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The cell cycle checkpoint kinase 2 (CHEK2) is a tumor suppressor gene coding for a protein kinase with a role in the cell cycle and DNA repair pathways. Variants within CHEK2 are associated with an increased risk of developing breast, colorectal, prostate and several other types of cancer. Comprehensive genetic risk assessment leads to early detection of hereditary cancer and provides an opportunity for better survival. Multigene panel screening can identify the presence of pathogenic variants in hereditary cancer predisposition genes (HCPG), including CHEK2. Multigene panels, however, also result in large quantities of genetic data some of which cannot be interpreted and are classified as variants of uncertain significance (VUS). A VUS provides no information for use in medical management and leads to ambiguity in genetic counseling. In the absence of variant segregation data, in vitro functional analyses can be used to clarify variant annotations, aiding in accurate clinical management of patient risk and treatment plans. In this study, we performed whole exome sequencing (WES) to investigate the prevalence of germline variants in 210 breast cancer (BC) patients and conspicuously among the many variants in HCPGs that we found, we identified 16 individuals with non-synonymous or frameshift CHEK2 variants, sometimes along with additional variants within other BC susceptibility genes. Using this data, we investigated the prevalence of these CHEK2 variants in African American (AA) and Caucasian (CA) populations identifying the presence of two novel frameshift variants, c.1350delA (p.Val451Serfs*18) and c.1528delC (p.Gln510Argfs*3) and a novel missense variant, c262C>T (p.Pro88Ser). Along with the current clinical classifications, we assembled available experimental data and computational predictions of function for these CHEK2 variants, as well as explored the role these variants may play in polygenic risk assessment., Competing Interests: Declaration of competing interest The authors declare that there are no conflicts of interest., (Copyright © 2024. Published by Elsevier Inc.)

Loss of function mutations in the checkpoint kinase gene CHEK2 are associated with increased risk of breast and other cancers. Most of the 3,188 unique amino acid changes that can result from non-synonymous single nucleotide variants (SNVs) of CHEK2, however, have not been tested for their impact on the function of the CHEK2-enocded protein (CHK2). One successful approach to testing the function of variants has been to test for their ability to complement mutations in the yeast ortholog of CHEK2, RAD53. This approach has been used to provide functional information on over 100 CHEK2 SNVs and the results align with functional assays in human cells and known pathogenicity. Here we tested all but two of the 4,887 possible SNVs in the CHEK2 open reading frame for their ability to complement RAD53 mutants using a high throughput technique of deep mutational scanning (DMS). Among the non-synonymous changes, 770 were damaging to protein function while 2,417 were tolerated. The results correlate well with previous structure and function data and provide a first or additional functional assay for all the variants of uncertain significance identified in clinical databases. Combined, this approach can be used to help predict the pathogenicity of CHEK2 variants of uncertain significance that are found in susceptibility screening and could be applied to other cancer risk genes., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 McCarthy-Leo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Campylobacter jejuni (C. jejuni) is a foodborne intestinal pathogen and major cause of gastroenteritis worldwide. C. jejuni proteins that are immunogenic have been sought for their potential use in the development of biomarkers, diagnostic assays, or subunit vaccines for humans or livestock. To identify new immunogenic C. jejuni proteins, we used a native protein microarray approach. A protein chip, with over 1400 individually purified GST-tagged C. jejuni proteins, representing over 86% of the proteome, was constructed to screen for antibody titers present in test sera raised against whole C. jejuni cells. Dual detection of GST signals was incorporated as a way of normalizing the variation of protein concentrations contributing to the antibody staining intensities. We detected strong signals to 102 C. jejuni antigens. In addition to antigens recognized by antiserum raised against C. jejuni, parallel experiments were conducted to identify antigens cross-reactive to antiserum raised against various serotypes of E. coli or Salmonella or to healthy human sera. This led to the identification of 34 antigens specifically recognized by the C. jejuni antiserum, only four of which were previously known. The chip approach also allowed identification of conformational antigens. We demonstrate in the case of Cj1621 that antigen signals are lost to denaturing conditions commonly used in other approaches to identify immunogens. Antigens identified in this study include those possessing sequence features indicative of cell surface localization, as well as those that do not. Together, our results indicate that the unbiased chip-based screen can help reveal the full repertoire of host antibodies against microbial proteomes., Competing Interests: The authors have declared that no competing interests exist. JL is currently employed by Idexx Laboratories, which played no role in this study.

The functions of roughly a third of all proteins in Streptococcus pneumoniae , a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein's function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae . We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae , the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins.

The gene encoding endoplasmic reticulum (ER) lipid raft-associated protein 2 (ERLIN2) is amplified in human breast cancers. ERLIN2 gene mutations were also found to be associated with human childhood progressive motor neuron diseases. Yet, an understanding of the physiological function and mechanism for ERLIN2 remains elusive. In this study, we reveal that ERLIN2 is a spatially and temporally regulated ER-microtubule-binding protein that has an important role in cell cycle progression by interacting with and stabilizing the mitosis-promoting factors. Whereas ERLIN2 is highly expressed in aggressive human breast cancers, during normal development ERLIN2 is expressed at the postnatal stage and becomes undetectable in adulthood. ERLIN2 interacts with the microtubule component α-tubulin, and this interaction is maximal during the cell cycle G2/M phase where ERLIN2 simultaneously interacts with the mitosis-promoting complex Cyclin B1/Cdk1. ERLIN2 facilitates K63-linked ubiquitination and stabilization of Cyclin B1 protein in G2/M phase. Downregulation of ERLIN2 results in cell cycle arrest, represses breast cancer proliferation and malignancy and increases sensitivity of breast cancer cells to anticancer drugs. In summary, our study revealed a novel ER-microtubule-binding protein, ERLIN2, which interacts with and stabilizes mitosis-promoting factors to regulate cell cycle progression associated with human breast cancer malignancy.

