A protein complex network of Drosophila melanogaster

Multiple functions of the SNARE protein Snap29 in autophagy, endocytic, and exocytic trafficking during epithelial formation in Drosophila

How autophagic degradation is linked to endosomal trafficking routes is little known. Here we screened a collection of uncharacterized Drosophila mutants affecting membrane transport to identify new genes that also have a role in autophagy. We isolated a loss of function mutant in Snap29 (Synaptosomal-associated protein 29 kDa), the gene encoding the Drosophila homolog of the human protein SNAP29 and have characterized its function in vivo. Snap29 contains 2 soluble NSF attachment protein receptor (SNARE) domains and a asparagine-proline-phenylalanine (NPF motif) at its N terminus and rescue experiments indicate that both SNARE domains are required for function, whereas the NPF motif is in part dispensable. We find that Snap29 interacts with SNARE proteins, localizes to multiple trafficking organelles, and is required for protein trafficking and for proper Golgi apparatus morphology. Developing tissue lacking Snap29 displays distinctive epithelial architecture defects and accumulates large amounts of autophagosomes, highlighting a major role of Snap29 in autophagy and secretion. Mutants for autophagy genes do not display epithelial architecture or secretion defects, suggesting that the these alterations of the Snap29 mutant are unlikely to be caused by the impairment of autophagy. In contrast, we find evidence of elevated levels of hop-Stat92E (hopscotch-signal transducer and activator of transcription protein at 92E) ligand, receptor, and associated signaling, which might underlie the epithelial defects. In summary, our findings support a role of Snap29 at key steps of membrane trafficking, and predict that signaling defects may contribute to the pathogenesis of cerebral dysgenesis, neuropathy, ichthyosis, and palmoplantar keratoderma (CEDNIK), a human congenital syndrome due to loss of Snap29.

Navigating the global protein-protein interaction landscape using iRefWeb

iRefWeb is a bioinformatics resource that offers access to a large collection of data on protein-protein interactions in over a thousand organisms. This collection is consolidated from 14 major public databases that curate the scientific literature. The collection is enhanced with a range of versatile data filters and search options that categorize various types of protein-protein interactions and protein complexes. Users of iRefWeb are able to retrieve all curated interactions for a given organism or those involving a given protein (or a list of proteins), narrow down their search results based on different supporting evidence, and assess the reliability of these interactions using various criteria. They may also examine all data and annotations related to any publication that described the interaction-detection experiments. iRefWeb is freely available to the research community worldwide at http://wodaklab.org/iRefWeb .

The Drosophila melanogaster septin gene Sep2 has a redundant function with the retrogene Sep5 in imaginal cell proliferation but is essential for oogenesis

Septins are cytoskeletal proteins that form hetero-oligomeric complexes and function in many biological processes, including cytokinesis. Drosophila melanogaster has five septin genes. Sep5, which is the most recently evolved septin gene in Drosophila, is a retrogene copy of Sep2. Sep5 mutants appear wild type, whereas Sep2 mutant females are semisterile. Their ovaries have egg chambers containing abnormal numbers of nurse cells. The egg chamber phenotype is rescued to wild type by expressing a Sep2 cDNA, but it is only partially rescued by expressing a Sep5 cDNA, showing that these paralogs have diverged in function at the protein level. Sep2 Sep5 double mutants have an early pupal lethal phenotype and lack imaginal discs, suggesting that these genes have redundant functions during imaginal cell proliferation.

Transcriptome and proteome quantification of a tumor model provides novel insights into post-transcriptional gene regulation

Background

Genome‐wide transcriptome analyses have given systems‐level insights into gene regulatory networks. Due to the limited depth of quantitative proteomics, however, our understanding of post‐transcriptional gene regulation and its effects on protein‐complex stoichiometry are lagging behind.

