Development of Ultra-High-Density Screening Tools for Microbial “Omics”

High-throughput genetic screens in model microbial organisms are a primary means of interrogating biological systems. In numerous cases, such screens have identified the genes that underlie a particular phenotype or a set of gene-gene, gene-environment or protein-protein interactions, which are then used to construct highly informative network maps for biological research. However, the potential test space of genes, proteins, or interactions is typically much larger than current screening systems can address. To push the limits of screening technology, we developed an ultra-high-density, 6144-colony arraying system and analysis toolbox. Using budding yeast as a benchmark, we find that these tools boost genetic screening throughput 4-fold and yield significant cost and time reductions at quality levels equal to or better than current methods. Thus, the new ultra-high-density screening tools enable researchers to significantly increase the size and scope of their genetic screens.     

Toward an integrated map of genetic interactions in cancer cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype‐dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene‐background relationships. In addition to known dependencies, we identified new genotype‐specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β‐catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.     

Where have all the interactions gone? Estimating the coverage of two-hybrid protein interaction maps

Yeast two-hybrid screens are an important method for mapping pairwise physical interactions between proteins. The fraction of interactions detected in independent screens can be very small, and an outstanding challenge is to determine the reason for the low overlap. Low overlap can arise from either a high false-discovery rate (interaction sets have low overlap because each set is contaminated by a large number of stochastic false-positive interactions) or a high false-negative rate (interaction sets have low overlap because each misses many true interactions). We extend capture-recapture theory to provide the first unified model for false-positive and false-negative rates for two-hybrid screens. Analysis of yeast, worm, and fly data indicates that 25% to 45% of the reported interactions are likely false positives. Membrane proteins have higher false-discovery rates on average, and signal transduction proteins have lower rates. The overall false-negative rate ranges from 75% for worm to 90% for fly, which arises from a roughly 50% false-negative rate due to statistical undersampling and a 55% to 85% false-negative rate due to proteins that appear to be systematically lost from the assays. Finally, statistical model selection conclusively rejects the Erd?s-Rényi network model in favor of the power law model for yeast and the truncated power law for worm and fly degree distributions. Much as genome sequencing coverage estimates were essential for planning the human genome sequencing project, the coverage estimates developed here will be valuable for guiding future proteomic screens. All software and datasets are available in and , -, and -, and are also available from our Web site, http://www.baderzone.org.     

A human protein-protein interaction network: a resource for annotating the proteome

Protein-protein interaction maps provide a valuable framework for a better understanding of the functional organization of the proteome. To detect interacting pairs of human proteins systematically, a protein matrix of 4456 baits and 5632 preys was screened by automated yeast two-hybrid (Y2H) interaction mating. We identified 3186 mostly novel interactions among 1705 proteins, resulting in a large, highly connected network. Independent pull-down and co-immunoprecipitation assays validated the overall quality of the Y2H interactions. Using topological and GO criteria, a scoring system was developed to define 911 high-confidence interactions among 401 proteins. Furthermore, the network was searched for interactions linking uncharacterized gene products and human disease proteins to regulatory cellular pathways. Two novel Axin-1 interactions were validated experimentally, characterizing ANP32A and CRMP1 as modulators of Wnt signaling. Systematic human protein interaction screens can lead to a more comprehensive understanding of protein function and cellular processes.     

Large-scale protein-protein interaction analysis in Arabidopsis mesophyll protoplasts by split firefly luciferase complementation

Protein-protein interactions (PPIs) constitute the regulatory network that coordinates diverse cellular functions. There are growing needs in plant research for creating protein interaction maps behind complex cellular processes and at a systems biology level. However, only a few approaches have been successfully used for large-scale surveys of PPIs in plants, each having advantages and disadvantages. Here we present split firefly luciferase complementation (SFLC) as a highly sensitive and noninvasive technique for in planta PPI investigation. In this assay, the separate halves of a firefly luciferase can come into close proximity and transiently restore its catalytic activity only when their fusion partners, namely the two proteins of interest, interact with each other. This assay was conferred with quantitativeness and high throughput potential when the Arabidopsis mesophyll protoplast system and a microplate luminometer were employed for protein expression and luciferase measurement, respectively. Using the SFLC assay, we could monitor the dynamics of rapamycin-induced and ascomycin-disrupted interaction between Arabidopsis FRB and human FKBP proteins in a near real-time manner. As a proof of concept for large-scale PPI survey, we further applied the SFLC assay to testing 132 binary PPIs among 8 auxin response factors (ARFs) and 12 Aux/IAA proteins from Arabidopsis. Our results demonstrated that the SFLC assay is ideal for in vivo quantitative PPI analysis in plant cells and is particularly powerful for large-scale binary PPI screens.     