Cyclin J (CycJ) is a poorly characterized member of the Cyclin superfamily of cyclin-dependent kinase regulators, many of which regulate the cell cycle or transcription. Although CycJ is conserved in metazoans its cellular function has not been identified and no mutant defects have been described. In Drosophila, CycJ transcript is present primarily in ovaries and very early embryos, suggesting a role in one or both of these tissues. The CycJ gene (CycJ) lies immediately downstream of armitage (armi), a gene involved in the Piwi-associated RNA (piRNA) pathways that are required for silencing transposons in the germline and adjacent somatic cells. Mutations in armi result in oogenesis defects but a role for CycJ in oogenesis has not been defined. Here we assessed oogenesis in CycJ mutants in the presence or absence of mutations in armi or other piRNA pathway genes. CycJ null ovaries appeared normal, indicating that CycJ is not essential for oogenesis under normal conditions. In contrast, armi null ovaries produced only two egg chambers per ovariole and the eggs had severe axis specification defects, as observed previously for armi and other piRNA pathway mutants. Surprisingly, the CycJ armi double mutant failed to produce any mature eggs. The double null ovaries generally had only one egg chamber per ovariole and the egg chambers frequently contained an overabundance of differentiated germline cells. Production of these compound egg chambers could be suppressed with CycJ transgenes but not with mutations in the checkpoint gene mnk, which suppress oogenesis defects in armi mutants. The CycJ null showed similar genetic interactions with the germline and somatic piRNA pathway gene piwi, and to a lesser extent with aubergine (aub), a member of the germline-specific piRNA pathway. The strong genetic interactions between CycJ and piRNA pathway genes reveal a role for CycJ in early oogenesis. Our results suggest that CycJ is required to regulate egg chamber production or maturation when piRNA pathways are compromised., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Background: Networks of interacting genes and gene products mediate most cellular and developmental processes. High throughput screening methods combined with literature curation are identifying many of the protein-protein interactions (PPI) and protein-DNA interactions (PDI) that constitute these networks. Most of the detection methods, however, fail to identify the in vivo spatial or temporal context of the interactions. Thus, the interaction data are a composite of the individual networks that may operate in specific tissues or developmental stages. Genome-wide expression data may be useful for filtering interaction data to identify the subnetworks that operate in specific spatial or temporal contexts. Here we take advantage of the extensive interaction and expression data available for Drosophila to analyze how interaction networks may be unique to specific tissues and developmental stages., Results: We ranked genes on a scale from ubiquitously expressed to tissue or stage specific and examined their interaction patterns. Interestingly, ubiquitously expressed genes have many more interactions among themselves than do non-ubiquitously expressed genes both in PPI and PDI networks. While the PDI network is enriched for interactions between tissue-specific transcription factors and their tissue-specific targets, a preponderance of the PDI interactions are between ubiquitous and non-ubiquitously expressed genes and proteins. In contrast to PDI, PPI networks are depleted for interactions among tissue- or stage- specific proteins, which instead interact primarily with widely expressed proteins. In light of these findings, we present an approach to filter interaction data based on gene expression levels normalized across tissues or developmental stages. We show that this filter (the percent maximum or pmax filter) can be used to identify subnetworks that function within individual tissues or developmental stages., Conclusions: These observations suggest that protein networks are frequently organized into hubs of widely expressed proteins to which are attached various tissue- or stage-specific proteins. This is consistent with earlier analyses of human PPI data and suggests a similar organization of interaction networks across species. This organization implies that tissue or stage specific networks can be best identified from interactome data by using filters designed to include both ubiquitously expressed and specifically expressed genes and proteins.

The four divergent serotypes of dengue virus are the causative agents of dengue fever, dengue hemorrhagic fever and dengue shock syndrome. About two-fifths of the world's population live in areas where dengue is prevalent, and thousands of deaths are caused by the viruses every year. Dengue virus is transmitted from one person to another primarily by the yellow fever mosquito, Aedes aegypti. Recent studies have begun to define how the dengue viral proteins interact with host proteins to mediate viral replication and pathogenesis. A combined analysis of these studies, however, suggests that many virus-host protein interactions remain to be identified, especially for the mosquito host. In this study, we used high-throughput yeast two-hybrid screening to identify mosquito and human proteins that physically interact with dengue proteins. We tested each identified host protein against the proteins from all four serotypes of dengue to identify interactions that are conserved across serotypes. We further confirmed many of the interactions using co-affinity purification assays. As in other large-scale screens, we identified some previously detected interactions and many new ones, moving us closer to a complete host - dengue protein interactome. To help summarize and prioritize the data for further study, we combined our interactions with other published data and identified a subset of the host-dengue interactions that are now supported by multiple forms of evidence. These data should be useful for understanding the interplay between dengue and its hosts and may provide candidates for drug targets and vector control strategies.