Results

Here, we employ deep sequencing and the isobaric tag for relative and absolute quantification (iTRAQ) technology to determine transcript and protein expression changes of a Drosophila brain tumor model at near genome‐wide resolution. In total, we quantify more than 6,200 tissue‐specific proteins, corresponding to about 70% of all transcribed protein‐coding genes. Using our integrated data set, we demonstrate that post‐transcriptional gene regulation varies considerably with biological function and is surprisingly high for genes regulating transcription. We combine our quantitative data with protein‐protein interaction data and show that post‐transcriptional mechanisms significantly enhance co‐regulation of protein‐complex subunits beyond transcriptional co‐regulation. Interestingly, our results suggest that only about 11% of the annotated Drosophila protein complexes are co‐regulated in the brain. Finally, we refine the composition of some of these core protein complexes by analyzing the co‐regulation of potential subunits.

Conclusions

Our comprehensive transcriptome and proteome data provide a valuable resource for quantitative biology and offer novel insights into understanding post‐transcriptional gene regulation in a tumor model.

Exploring mitochondrial system properties of neurodegenerative diseases through interactome mapping

Mitochondria are double membraned, dynamic organelles that are required for a large number of cellular processes, and defects in their function have emerged as causative factors for a growing number of human disorders and are highly associated with cancer, metabolic, and neurodegenerative (ND) diseases. Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in ND disease, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. However, high-throughput proteomic and genomic approaches developed in genetically tractable model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins, including cytosolic and membrane proteins. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We discuss how the knowledge from the resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus further clarify the role of mitochondrial biology and the complex etiologies of ND disease.

BIOLOGICAL SIGNIFICANCE

Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in neurodegenerative (ND) diseases, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. Large-scale proteomic and genomic approaches developed in model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins. Extension of this new framework to the mitochondrial sub-system in human will likewise provide a universally informative systems-level view of the physical and functional landscape for exploring the evolutionary principles underlying mitochondrial function. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We anticipate that the knowledge from these resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus foster a deeper molecular understanding of mitochondrial biology as well as the etiology of mitochondrial diseases. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Integrating protein-protein interaction networks with phenotypes reveals signs of interactions

A major objective of systems biology is to organize molecular interactions as networks and to characterize information flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the 'signs' of interactions (i.e., activation-inhibition relationships). We constructed a Drosophila melanogaster signed PPI network consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes enolase and aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation-inhibition relationships between physically interacting proteins within signaling pathways will affect our understanding of many biological functions, including signal transduction and mechanisms of disease.

High-resolution network biology: connecting sequence with function

Proteins are not monolithic entities; rather, they can contain multiple domains that mediate distinct interactions, and their functionality can be regulated through post-translational modifications at multiple distinct sites. Traditionally, network biology has ignored such properties of proteins and has instead examined either the physical interactions of whole proteins or the consequences of removing entire genes. In this Review, we discuss experimental and computational methods to increase the resolution of protein-protein, genetic and drug-gene interaction studies to the domain and residue levels. Such work will be crucial for using interaction networks to connect sequence and structural information, and to understand the biological consequences of disease-associated mutations, which will hopefully lead to more effective therapeutic strategies.

Promiscuity as a functional trait: intrinsically disordered regions as central players of interactomes

Because of their pervasiveness in eukaryotic genomes and their unique properties, understanding the role that ID (intrinsically disordered) regions in proteins play in the interactome is essential for gaining a better understanding of the network. Especially critical in determining this role is their ability to bind more than one partner using the same region. Studies have revealed that proteins containing ID regions tend to take a central role in protein interaction networks; specifically, they act as hubs, interacting with multiple different partners across time and space, allowing for the co-ordination of many cellular activities. There appear to be three different modules within ID regions responsible for their functionally promiscuous behaviour: MoRFs (molecular recognition features), SLiMs (small linear motifs) and LCRs (low complexity regions). These regions allow for functionality such as engaging in the formation of dynamic heteromeric structures which can serve to increase local activity of an enzyme or store a collection of functionally related molecules for later use. However, the use of promiscuity does not come without a cost: a number of diseases that have been associated with ID-containing proteins seem to be caused by undesirable interactions occurring upon altered expression of the ID-containing protein.

