Comprehensive analysis of the functional impact of single nucleotide variants of human CHEK2.

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.

Integrating the interactome and the transcriptome of Drosophila.

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.

Germline variant profiling of CHEK2 sequencing variants in breast cancer patients.

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.

DroID 2011: a comprehensive, integrated resource for protein, transcription factor, RNA and gene interactions for Drosophila.

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.

Antagonists of anaphase-promoting complex (APC)-2-cell cycle and apoptosis regulatory protein (CARP)-1 interaction are novel regulators of cell growth and apoptosis.

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 ß-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.

Cost-effective strategies for completing the interactome.

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.

Loss of mitochondrial DNA in the yeast cardiolipin synthase crd1 mutant leads to up-regulation of the protein kinase Swe1p that regulates the G2/M transition.

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.

Genetic diversity in Campylobacter jejuni is associated with differential colonization of broiler chickens and C57BL/6J IL10-deficient mice.

Previous studies have demonstrated that Campylobacter jejuni, the leading causative agent of bacterial food-borne disease in the USA, exhibits high-frequency genetic variation that is associated with changes in cell-surface antigens and ability to colonize chickens. To expand our understanding of the role of genetic diversity in the disease process, we analysed the ability of three C. jejuni human disease isolates (strains 11168, 33292 and 81-176) and genetically marked derivatives to colonize Ross 308 broilers and C57BL/6J IL10-deficient mice. C. jejuni colonized broilers at much higher efficiency (all three strains, 23 of 24 broilers) than mice (11168 only, 8 of 24 mice). C. jejuni 11168 genetically marked strains colonized mice at very low efficiency (2 of 42 mice); however, C. jejuni reisolated from mice colonized both mice and broilers at high efficiency, suggesting that this pathogen can adapt genetically in the mouse. We compared the genome composition in the three wild-type C. jejuni strains and derivatives by microarray DNA/DNA hybridization analysis; the data demonstrated a high degree of genetic diversity in three gene clusters associated with synthesis and modification of the cell-surface structures capsule, flagella and lipo-oligosaccharide. Finally, we analysed the frequency of mutation in homopolymeric tracts associated with the contingency genes wlaN (GC tract) and flgR (AT tracts) in culture and after passage through broilers and mice. C. jejuni adapted genetically in culture at high frequency and the degree of genetic diversity was increased by passage through broilers but was nearly eliminated in the gastrointestinal tract of mice. The data suggest that the broiler gastrointestinal tract provides an environment which promotes outgrowth and genetic variation in C. jejuni; the enhancement of genetic diversity at this location may contribute to its importance as a human disease reservoir.

Combining multiple positive training sets to generate confidence scores for protein-protein interactions.

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.

A proteome-wide screen of Campylobacter jejuni using protein microarrays identifies novel and conformational antigens.

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.

DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein 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 protein network of bacterial motility.

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.

Yeast two-hybrid contributions to interactome mapping.

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

Automated segmentation and classification of high throughput yeast assay spots.

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.

A database and tool, IM Browser, for exploring and integrating emerging gene and protein interaction data for Drosophila.

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.

From protein networks to biological systems.

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 novel ER-microtubule-binding protein, ERLIN2, stabilizes Cyclin B1 and regulates cell cycle progression.

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.

Regulated expression of proteins in yeast using the MAL61-62 promoter and a mating scheme to increase dynamic range.

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.

Simultaneous cloning of open reading frames into several different expression vectors.

Genomic sequencing has enabled the prediction of thousands of genes, most of which either cannot be assigned a function or can be only broadly categorized on the basis of sequence alone. High-throughput strategies for elucidating protein function are of high priority, and numerous approaches are being developed. Many of these approaches require the cloning of open reading frames (ORFs) into expression vectors that enable the encoded proteins to be tested for biological and biochemical activities. Typically, more than one type of vector must be employed, as different experiments require different conditions of protein production. Here we show that it is possible to simultaneously transfer a single ORF from a source vector to four target vectors using a commercially available in vitro recombination system. To test the approach, we constructed new vectors for expression of fusion proteins in yeast, including vectors for the LexA two-hybrid system. We show that individual ORFs can be efficiently transferred to four different vectors in a single in vitro reaction. The resulting expression plasmids can be separated using prototrophic markers specific to each vector. Using this system to produce multiple expression constructs simultaneously could greatly facilitate high-throughput subcloning and proteomic studies.

A role for Drosophila Cyclin J in oogenesis revealed by genetic interactions with the piRNA pathway.

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.