Many emerging database applications entail sophisticated graph-based query manipulation, predominantly evident in large-scale scientific applications. To access the information embedded in graphs, efficient graph matching tools and algorithms have become of prime importance. Although the prohibitively expensive time complexity associated with exact subgraph isomorphism techniques has limited its efficacy in the application domain, approximate yet efficient graph matching techniques have received much attention due to their pragmatic applicability. Since public domain databases are noisy and incomplete in nature, inexact graph matching techniques have proven to be more promising in terms of inferring knowledge from numerous structural data repositories. In this paper, we propose a novel technique called TraM for approximate graph matching that off-loads a significant amount of its processing on to the database making the approach viable for large graphs. Moreover, the vector space embedding of the graphs and efficient filtration of the search space enables computation of approximate graph similarity at a throw-away cost. We annotate nodes of the query graphs by means of their global topological properties and compare them with neighborhood biased segments of the datagraph for proper matches. We have conducted experiments on several real data sets, and have demonstrated the effectiveness and efficiency of the proposed method

Screens for protein-protein interactions using assays like the yeast two-hybrid system have generated volumes of useful data. The protein interactions from these screens have been used to develop a better understanding of the functions of individual proteins, regulatory pathways, molecular machines, and entire biological systems. The value of this data, however, is limited by the inherent frequency of false positives that arise in most protein interaction screens. Appreciable numbers of false positives can crop up in both low-throughput and high-throughput screens, and even in screens that employ stringent criteria for defining a positive. A number of classification systems have been used to help distinguish false positives from biologically relevant true positives. This chapter describes a system for assigning a confidence score to each interaction based on the probability that it is a true positive. Such confidence scores can be used to prioritize interactions for validation. The scores are also useful for network analysis methods that take advantage of probabilistic edge weights. The scoring method does not rely on gold standard datasets of reliable true positives and true negatives, and thus circumvents the challenges associated with obtaining such datasets. Moreover, the scoring method uses data features that are largely assay-independent, making it useful for interactions obtained from a variety of different technologies and screening methods.

Charting the interactions among proteins is essential for understanding biological processes. While a number of complementary technologies for detecting protein interactions are available, the yeast two-hybrid system is one of the few that have been successfully scaled up. Two-hybrid screens have been used to construct extensive protein interaction maps for humans and several model organisms, and these maps have proven invaluable for studies on a variety of biological systems. These maps, however, have not come close to covering all proteins or interactions detectable by yeast two-hybrid. This is due in part to the difficulty of using library screening methods to sample all possible binary combinations of proteins. Ideally, every binary pair of proteins would be tested individually to ensure that every detectable interaction is identified. For organisms with large proteomes, however, this is not economically feasible and instead efficient pooling schemes must be implemented. The high-throughput two-hybrid screening methods presented here are designed to efficiently maximize coverage for selected sets of proteins or entire proteomes. We present two high-throughput screening protocols. Both methods are designed to identify interactors for any number of bait proteins expressed as DNA-binding domain (BD) fusions. The choice of which protocol to use depends largely on the nature of the available library of proteins fused to an activation domain (AD). The first protocol is appropriate for screening a library of AD clones, such as a cDNA library, a domain library, or a large pool of AD clones. By contrast, the second protocol is appropriate for screening a large array of individual sequence-verified AD clones. This protocol screens small pools of AD clones from the array in a two-phase scheme. Although the methods presented were developed using the LexA version of the yeast two-hybrid system, we include notes as appropriate to accommodate users of other versions.

The yeast two-hybrid method (or interaction trap) is a powerful technique for detecting protein interactions. The procedure is performed using transcriptional activation of a dual reporter system in yeast to identify interactions between a protein of interest (the bait protein) and the candidate proteins for interaction. The method can be used to screen a protein library for interactions with a bait protein or to test for association between proteins that are expected to interact based on prior evidence. Interaction mating facilitates the screening of a library with multiple bait proteins., (© 2011 by John Wiley & Sons, Inc.)

CARP-1/CCAR1, a perinuclear phosphoprotein, is a regulator of cell growth and apoptosis signaling. Although CARP-1 is a regulator of chemotherapy-dependent apoptosis, it is also a part of the NF-κB proteome and a co-activator of steroid/thyroid nuclear receptors as well as β-catenin signaling. Our yeast two-hybrid screen revealed CARP-1 binding with the anaphase-promoting complex/cyclosome E3 ubiquitin ligase component APC-2 protein. CARP-1 also binds with anaphase-promoting complex/cyclosome co-activators Cdc20 and Cdh1. Following mapping of the minimal epitopes involved in CARP-1 binding with APC-2, a fluorescence polarization assay was established that indicated a dissociation constant (K(d)) of 480 nm for CARP-1/APC-2 binding. Fluorescence polarization assay-based high throughput screening of a chemical library yielded several small molecule antagonists of CARP-1/APC-2 binding, termed CARP-1 functional mimetics. CFM-4 (1(2-chlorobenzyl)-5'-phenyl-3'H-spiro[indoline-3,2'-[1,3,4]thiadiazol]-2-one), a lead compound, binds with and stimulates CARP-1 expression. CFM-4 prevents CARP-1 binding with APC-2, causes G(2)M cell cycle arrest, and induces apoptosis with an IC(50) range of 10-15 μm. Apoptosis signaling by CFM-4 involves activation of caspase-8 and -9 and caspase-mediated ubiquitin-proteasome pathway-independent loss of cyclin B1 and Cdc20 proteins. Depletion of CARP-1, however, interferes with CFM-4-dependent cell growth inhibition, activation of caspases, and apoptosis. Because CFM-4 also suppresses growth of drug-resistant human breast cancer cells without affecting the growth of human breast epithelial MCF-10A cells, elevating CARP-1 by CFM-4 and consequent apoptosis could in principle be exploited to further elucidate, and perhaps effectively target, often deregulated cell cycle pathways in pathological conditions, including cancer.