Human intellectual disability genes form conserved functional modules in Drosophila

Intellectual Disability (ID) disorders, defined by an IQ below 70, are genetically and phenotypically highly heterogeneous. Identification of common molecular pathways underlying these disorders is crucial for understanding the molecular basis of cognition and for the development of therapeutic intervention strategies. To systematically establish their functional connectivity, we used transgenic RNAi to target 270 ID gene orthologs in the Drosophila eye. Assessment of neuronal function in behavioral and electrophysiological assays and multiparametric morphological analysis identified phenotypes associated with knockdown of 180 ID gene orthologs. Most of these genotype-phenotype associations were novel. For example, we uncovered 16 genes that are required for basal neurotransmission and have not previously been implicated in this process in any system or organism. ID gene orthologs with morphological eye phenotypes, in contrast to genes without phenotypes, are relatively highly expressed in the human nervous system and are enriched for neuronal functions, suggesting that eye phenotyping can distinguish different classes of ID genes. Indeed, grouping genes by Drosophila phenotype uncovered 26 connected functional modules. Novel links between ID genes successfully predicted that MYCN, PIGV and UPF3B regulate synapse development. Drosophila phenotype groups show, in addition to ID, significant phenotypic similarity also in humans, indicating that functional modules are conserved. The combined data indicate that ID disorders, despite their extreme genetic diversity, are caused by disruption of a limited number of highly connected functional modules.

Intellectual Disability (ID) affects 2% of our population and is associated with many different disorders. Although more than 400 causative genes (‘ID genes’) have been identified, their function remains poorly understood and the degree to which these disorders share a common molecular basis is unknown. Here, we systematically characterized behavioral and morphological phenotypes associated with 270 conserved ID genes, using the Drosophila eye and photoreceptor neurons as a model. These and follow up approaches generated previously undescribed genotype-phenotype associations for the majority (180) of ID gene orthologs, and identified, among others, 16 novel regulators of basal neurotransmission. Importantly, groups of genes that show the same phenotype in Drosophila are highly enriched in known connectivity, also share increased phenotypic similarity in humans and successfully predicted novel gene functions. In total, we mapped 26 conserved functional modules that together comprise 100 ID gene orthologs. Our findings provide unbiased evidence for the long suspected but never experimentally demonstrated functional coherence among ID disorders. The identified conserved functional modules may aid to develop therapeutic strategies that target genetically heterogeneous ID patients with a common treatment.

Limiting of the innate immune response by SF3A-dependent control of MyD88 alternative mRNA splicing

Controlling infectious disease without inducing unwanted inflammatory disease requires proper regulation of the innate immune response. Thus, innate immunity needs to be activated when needed during an infection, but must be limited to prevent damage. To accomplish this, negative regulators of innate immunity limit the response. Here we investigate one such negative regulator encoded by an alternative splice form of MyD88. MyD88 mRNA exists in two alternative splice forms: MyD88L, a long form that encodes a protein that activates innate immunity by transducing Toll-like receptor (TLR) signals; and a short form that encodes a different protein, MyD88S, that inhibits the response. We find that MyD88S levels regulate the extent of inflammatory cytokine production in murine macrophages. MyD88S mRNA levels are regulated by the SF3A and SF3B mRNA splicing complexes, and these mRNA splicing complexes function with TLR signaling to regulate MyD88S production. Thus, the SF3A mRNA splicing complex controls production of a negative regulator of TLR signaling that limits the extent of innate immune activation.

Simple topological features reflect dynamics and modularity in protein interaction networks