Focusing-in on microtubules

A good approximation of the atomic structure of a microtubule has been derived from docking the high-resolution structure of tubulin, solved by electron crystallography, into lower resolution maps of whole microtubules. Some structural interactions with other molecules, including nucleotides, drugs, motor proteins and microtubule-associated proteins, can now be predicted.     

Chemogenomics and biotechnology

A robust knowledge of the interactions between small molecules and specific proteins aids the development of new biotechnological tools and the identification of new drug targets, and can lead to specific biological insights. Such knowledge can be obtained through chemogenomic screens. In these screens, each small molecule from a chemical library is applied to each cell type from a library of cells, and the resulting phenotypes are recorded. Chemogenomic screens have recently become very common and will continue to generate large amounts of data. The interpretation of this data will occupy biologists and chemists alike for some time to come. This review discusses methods for the acquisition and interpretation of chemogenomic data, in addition to possible applications of chemogenomics in biotechnology.     

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.     

Detection and identification of plasma proteins that bind GlialCAM using ProteinChip arrays, SELDI-TOF MS, and nano-LC MS/MS

In order to fully understand biological processes it is essential to identify interactions in protein complexes. There are several techniques available to study this type of interactions, such as yeast two-hybrid screens, affinity chromatography, and coimmunoprecipitation. We propose a novel strategy to identify protein-protein interactions, comprised of first detecting the interactions using ProteinChips and SELDI-TOF MS, followed by the isolation of the interacting proteins through affinity beads and RP-HPLC and finally identifying the proteins using nano-LC MS/MS. The advantages of this new strategy are that the primary high-throughput screening of samples can be performed with small amounts of sample, no specific antibody is needed and the proteins represented on the SELDI-TOF MS spectra can be identified with high confidence. Furthermore, the method is faster and less labor-intensive than other current approaches. Using this novel method, we isolated and identified the interactions of two mouse plasma proteins, mannose binding lectin C and properdin, with GlialCAM, a type 1 transmembrane glycoprotein that belongs to the Ig superfamily.     

Combinations of protein-chemical complex structures reveal new targets for established drugs

Biological networks are powerful tools for predicting undocumented relationships between molecules. The underlying principle is that existing interactions between molecules can be used to predict new interactions. Here we use this principle to suggest new protein-chemical interactions via the network derived from three-dimensional structures. For pairs of proteins sharing a common ligand, we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other. The method reproduces 84% of complexes in a benchmark, and we make many predictions that would not be possible using conventional modeling techniques. Within 19,578 novel predicted interactions are 7,793 involving 718 drugs, including filaminast, coumarin, alitretonin and erlotinib. The growth rate of confident predictions is twice that of experimental complexes, meaning that a complete structural drug-protein repertoire will be available at least ten years earlier than by X-ray and NMR techniques alone.     

A proteome-wide protein interaction map for Campylobacter jejuni

Background

Data from large-scale protein interaction screens for humans and model eukaryotes have been invaluable for developing systems-level models of biological processes. Despite this value, only a limited amount of interaction data is available for prokaryotes. Here we report the systematic identification of protein interactions for the bacterium Campylobacter jejuni, a food-borne pathogen and a major cause of gastroenteritis worldwide.

Results

Using high-throughput yeast two-hybrid screens we detected and reproduced 11,687 interactions. The resulting interaction map includes 80% of the predicted C. jejuni NCTC11168 proteins and places a large number of poorly characterized proteins into networks that provide initial clues about their functions. We used the map to identify a number of conserved subnetworks by comparison to protein networks from Escherichia coli and Saccharomyces cerevisiae. We also demonstrate the value of the interactome data for mapping biological pathways by identifying the C. jejuni chemotaxis pathway. Finally, the interaction map also includes a large subnetwork of putative essential genes that may be used to identify potential new antimicrobial drug targets for C. jejuni and related organisms.

Conclusion

The C. jejuni protein interaction map is one of the most comprehensive yet determined for a free-living organism and nearly doubles the binary interactions available for the prokaryotic kingdom. This high level of coverage facilitates pathway mapping and function prediction for a large number of C. jejuni proteins as well as orthologous proteins from other organisms. The broad coverage also facilitates cross-species comparisons for the identification of evolutionarily conserved subnetworks of protein interactions.     

Two-hybrid reporter vectors for gap repair cloning

Yeast two-hybrid analysis is a valuable approach to the discovery and characterization of protein interactions. We have developed vectors that can indicate the presence of an insert when used in two-hybrid bait and prey construction by gap repair cloning. The strategy uses a recombination cloning site flanked by sequences encoding the GAL4 activation and binding domains. After gap repair cloning in standard hosts carrying an ADE2 reporter gene, disruption of GAL4 by an insert can be identified by the development of red colony color, while empty vector plasmids produce white colonies. Function in yeast two-hybrid applications was initially validated using known interacting proteins in pair-wise analyses, and subsequently, the bait vectors were used in library screens with the mouse Mad212 and human Mccd1 proteins, identifying a number of putative new interactions for these proteins. These vectors should facilitate high-throughput yeast two-hybrid screens in which large numbers of bait and prey constructs may be required.     