Characterizing the extent and logic of signaling networks is essential to understanding specificity in such physiological and pathophysiological contexts as cell fate decisions and mechanisms of oncogenesis and resistance to chemotherapy. Cell-based RNA interference (RNAi) screens enable the inference of large numbers of genes that regulate signaling pathways, but these screens cannot provide network structure directly. We describe an integrated network around the canonical receptor tyrosine kinase (RTK)-Ras-extracellular signal-regulated kinase (ERK) signaling pathway, generated by combining parallel genome-wide RNAi screens with protein-protein interaction (PPI) mapping by tandem affinity purification-mass spectrometry. We found that only a small fraction of the total number of PPI or RNAi screen hits was isolated under all conditions tested and that most of these represented the known canonical pathway components, suggesting that much of the core canonical ERK pathway is known. Because most of the newly identified regulators are likely cell type- and RTK-specific, our analysis provides a resource for understanding how output through this clinically relevant pathway is regulated in different contexts. We report in vivo roles for several of the previously unknown regulators, including CG10289 and PpV, the Drosophila orthologs of two components of the serine/threonine-protein phosphatase 6 complex; the Drosophila ortholog of TepIV, a glycophosphatidylinositol-linked protein mutated in human cancers; CG6453, a noncatalytic subunit of glucosidase II; and Rtf1, a histone methyltransferase.

Background: Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both., Results: We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives. To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 (eIF3) complex, the COP9 signalosome, and the proteasome lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity, the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition., Conclusions: Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed between similar large-scale RNAi-based screens. Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process. Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.

The yeast two-hybrid method (or interaction trap) is a powerful technique for detecting protein interactions. The procedure is performed using transcriptional activation of a dual reporter system in yeast to identify interactions between a protein of interest (the bait protein) and the candidate proteins for interaction. The method can be used to screen a protein library for interactions with a bait protein or to test for association between proteins that are expected to interact based on prior evidence. Interaction mating facilitates the screening of a library with multiple bait proteins., (© 2011 by John Wiley & Sons, Inc.)

DroID (http://droidb.org/), the Drosophila Interactions Database, is a comprehensive public resource for Drosophila gene and protein interactions. DroID contains genetic interactions and experimentally detected protein-protein interactions curated from the literature and from external databases, and predicted protein interactions based on experiments in other species. Protein interactions are annotated with experimental details and periodically updated confidence scores. Data in DroID is accessible through user-friendly, intuitive interfaces that allow simple or advanced searches and graphical visualization of interaction networks. DroID has been expanded to include interaction types that enable more complete analyses of the genetic networks that underlie biological processes. In addition to protein-protein and genetic interactions, the database now includes transcription factor-gene and regulatory RNA-gene interactions. In addition, DroID now has more gene expression data that can be used to search and filter interaction networks. Orthologous gene mappings of Drosophila genes to other organisms are also available to facilitate finding interactions based on gene names and identifiers for a number of common model organisms and humans. Improvements have been made to the web and graphical interfaces to help biologists gain a comprehensive view of the interaction networks relevant to the genes and systems that they study.

Cyclin Y is one of the most highly conserved members of the cyclin superfamily of proteins, which are famous for their crucial roles in regulating the cell cycle and transcription. Despite this high degree of conservation, very little was known about Cyclin Y function prior to a handful of studies published in this past year. Cyclins typically function by activating cyclin-dependent kinases (Cdks) and one insight has come from the identification of a Cdk that is activated by Cyclin Y. Yeast two-hybrid data first linked Cyclin Y with Cdk14, known as Eip63E in Drosophila or PFTAIRE1 in vertebrates. In Drosophila, both Cyclin Y and Eip63E are essential at many stages of development, from embryogenesis to metamorphosis and null mutants show a similar spectrum of developmental defects. In cultured cells, Cyclin Y and Eip63E were shown to phosphorylate the Wg/Wnt co-receptor Arrow/LRP6 in a ligand-independent manner. Eip63E is recruited to LRP6 at the plasma membrane by interacting with Cyclin Y, which is tethered to the membrane through an N-terminal myristoylation. Cyclin Y-dependent LRP6 phosphorylation appears to prime the receptor for subsequent ligand-dependent phosphorylation and activation of the canonical Wnt signaling pathway. Interestingly, Wnt receptor phosphorylation and signaling is maximal in G₂/M when Cyclin Y is at its highest levels, suggesting that Cyclin Y may serve to entrain Wnt signaling to the cell cycle. Given the wide range of roles for Wnt signaling during development, these studies may help explain why Cyclin Y is required at several developmental stages and in turn why these proteins are so well conserved in metazoans.

The anionic phospholipid cardiolipin and its precursor phosphatidylglycerol are synthesized and localized in the mitochondrial inner membrane of eukaryotes. They are required for structural integrity and optimal activities of a large number of mitochondrial proteins and complexes. Previous studies showed that loss of anionic phospholipids leads to cell inviability in the absence of mitochondrial DNA. However, the mechanism linking loss of anionic phospholipids to petite lethality was unclear. To elucidate the mechanism, we constructed a crd1Deltarho degrees mutant, which is viable and mimics phenotypes of pgs1Delta in the petite background. We found that loss of cardiolipin in rho degrees cells leads to elevated expression of Swe1p, a morphogenesis checkpoint protein. Moreover, the retrograde pathway is activated in crd1Deltarho degrees cells, most likely due to the exacerbation of mitochondrial dysfunction. Interestingly, the expression of SWE1 is dependent on retrograde regulation as elevated expression of SWE1 is suppressed by deletion of RTG2 or RTG3. Taken together, these findings indicate that activation of the retrograde pathway leads to up-regulation of SWE1 in crd1Deltarho degrees cells. These results suggest that anionic phospholipids are required for processes that are essential for normal cell division in rho degrees cells.