The availability of large-scale protein-protein interaction networks for numerous organisms provides an opportunity to comprehensively analyze whether simple properties of proteins are predictive of the roles they play in the functional organization of the cell. We begin by re-examining an influential but controversial characterization of the dynamic modularity of the S. cerevisiae interactome that incorporated gene expression data into network analysis. We analyse the protein-protein interaction networks of five organisms, S. cerevisiae, H. sapiens, D. melanogaster, A. thaliana, and E. coli, and confirm significant and consistent functional and structural differences between hub proteins that are co-expressed with their interacting partners and those that are not, and support the view that the former tend to be intramodular whereas the latter tend to be intermodular. However, we also demonstrate that in each of these organisms, simple topological measures are significantly correlated with the average co-expression of a hub with its partners, independent of any classification, and therefore also reflect protein intra- and inter- modularity. Further, cross-interactomic analysis demonstrates that these simple topological characteristics of hub proteins tend to be conserved across organisms. Overall, we give evidence that purely topological features of static interaction networks reflect aspects of the dynamics and modularity of interactomes as well as previous measures incorporating expression data, and are a powerful means for understanding the dynamic roles of hubs in interactomes.

A better understanding of protein interaction networks would be a great aid in furthering our knowledge of the molecular biology of the cell. Towards this end, large-scale protein-protein physical interaction data have been determined for organisms across the evolutionary spectrum. However, the resulting networks give a static view of interactomes, and our knowledge about protein interactions is rarely time or context specific. A previous prominent but controversial attempt to characterize the dynamic modularity of the interactome was based on integrating physical interaction data with gene activity measurements from transcript expression data. This analysis distinguished between proteins that are co-expressed with their interacting partners and those that are not, and argued that the former are intramodular and the latter are intermodular. By analyzing the interactomes of five organisms, we largely confirm the biological significance of this characterization through a variety of statistical tests and computational experiments. Surprisingly, however, we find that similar results can be obtained using just network information without additionally integrating expression data, suggesting that purely topological characteristics of interaction networks strongly reflect certain aspects of the dynamics and modularity of interactomes.

The Hippo signaling pathway interactome

The Hippo pathway controls metazoan organ growth by regulating cell proliferation and apoptosis. Many components have been identified, but our knowledge of the composition and structure of this pathway is still incomplete. Using existing pathway components as baits, we generated by mass spectrometry a high-confidence Drosophila Hippo protein-protein interaction network (Hippo-PPIN) consisting of 153 proteins and 204 interactions. Depletion of 67% of the proteins by RNA interference regulated the transcriptional coactivator Yorkie (Yki) either positively or negatively. We selected for further characterization a new member of the alpha-arrestin family, Leash, and show that it promotes degradation of Yki through the lysosomal pathway. Given the importance of the Hippo pathway in tumor development, the Hippo-PPIN will contribute to our understanding of this network in both normal growth and cancer.

Drosophila morgue associates with SkpA and polyubiquitin in vivo

Morgue is a unique ubiquitination protein that influences programmed cell death and circadian rhythms in Drosophila. We have found that over-expression of wild-type Morgue results in organismal lethality. This over-expression phenotype was used as the basis for an in vivo functional assay to investigate the importance of the Morgue zinc finger, F box, Ubiquitin E2 Conjugase Variant (UEV) domain, and active site Glycine residue. Removal of the zinc finger or UEV domain reduced Morgue's ability to induce lethality and enhance cell death. In contrast, lack of the F box as well as several different substitutions of the active site Glycine did not alter Morgue-induced lethality or cell death enhancement. To further characterize Morgue functions, a Flag:Morgue protein was used to isolate Morgue-associated proteins from whole adult Drosophila. Mass spectrometry analysis of the Morgue-associated proteins identified SkpA as well as a ubiquitin multimer. The identification of SkpA is consistent with previous in vitro studies and further suggests Morgue acts in an SCF-type ubiquitin E3 ligase complex. The identification of poly-ubiquitin was unexpected and this interaction had not been previously identified. The associated poly-ubiquitin was found to exhibit a Lys-48 topology, consistent with distinct functions of Morgue in proteasome-mediated protein turnover. Multiple regions of Morgue were subsequently shown to be required for poly-ubiquitin binding. Overall, Morgue is a novel multi-functional ubiquitin-binding protein.

