactions that have been found among lambda proteins. We set out to find out how many protein-protein interactions remain to be found in this phage.

Results

In order to map lambda's interactions, we have cloned 68 out of 73 lambda open reading frames (the "ORFeome") into Gateway vectors and systematically tested all proteins for interactions using exhaustive array-based yeast two-hybrid screens. These screens identified 97 interactions. We found 16 out of 30 previously published interactions (53%). We have also found at least 18 new plausible interactions among functionally related proteins. All previously found and new interactions are combined into structural and network models of phage lambda.

Conclusions

Phage lambda serves as a benchmark for future studies of protein interactions among phage, viruses in general, or large protein assemblies. We conclude that we could not find all the known interactions because they require chaperones, post-translational modifications, or multiple proteins for their interactions. The lambda protein network connects 12 proteins of unknown function with well characterized proteins, which should shed light on the functional associations of these uncharacterized proteins.

Categorizing biases in high-confidence high-throughput protein-protein interaction data sets

We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not necessarily imply that one detection methodology was better or worse, but rather that, to a large extent, the insights reflected the methodological biases themselves. Consequently, interpreting the protein interaction data within their experimental or cellular context provided the best avenue for overcoming biases and inferring biological knowledge.

The proteome and interactome of Streptococcus pneumoniae phage Cp-1

Mass spectrometry analysis of Streptococcus pneumoniae bacteriophage Cp-1 identified a total of 12 proteins, and proteome-wide yeast two-hybrid screens revealed 17 binary interactions mainly among these structural proteins. On the basis of the resulting linkage map, we suggest an improved structural model of the Cp-1 virion.

The Escherichia coli K-12 ORFeome: a resource for comparative molecular microbiology

Background

Systems biology and functional genomics require genome-wide datasets and resources. Complete sets of cloned open reading frames (ORFs) have been made for about a dozen bacterial species and allow researchers to express and study complete proteomes in a high-throughput fashion.

Results

We have constructed an open reading frame (ORFeome) collection of 3974 or 94% of the known Escherichia coli K-12 ORFs in Gateway^® entry vector pENTR/Zeo. The collection has been used for protein expression and protein interaction studies. For example, we have compared interactions among YgjD, YjeE and YeaZ proteins in E. coli, Streptococcus pneumoniae, and Staphylococcus aureus. We also compare this ORFeome with other Gateway-compatible bacterial ORFeomes and show its utility for comparative functional genomics.

Conclusions

The E. coli ORFeome provides a useful resource for functional genomics and other areas of protein research in a highly flexible format. Our comparison with other ORFeomes makes comparative analyses straighforward and facilitates direct comparisons of many proteins across many genomes.

Global approaches to study protein-protein interactions among viruses and hosts

While high-throughput protein-protein interaction screens were first published approximately 10 years ago, systematic attempts to map interactions among viruses and hosts started only a few years ago. HIV-human interactions dominate host-pathogen interaction databases (with approximately 2000 interactions) despite the fact that probably none of these interactions have been identified in systematic interaction screens. Recently, combinations of protein interaction data with RNAi and other functional genomics data allowed researchers to model more complex interaction networks. The rapid progress in this area promises a flood of new data in the near future, with clinical applications as soon as structural and functional genomics catches up with next-generation sequencing of human variation and structure-based drug design.

Improving the yeast two-hybrid system with permutated fusions proteins: the Varicella Zoster Virus interactome

Background

Yeast two-hybrid (Y2H) screens have been among the most powerful methods to detect and analyze protein-protein interactions. However, they suffer from a significant degree of false negatives, i.e. true interactions that are not detected, and to a certain degree from false positives, i.e. interactions that appear to take place only in the context of the Y2H assay. While the fraction of false positives remains difficult to estimate, the fraction of false negatives in typical Y2H screens is on the order of 70-90%. Here we present novel Y2H vectors that significantly decrease the number of false negatives and help to mitigate the false positive problem.

Results

We have constructed two new vectors (pGBKCg and pGADCg) that allow us to make both C-terminal fusion proteins of DNA-binding and activation domains. Both vectors can be combined with existing vectors for N-terminal fusions and thus allow four different bait-prey combinations: NN, CC, NC, and CN. We have tested all ~4,900 pairwise combinations of the 70 Varicella-Zoster-Virus (VZV) proteins for interactions, using all possible combinations. About ~20,000 individual Y2H tests resulted in 182 NN, 89 NC, 149 CN, and 144 CC interactions. Overlap between screens ranged from 17% (NC-CN) to 43% (CN-CC). Performing four screens (i.e. permutations) instead of one resulted in about twice as many interactions and thus much fewer false negatives. In addition, interactions that are found in multiple combinations confirm each other and thus provide a quality score. This study is the first systematic analysis of such N- and C-terminal Y2H vectors.

Conclusions

Permutations of C- and N-terminal Y2H vectors dramatically increase the coverage of interactome studies and thus significantly reduce the number of false negatives. We suggest that future interaction screens should use such vector combinations on a routine basis, not the least because they provide a built-in quality score for Y2H interactions that can provide a measure of reproducibility without additional assays.