The Drosophila gene CG14939 encodes a member of a highly conserved family of cyclins, the Y-type cyclins, which have not been functionally characterized in any organism. Here we report the generation and phenotypic characterization of a null mutant of CG14939, which we rename Cyclin Y (CycY). We show that the null mutant, CycY(E8), is homozygous lethal with most mutant animals arresting during pupal development. The mutant exhibits delayed larval growth and major developmental defects during metamorphosis, including impaired gas bubble translocation, head eversion, leg elongation, and adult tissue growth. Heat-shock-induced expression of CycY at different times during development resulted in variable levels of rescue, the timing of which suggests a key function for zygotic CycY during the transition from third instar larvae to prepupae. CycY also plays an essential role during embryogenesis since zygotic null embryos from null mothers fail to hatch into first instar larvae. We provide evidence that the CycY protein (CycY) interacts with Eip63E, a cyclin-dependent kinase (Cdk) for which no cyclin partner had previously been identified. Like CycY, the Eip63E gene has essential functions during embryogenesis, larval development, and metamorphosis. Our data suggest that CycY/Eip63E form a cyclin/Cdk complex that is essential for several developmental processes.

The yeast two-hybrid method (or interaction trap) is a powerful technique for detecting protein interactions. The procedure is performed using transcriptional activation of a dual reporter system in yeast to identify interactions between a protein of interest (the bait protein) and the candidate proteins for interaction. The method can be used to screen a protein library for interactions with a bait protein or to test for association between proteins that are expected to interact based on prior evidence. Interaction mating facilitates the screening of a library with multiple bait proteins.

Comprehensive protein-interaction mapping projects are underway for many model species and humans. A key step in these projects is estimating the time, cost and personnel required for obtaining an accurate and complete map. Here we modeled the cost of interaction-map completion for various experimental designs. We showed that current efforts may require up to 20 independent tests covering each protein pair to approach completion. We explored designs for reducing this cost substantially, including prioritization of protein pairs, probability thresholding and interaction prediction. The best experimental designs lowered cost by fourfold overall and >100-fold in early stages of mapping. We demonstrate the best strategy in an ongoing project in Drosophila melanogaster, in which we mapped 450 high-confidence interactions using 47 microtiter plates, versus thousands of plates expected using current designs. This study provides a framework for assessing the feasibility of interaction mapping projects and for future efforts to increase their efficiency.

Motivation: High-throughput experimental and computational methods are generating a wealth of protein-protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously described scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interaction space., Results: We demonstrate a method to score protein interactions by utilizing multiple independent sets of training positives to reduce the potential bias inherent in using a single training set. We used a set of benchmark yeast protein interactions to show that our approach outperforms other scoring methods. Our approach can also score interactions across data types, which makes it more widely applicable than many previously proposed methods. We applied the method to protein interaction data from both Drosophila melanogaster and Homo sapiens. Independent evaluations show that the resulting confidence scores accurately reflect the biological significance of the interactions.

Background: Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, Drosophila melanogaster., Description: DroID, the Drosophila Interactions Database, is a comprehensive interactions database designed specifically for Drosophila. DroID houses published physical protein interactions, genetic interactions, and computationally predicted interactions, including interologs based on data for other model organisms and humans. All interactions are annotated with original experimental data and source information. DroID can be searched and filtered based on interaction information or a comprehensive set of gene attributes from Flybase. DroID also contains gene expression and expression correlation data that can be searched and used to filter datasets, for example, to focus a study on sub-networks of co-expressed genes. To address the inherent noise in interaction data, DroID employs an updatable confidence scoring system that assigns a score to each physical interaction based on the likelihood that it represents a biologically significant link., Conclusion: DroID is the most comprehensive interactions database available for Drosophila. To facilitate downstream analyses, interactions are annotated with original experimental information, gene expression data, and confidence scores. All data in DroID are freely available and can be searched, explored, and downloaded through three different interfaces, including a text based web site, a Java applet with dynamic graphing capabilities (IM Browser), and a Cytoscape plug-in. DroID is available at http://www.droidb.org.

The yeast two-hybrid method (or interaction trap) is a powerful technique for detecting protein interactions. The procedure is performed using transcriptional activation of a dual reporter system in yeast to identify interactions between a protein of interest (the bait protein) and the candidate proteins for interaction. The method can be used to screen a protein library for interactions with a bait protein or to test for association between proteins that are expected to interact based on prior evidence. Interaction mating facilitates the screening of a library with multiple bait proteins., ((c) 2008 by John Wiley & Sons, Inc.)

Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here, an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named "X-Gal" and "growth assay" plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally, an artificial neural network is used to score the X-Gal and growth assay spots using the extracted features. The performance of the system is evaluated using spots of 60 images. After training the networks using training and validation sets, the system was assessed on the test set. The overall accuracies of 95.4% and 88.2% are achieved, respectively, for scoring the X-Gal and growth assay spots.

Motility is achieved in most bacterial species by the flagellar apparatus. It consists of dozens of different proteins with thousands of individual subunits. The published literature about bacterial chemotaxis and flagella documented 51 protein-protein interactions (PPIs) so far. We have screened whole genome two-hybrid arrays of Treponema pallidum and Campylobacter jejuni for PPIs involving known flagellar proteins and recovered 176 and 140 high-confidence interactions involving 110 and 133 proteins, respectively. To explore the biological relevance of these interactions, we tested an Escherichia coli gene deletion array for motility defects (using swarming assays) and found 159 gene deletion strains to have reduced or no motility. Comparing our interaction data with motility phenotypes from E. coli, Bacillus subtilis, and Helicobacter pylori, we found 23 hitherto uncharacterized proteins involved in motility. Integration of phylogenetic information with our interaction and phenotyping data reveals a conserved core of motility proteins, which appear to have recruited many additional species-specific components over time. Our interaction data also predict 18,110 interactions for 64 flagellated bacteria.