Large precursor tolerance database search - a simple approach for estimation of the amount of spectra with precursor mass shifts in proteomic data

Mass measurement and precursor mass assignment are independent processes in proteomic data acquisition. Due to misassignments to C-13 peak, or for other reasons, extensive precursor mass shifts (i.e., deviations of the measured from calculated precursor neutral masses) in LC-MS/MS data obtained with the high-accuracy LTQ-Orbitrap mass spectrometers have been reported in previous studies. Although computational methods for post-acquisition reassignment to monoisotopic mass have been developed to curate the MS/MS spectra prior to database search, a simpler method for estimating the fraction of spectra with precursor mass shift so as to determine whether the data require curation remains desirable. Here, we provide the evidence that an easy approach, which applies a large precursor tolerance (2.1Da or higher) in SEQUEST search against a forward and decoy protein sequence database and then filters the data with PeptideProphet peptide identification probability (p≥0.9), could detect most of the MS/MS spectra containing inaccurate precursor masses. Furthermore, through the implementation of artificial mass shifts on 4000 randomly selected MS/MS spectra, which originally had accurate precursor mass assigned by the mass spectrometers, we demonstrated that the accuracy of the precursor mass has almost negligible influence on the efficacy and fidelity of peptide identification.

BIOLOGICAL SIGNIFICANCE

Integral precursor mass shift is a known problem and thus proteomic data should be handled and analyzed properly to avoid losing important protein identification and/or quantification information. A quick and easy approach for estimating the number of MS/MS spectra with inaccurate precursor mass assignments would be helpful for evaluating the performance of the instrument, determining whether the data requires curation prior to database search or should be searched with specific search parameter(s). Here we demonstrated most of the MS/MS spectra with inaccurate mass assignments (integral or non-integral changes) that could be easily identified by database search with large precursor tolerance windows.

A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis

Background

Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts.

Results

We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance.

Conclusions

Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely possible. Fruitful future directions may include investigating more sophisticated schemes for converting spectral counts to probabilities and applying the framework to direct protein complex prediction methods.

Specification of DNA replication origins and genomic base composition in fission yeasts

In the "Replicon Theory", Jacob, Brenner and Cuzin proposed the existence of replicators and initiators as the two major actors in DNA replication. Over the years, many protein components of initiators have been shown to be conserved in different organisms during evolution. By contrast, replicator DNA sequences (often referred to as replication origins) have diverged beyond possible comparison between eukaryotic genomes. Replication origins in the fission yeast Schizosaccharomyces pombe are made up of A+T-rich sequences that do not share any consensus elements. The information encoded in these replicators is interpreted by the Orc4 subunit of the ORC (origin recognition complex), which is unique among eukaryotes in that it contains a large domain harboring nine AT-hook subdomains that target ORC to a great variety of A+T-rich sequences along the chromosomes. Recently, the genomes of other Schizosaccharomyces species have been sequenced and the regions encompassing their replication origins have been identified. DNA sequence analysis and comparison of the organization of their Orc4 proteins have revealed species-specific differences that contribute to our understanding of how the specification of replication origins has evolved during the phylogenetic divergence of fission yeasts.

The Drosophila transcription factor Adf-1 (nalyot) regulates dendrite growth by controlling FasII and Staufen expression downstream of CaMKII and neural activity

Memory deficits in Drosophila nalyot mutants suggest that the Myb family transcription factor Adf-1 is an important regulator of developmental plasticity in the brain. However, the cellular functions for this transcription factor in neurons or molecular mechanisms by which it regulates plasticity remain unknown. Here, we use in vivo 3D reconstruction of identifiable larval motor neuron dendrites to show that Adf-1 is required cell autonomously for dendritic development and activity-dependent plasticity of motor neurons downstream of CaMKII. Adf-1 inhibition reduces dendrite growth and neuronal excitability, and results in motor deficits and altered transcriptional profiles. Surprisingly, analysis by comparative chromatin immunoprecipitation followed by sequencing (ChIP-Seq) of Adf-1, RNA Polymerase II (Pol II), and histone modifications in Kc cells shows that Adf-1 binding correlates positively with high Pol II-pausing indices and negatively with active chromatin marks such as H3K4me3 and H3K27ac. Consistently, the expression of Adf-1 targets Staufen and Fasciclin II (FasII), identified through larval brain ChIP-Seq for Adf-1, is negatively regulated by Adf-1, and manipulations of these genes predictably modify dendrite growth. Our results imply mechanistic interactions between transcriptional and local translational machinery in neurons as well as conserved neuronal growth mechanisms mediated by cell adhesion molecules, and suggest that CaMKII, Adf-1, FasII, and Staufen influence crucial aspects of dendrite development and plasticity with potential implications for memory formation. Further, our experiments reveal molecular details underlying transcriptional regulation by Adf-1, and indicate active interaction between Adf-1 and epigenetic regulators of gene expression during activity-dependent neuronal plasticity.