A fluorescent two-hybrid assay for direct visualization of protein interactions in living cells

Genetic high throughput screens have yielded large sets of potential protein-protein interactions now to be verified and further investigated. Here we present a simple assay to directly visualize protein-protein interactions in single living cells. Using a modified lac repressor system, we tethered a fluorescent bait at a chromosomal lac operator array and assayed for co-localization of fluorescent prey fusion proteins. With this fluorescent two-hybrid assay we successfully investigated the interaction of proteins from different subcellular compartments including nucleus, cytoplasm, and mitochondria. In combination with an S phase marker we also studied the cell cycle dependence of protein-protein interactions. These results indicate that the fluorescent two-hybrid assay is a powerful tool to investigate protein-protein interactions within their cellular environment and to monitor the response to external stimuli in real time.     

Systematic interactome mapping and genetic perturbation analysis of a C. elegans TGF-beta signaling network

To initiate a system-level analysis of C. elegans DAF-7/TGF-beta signaling, we combined interactome mapping with single and double genetic perturbations. Yeast two-hybrid (Y2H) screens starting with known DAF-7/TGF-beta pathway components defined a network of 71 interactions among 59 proteins. Coaffinity purification (co-AP) assays in mammalian cells confirmed the overall quality of this network. Systematic perturbations of the network using RNAi, both in wild-type and daf-7/TGF-beta pathway mutant animals, identified nine DAF-7/TGF-beta signaling modifiers, seven of which are conserved in humans. We show that one of these has functional homology to human SNO/SKI oncoproteins and that mutations at the corresponding genetic locus daf-5 confer defects in DAF-7/TGF-beta signaling. Our results reveal substantial molecular complexity in DAF-7/TGF-beta signal transduction. Integrating interactome maps with systematic genetic perturbations may be useful for developing a systems biology approach to this and other signaling modules.     

Molecular and cellular approaches for the detection of protein-protein interactions: latest techniques and current limitations

Homotypic and heterotypic protein interactions are crucial for all levels of cellular function, including architecture, regulation, metabolism, and signaling. Therefore, protein interaction maps represent essential components of post-genomic toolkits needed for understanding biological processes at a systems level. Over the past decade, a wide variety of methods have been developed to detect, analyze, and quantify protein interactions, including surface plasmon resonance spectroscopy, NMR, yeast two-hybrid screens, peptide tagging combined with mass spectrometry and fluorescence-based technologies. Fluorescence techniques range from co-localization of tags, which may be limited by the optical resolution of the microscope, to fluorescence resonance energy transfer-based methods that have molecular resolution and can also report on the dynamics and localization of the interactions within a cell. Proteins interact via highly evolved complementary surfaces with affinities that can vary over many orders of magnitude. Some of the techniques described in this review, such as surface plasmon resonance, provide detailed information on physical properties of these interactions, while others, such as two-hybrid techniques and mass spectrometry, are amenable to high-throughput analysis using robotics. In addition to providing an overview of these methods, this review emphasizes techniques that can be applied to determine interactions involving membrane proteins, including the split ubiquitin system and fluorescence-based technologies for characterizing hits obtained with high-throughput approaches. Mass spectrometry-based methods are covered by a review by Miernyk and Thelen (2008; this issue, pp. 597–609 ). In addition, we discuss the use of interaction data to construct interaction networks and as the basis for the exciting possibility of using to predict interaction surfaces.     

Baculovirus display for discovery of low-affinity extracellular receptor–ligand interactions using protein microarrays

When used in conjunction with multivalent protein probes, protein microarrays offer a robust technology for discovery of low-affinity extracellular protein–protein interactions. Probes for receptor-matching screens generally consist of purified extracellular domains fused to affinity tags. Given that approximately two-thirds of extracellular proteins are transmembrane domain-containing proteins, it would be desirable to develop a system to express and display probe receptors in a native-like membrane environment. Toward this end, we evaluated baculovirus display as a platform for generating multivalent probes for protein microarray screens. Virion particles were generated displaying single-transmembrane domain receptors BTLA, CD200, and EFNB2, representing a range of affinities for their interacting partners. Virions directly labeled with Cy5 fluorophore were screened against a microarray containing more than 600 extracellular proteins, and the results were compared with data derived from soluble Fc protein or probe-coated protein A microbeads. An optimized protocol employing a blocking step with a nonrelated probe-expressing control baculovirus allowed identification of the expected interactions with a signal-to-noise ratio similar to or higher than those obtained with the other formats. Our results demonstrate that baculovirus display is suitable for detection of high- and low-affinity extracellular protein–protein interactions on protein microarrays. This platform eliminates the need for protein purification and provides a native-like lipid environment for membrane-associated receptors.