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.

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.

Background: Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets., Description: Here we describe a comprehensive database of Drosophila gene and protein interactions collected from a variety of sources, including low and high throughput screens, genetic interactions, and computational predictions. We also present a program for exploring multiple interaction data sets and for combining data from different sources. The program, referred to as the Interaction Map (IM) Browser, is a web-based application for searching and visualizing interaction data stored in a relational database system. Use of the application requires no downloads and minimal user configuration or training, thereby enabling rapid initial access to interaction data. IM Browser was designed to readily accommodate and integrate new types of interaction data as it becomes available. Moreover, all information associated with interaction measurements or predictions and the genes or proteins involved are accessible to the user. This allows combined searches and analyses based on either common or technique-specific attributes. The data can be visualized as an editable graph and all or part of the data can be downloaded for further analysis with other tools for specific applications. The database is available at http://proteome.wayne.edu/PIMdb.html, Conclusion: The Drosophila Interactions Database described here places a variety of disparate data into one easily accessible location. The database has a simple structure that maintains all relevant information about how each interaction was determined. The IM Browser provides easy, complete access to this database and could readily be used to publish other sets of interaction data. By providing access to all of the available information from a variety of data types, the program will also facilitate advanced computational analyses.

Maspin, a novel serine protease inhibitor, suppresses tumor progression in several cancer models, including an in vivo model for prostate cancer bone metastasis. However, the molecular mechanism of maspin remains illusive, primarily because its molecular targets are unknown. To this end, we used a full-length maspin cDNA bait to screen against both a primary prostate tumor cDNA prey library and a HeLa cDNA prey library by the yeast two-hybrid method. We found that heat shock protein 90, glutathione S-transferase (GST), and heat shock protein 70 interacted with maspin with the highest frequencies. We confirmed the maspin/GST interaction using purified proteins, human epithelial cell lines, and human prostate tissues. A maspin variant that has a point mutation of Arg(340) to Ala (Mas(R340A)) showed a significantly decreased affinity for GST. Although purified maspin had no effect on the activity of purified GST in vitro, intracellular interaction between endogenous maspin and GST correlated with an elevated total GST activity in both MDA-MB-435- and DU145-derived stably transfected cells. Consistently, tumor cells treated with purified wild type maspin, but not Mas(R340A), enhanced cellular GST activity. Maspin expression in cancer cell lines also correlated with decreased basal levels of reactive oxygen species (ROS). Furthermore, H(2)O(2) treatment not only induced GST expression but also increased intracellular maspin/GST interaction, which was inversely correlated with the level of ROS generation. Conversely, maspin knockdown by small interfering RNA increased the basal, as well as H(2)O(2)-induced, ROS generation. Furthermore, the maspin effect on ROS generation was completely abolished by a GST inhibitor, indicating an essential role of GST in maspin-mediated cellular response to oxidative stress. Consistently, oxidative stress-induced vascular endothelial growth factor A expression was significantly inhibited in maspin-expressing cells. Together, our data suggest a new mechanism by which maspin, through its direct interaction with GST, may inhibit oxidative stress-induced ROS generation and vascular endothelial growth factor A induction, thus preventing further adverse effects on tumor genetics and stromal reactivity.

A system-level understanding of any biological process requires a map of the relationships among the various molecules involved. Technologies to detect and predict protein interactions have begun to produce very large maps of protein interactions, some including most of an organism's proteins. These maps can be used to study how proteins work together to form molecular machines and regulatory pathways. They also provide a framework for constructing predictive models of how information and energy flow through biological networks. In many respects, protein interaction maps are an entrée into systems biology.

A rate-limiting and costly step in many proteomics analyses is the cloning of all of the ORFs for an organism into technique-specific vectors. Here, we describe the generation of a Campylobacter jejuni expression clone set using a high-throughput cloning approach based on recombination in E. coli. The approach uses native E. coli recombination functions and requires no in vitro enzymatic steps or special strains. Our results indicate that this approach is an efficient and economical alternative for high-throughput cloning.

Background: Maps depicting binary interactions between proteins can be powerful starting points for understanding biological systems. A proven technology for generating such maps is high-throughput yeast two-hybrid screening. In the most extensive screen to date, a Gal4-based two-hybrid system was used recently to detect over 20,000 interactions among Drosophila proteins. Although these data are a valuable resource for insights into protein networks, they cover only a fraction of the expected number of interactions., Results: To complement the Gal4-based interaction data, we used the same set of Drosophila open reading frames to construct arrays for a LexA-based two-hybrid system. We screened the arrays using a novel pooled mating approach, initially focusing on proteins related to cell-cycle regulators. We detected 1,814 reproducible interactions among 488 proteins. The map includes a large number of novel interactions with potential biological significance. Informative regions of the map could be highlighted by searching for paralogous interactions and by clustering proteins on the basis of their interaction profiles. Surprisingly, only 28 interactions were found in common between the LexA- and Gal4-based screens, even though they had similar rates of true positives., Conclusions: The substantial number of new interactions discovered here supports the conclusion that previous interaction mapping studies were far from complete and that many more interactions remain to be found. Our results indicate that different two-hybrid systems and screening approaches applied to the same proteome can generate more comprehensive datasets with more cross-validated interactions. The cell-cycle map provides a guide for further defining important regulatory networks in Drosophila and other organisms.