The superfamily of mitochondrial Complex1_LYR motif-containing (LYRM) proteins

Mitochondrial LYRM (leucine/tyrosine/arginine motif) proteins are members of the Complex1_LYR-like superfamily. Individual LYRM proteins have been identified as accessory subunits or assembly factors of mitochondrial OXPHOS (oxidative phosphorylation) complexes I, II, III and V respectively, and they play particular roles in the essential Fe–S cluster biogenesis and in acetate metabolism. LYRM proteins have been implicated in mitochondrial dysfunction, e.g. in the context of insulin resistance. However, the functional significance of the common LYRM is still unknown. Analysis of protein–protein interaction screens suggests that LYRM proteins form protein complexes with phylogenetically ancient proteins of bacterial origin. Interestingly, the mitochondrial FAS (fatty acid synthesis) type II acyl-carrier protein ACPM associates with some of the LYRM protein-containing complexes. Eukaryotic LYRM proteins interfere with mitochondrial homoeostasis and might function as adaptor-like ‘accessory factors’.

Wingless/Wnt signaling in Drosophila: the pattern and the pathway

Wnt signaling generates pattern in all animal embryos, from flies and worms to humans, and promotes the undifferentiated, proliferative state critical for stem cells in adult tissues. Inappropriate Wnt pathway activation is the major cause of colorectal cancers, a leading cause of cancer death in humans. Although this pathway has been studied extensively for years, large gaps remain in our understanding of how it switches on and off, and how its activation changes cellular behaviors. Much of what is known about the pathway comes from genetic studies in Drosophila, where a single Wnt molecule, encoded by wingless (wg), directs an array of cell-fate decisions similar to those made by the combined activities of all 19 Wnt family members in vertebrates. Although Wg specifies fate in many tissues, including the brain, limbs, and major organs, the fly embryonic epidermis has proven to be a very powerful system for dissecting pathway activity. It is a simple, accessible tissue, with a pattern that is highly sensitive to small changes in Wg pathway activity. This review discusses what we have learned about Wnt signaling from studying mutations that disrupt epidermal pattern in the fly embryo, highlights recent advances and controversies in the field, and sets these issues in the context of questions that remain about how this essential signaling pathway functions.

Piecing together the extracellular puzzle

Large-scale screens to identify protein interactions typically underperform with eukaryotic extracellular proteins. In this issue, Özkan et al. report development of a high-throughput assay designed specifically for extracellular proteins that uncovers a wealth of new interactions among three protein superfamilies in Drosophila and sets the stage for more extensive screens.

Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS

Background

The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms.

Results

In this study we demonstrate a random-forest based technique, ENTS, for the computational prediction of protein-protein interactions based only on primary sequence data. Our approach is able to efficiently predict interactions on a whole-genome scale for any eukaryotic organism, using pairwise combinations of conserved domains and predicted subcellular localization of proteins as input features. We present the first predicted interactome for the forest tree Populus trichocarpa in addition to the predicted interactomes for Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Arabidopsis thaliana. Comparing our approach to other PPI predictors, we find that ENTS performs comparably to or better than a number of existing approaches, including several that utilize a variety of functional information for their predictions. We also find that the predicted interactions are biologically meaningful, as indicated by similarity in functional annotations and enrichment of co-expressed genes in public microarray datasets. Furthermore, we demonstrate some of the biological insights that can be gained from these predicted interaction networks. We show that the predicted interactions yield informative groupings of P. trichocarpa metabolic pathways, literature-supported associations among human disease states, and theory-supported insight into the evolutionary dynamics of duplicated genes in paleopolyploid plants.