PIKfyve, a kinase that displays specificity for phosphatidylinositol (PtdIns), PtdIns 3-phosphate (3-P), and proteins, is important in multivesicular body/late endocytic function. Enzymatically inactive PIKfyve mutants elicit enormous dilation of late endocytic structures, suggesting a role for PIKfyve in endosome-to-trans-Golgi network (TGN) membrane retrieval. Here we report that p40, a Rab9 effector reported previously to bind Rab9-GTP and stimulate endosome-to-TGN transport, interacts with PIKfyve as determined by yeast two-hybrid assays, glutathione S-transferase (GST) pull-down assays, and co-immunoprecipitation in doubly transfected HEK293 cells. The interaction engages the PIKfyve chaperonin domain and four out of the six C-terminally positioned kelch repeats in p40. Differential centrifugation in a HEK293 cell line, stably expressing PIKfyveWT, showed the membrane-associated immunoreactive p40 co-sedimenting with PIKfyve in the high speed pellet (HSP) fraction. Remarkably, similar analysis in a HEK293 cell line stably expressing dominant-negative kinase-deficient PIKfyveK1831E demonstrated a marked depletion of p40 from the HSP fraction. GST-p40 failed to specifically associate with the PIKfyve lipid products PtdIns 5-P and PtdIns 3,5-P2 in a liposome binding assay but was found to be an in vitro substrate of the PIKfyve serine kinase activity. A band with the p40 electrophoretic mobility was found to react with a phosphoserine-specific antibody mainly in the PIKfyveWT-containing fractions obtained by density gradient sedimentation of total membranes from PIKfyveWT-expressing HEK293 cells. Together these results identify the Rab9 effector p40 as a PIKfyve partner and suggest that p40-PIKfyve interaction and the subsequent PIKfyve-catalyzed p40 phosphorylation anchor p40 to discrete membranes facilitating late endosome-to-TGN transport.

Drosophila melanogaster is a proven model system for many aspects of human biology. Here we present a two-hybrid-based protein-interaction map of the fly proteome. A total of 10,623 predicted transcripts were isolated and screened against standard and normalized complementary DNA libraries to produce a draft map of 7048 proteins and 20,405 interactions. A computational method of rating two-hybrid interaction confidence was developed to refine this draft map to a higher confidence map of 4679 proteins and 4780 interactions. Statistical modeling of the network showed two levels of organization: a short-range organization, presumably corresponding to multiprotein complexes, and a more global organization, presumably corresponding to intercomplex connections. The network recapitulated known pathways, extended pathways, and uncovered previously unknown pathway components. This map serves as a starting point for a systems biology modeling of multicellular organisms, including humans.

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.

DCC (deleted in colorectal cancer) is a candidate tumor suppressor gene. However the function of DCC remains elusive. Previously, we demonstrated that forced expression of DCC induces apoptosis or cell cycle arrest (Chen, Y. Q., Hsieh, J. T., Yao, F., Fang, B., Pong, R. C., Cipriano, S. C. & Krepulat, F. (1999) Oncogene 18, 2747-2754). To delineate the DCC-induced apoptotic pathway, we have identified a protein, DIP13 alpha, which interacts with DCC. The DIP13 alpha protein has a pleckstrin homology domain and a phosphotyrosine binding domain. It interacts with a region on the DCC cytoplasmic domain that is required for the induction of apoptosis. Although ectopic expression of DIP13 alpha alone causes only a slight increase in apoptosis, co-expression of DCC and DIP13 alpha results in an approximately 5-fold increase in apoptosis. Removal of the DCC-interacting domain on DIP13 alpha abolishes its ability to enhance DCC-induced apoptosis. Inhibition of endogenous DIP13 alpha expression by small interfering RNA blocks DCC-induced apoptosis. Our data suggest that DIP13 alpha is a mediator of the DCC apoptotic pathway.

Galectin-3 is a multifunctional oncogenic protein found in the nucleus and cytoplasm and also the extracellular milieu. Although recent studies demonstrated an anti-apoptotic activity of galectin-3, neither the functional site nor the mechanism of how galectin-3 regulates apoptosis is known. In this study, we examined the subcellular localization of galectin-3 during apoptosis and investigated its anti-apoptotic actions. We report that galectin-3 translocates to the perinuclear membrane following a variety of apoptotic stimuli. Confocal microscopy and biochemical analysis revealed that galectin-3 is enriched in the mitochondria and prevents mitochondrial damage and cytochrome c release. Using a yeast two-hybrid system, we screened for galectin-3-interacting proteins that regulate galectin-3 localization and anti-apoptotic activity. Synexin, a Ca(2+)- and phospholipid-binding protein, was one of the proteins identified. We confirmed direct interaction between galectin-3 and synexin by glutathione S-transferase pull-down assay in vitro. We showed that galectin-3 failed to translocate to the perinuclear membranes when expression of synexin was down-regulated using an oligodeoxyribonucleotide complementary to the synexin mRNA, suggesting a role for synexin in galectin-3 trafficking. Furthermore, synexin down-regulation abolished anti-apoptotic activity of galectin-3. Taken together, these results suggest that synexin mediates galectin-3 translocation to the perinuclear mitochondrial membranes, where it regulates mitochondrial integrity critical for apoptosis regulation.

The ability to express heterologous genes in yeast has become indispensable for many biological research techniques. Expression systems that can be regulated are particularly useful because they allow an experimenter to control the timing and levels of gene expression. Despite their many advantages, however, surprisingly few conditional expression systems are available for yeast. Moreover, of those that have been described, many are not ideal either because they have high background expression levels, low induced levels, or because they require restrictive growth conditions. Here we describe a conditional expression system that takes advantage of the yeast MAL62 promoter (MAL62p), which can be controlled by adding maltose or glucose to the growth medium to induce or repress transcription, respectively. In addition, we use a mating scheme to dramatically increase the dynamic range of expression levels possible. We show that MAL62p background activity can be effectively eliminated by maintaining expression constructs in a mal(-) yeast strain. High-level expression can be induced in diploids formed by mating the mal(-) strain with a MAL(+) strain. A similar mating scheme may be useful for other conditional expression systems as well. Among other uses, this approach should aid high throughput yeast two-hybrid assays, which rely on maintaining large libraries of expression strains, which are eventually mated to conduct assays for protein interactions. We demonstrate a two-hybrid system in which MAL62p is used in conjunction with the yeast GAL1 promoter to independently regulate expression of both hybrid proteins, and to allow detection of interactions involving toxic proteins.

This unit presents protocols designed to detect interacting proteins. Using yeast as a "test tube" and transcriptional activation of a reporter system, interacting proteins can be identified. The system can also be used to test complex formation for proteins for which there exists a reason to expect interaction.

The parameters and experimental conditions for this important system are constantly undergoing improvement. This newest version includes expanded tables describing interaction trap components and additional libraries compatible with the interaction trap system. It also features a new protocol on performing a hunt by interaction mating. Some of the commercial vendors selling yeast two-hybrid reagents recommend using interaction mating to perform a hunt, so this procedure should be of great interest.

The parameters and experimental conditions for this important system are constantly undergoing improvement. This newest version includes expanded tables describing interaction trap components and additional libraries compatible with the interaction trap system. It also features a new protocol on performing a hunt by interaction mating. Some of the commercial vendors selling yeast two-hybrid reagents recommend using interaction mating to perform a hunt, so this procedure should be of great interest.

The nuclear division cycles of early Drosophila embryogenesis have a number of unique features that distinguish them from later cell cycles. These features include the lack of some checkpoints that operate in later cell cycles, the absence of gap phases, and very rapid DNA synthesis phases. The molecular mechanisms that control these rapid nuclear division cycles are poorly understood. Here we describe analysis of cyclin J, a previously uncharacterized cyclin which has an RNA expression pattern that suggests a possible role in early embryogenesis. We show that the cyclin J protein is present in early embryos where it forms active kinase complexes with cyclin-dependent kinase (Cdk) 2. To determine whether cyclin J plays a role in controlling the early nuclear cycles we isolated peptide aptamers that specifically bind to cyclin J and inhibit its ability to activate Cdks. We injected the inhibitory aptamers into syncytial Drosophila embryos and demonstrated that they caused defects in chromosome segregation and progression through mitosis. We obtained similar results by injecting cyclin J antibodies into embryos. Our results suggest that a cyclin J-associated kinase activity is required for the early embryonic division cycles., (Copyright 2000 Academic Press.)

Protein-protein interactions mediate many important cellular processes and are central to the mechanisms by which most proteins function. Charting the interactions among the proteins involved in a process has been an essential step in characterising the function of proteins and pathways. The yeast two-hybrid system is one approach to detecting protein interactions that can now be scaled-up to assay large sets of proteins systematically, such as those being identified from genome sequencing efforts. The system has already been extensively used to acquire data that have enabled construction of large protein interaction maps (PIMs). When combined with other data, including data being generated by other functional genomics approaches, PIMs help assign function to new proteins and delineate functional networks. Hypotheses generated in such a manner often must be tested by additional experimentation, preferably in vivo. The model organism Drosophila melanogaster has a wealth of genetic and bioinformatic tools available for such analyses. The proteome predicted from the recently sequenced Drosophila genome indicates that humans have more genes in common with Drosophila than with any other invertebrate model organism characterised to date. Thus, the construction and characterisation of Drosophila PIMs will help define the functions of many conserved genes and pathways, and will provide the pharmaceutical research industry with invaluable data to assist with drug target identification and validation.

To study potential roles of plasma membrane-associated extracellular cathepsin B in tumor cell invasion and metastasis, we used the yeast two-hybrid system to screen for proteins that interact with human procathepsin B. The annexin II light chain (p11), one of the two subunits of the annexin II tetramer, was one of the proteins identified. We have confirmed that recombinant human procathepsin B interacts with p11 as well as with the annexin II tetramer in vitro. Furthermore, procathepsin B could interact with the annexin II tetramer in vivo as demonstrated by coimmunoprecipitation. Cathepsin B and the annexin II tetramer were shown by immunofluorescent staining to colocalize on the surface of human breast carcinoma and glioma cells. Taken together, our results indicate that the annexin II tetramer can serve as a binding protein for procathepsin B on the surface of tumor cells, an interaction that may facilitate tumor invasion and metastasis.

The platelet isoform of 12-lipoxygenase (12-LOX) is expressed in a variety of human tumors. 12-LOX metabolizes arachidonic acid to 12(S)-hydroxyeicosateraenoic acid (12(S)-HETE), which induces a number of cellular responses associated with tumor progression and metastasis. Little is known about 12-LOX regulation and no direct regulators of 12-LOX activity have been identified. To identify potential regulators of 12-LOX, we isolated cDNAs encoding 12-LOX interacting proteins using the yeast two-hybrid system. We screened a yeast two-hybrid interaction library from human epidermoid carcinoma A431 cells and identified four cellular proteins that interact specifically with 12-LOX. We identified type II keratin 5, lamin A, the cytoplasmic domain of integrin beta4 subunit and a phosphoprotein C8FW as 12-LOX interacting proteins. Here, we demonstrated that keratin 5, a 58 kD protein required for formation of 8 nm intermediate filaments, binds to 12-LOX in human tumor cells and may contribute to the regulated trafficking of 12-LOX. We also showed that lamin A binds 12-LOX in human tumor cells. These proteins provide the first candidate regulators of 12-LOX.