Conclusion

We conclude that the ENTS classifier will be a valuable tool for the de novo annotation of genome sequences, providing initial clues about regulatory and metabolic network topology, and revealing relationships that are not immediately obvious from traditional homology-based annotations.

Affinity purification strategies for proteomic analysis of transcription factor complexes

Affinity purification (AP) coupled to mass spectrometry (MS) has been successful in elucidating protein molecular networks of mammalian cells. These approaches have dramatically increased the knowledge of the interconnectivity present among proteins and highlighted biological functions within different protein complexes. Despite significant technical improvements reached in the past years, it is still challenging to identify the interaction networks and the subsequent associated functions of nuclear proteins such as transcription factors (TFs). A straightforward and robust methodology is therefore required to obtain unbiased and reproducible interaction data. Here we present a new approach for TF AP-MS, exemplified with the CCAAT/enhancer binding protein alpha (C/EBPalpha). Utilizing the advantages of a double tag and three different MS strategies, we conducted a total of six independent AP-MS strategies to analyze the protein–protein interactions of C/EBPalpha. The resultant data were combined to produce a cohesive C/EBPalpha interactome. Our study describes a new methodology that robustly identifies specific molecular complexes associated with transcription factors. Moreover, it emphasizes the existence of TFs as protein complexes essential for cellular biological functions and not as single, static entities.

Tracking context-specific transcription factors regulating hox activity

BACKGROUND

Hox proteins are key developmental regulators involved in almost every embryonic tissue for specifying cell fates along longitudinal axes or during organ formation. It is thought that the panoply of Hox activities relies on interactions with tissue-, stage-, and/or cell-specific transcription factors. High-throughput approaches in yeast or cell culture systems have shown that Hox proteins bind to various types of nuclear and cytoplasmic components, illustrating their remarkable potential to influence many different cell regulatory processes. However, these approaches failed to identify a relevant number of context-specific transcriptional partners, suggesting that these interactions are hard to uncover in non-physiological conditions. Here we discuss this problematic.

RESULTS

In this review, we present intrinsic Hox molecular signatures that are probably involved in multiple (yet specific) interactions with transcriptional partners. We also recapitulate the current knowledge on Hox cofactors, highlighting the difficulty to tracking context-specific cofactors through traditional large-scale approaches.

CONCLUSION

We propose experimental approaches that will allow a better characterisation of interaction networks underlying Hox contextual activities in the next future.

Platelet-derived growth factor/vascular endothelial growth factor receptor inactivation by sunitinib results in Tsc1/Tsc2-dependent inhibition of TORC1

Vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) receptors are implicated in development and tumorigenesis and dual inhibitors like sunitinib are prescribed for cancer treatment. While mammalian VEGF and PDGF receptors are present in multiple isoforms and heterodimers, Drosophila encodes one ancestral PDGF/VEGF receptor, PVR. We identified PVR in an unbiased cell-based RNA interference (RNAi) screen of all Drosophila kinases and phosphatases for novel regulators of TORC1. PVR is essential to sustain target of rapamycin complex 1 (TORC1) and extracellular signal-regulated kinase (ERK) activity in cultured insect cells and for maximal stimulation by insulin. CG32406 (henceforth, PVRAP, for PVR adaptor protein), an Src homology 2 (SH2) domain-containing protein, binds PVR and is required for TORC1 activation. TORC1 activation by PVR involves Tsc1/Tsc2 and, in a cell-type-dependent manner, Lobe (ortholog of PRAS40). PVR is required for cell survival in vitro, and both PVR and TORC1 are necessary for hemocyte expansion in vivo. Constitutive PVR activation induces tumor-like structures that exhibit high TORC1 activity. Like its mammalian orthologs, PVR is inhibited by sunitinib, and sunitinib treatment phenocopies PVR loss in hemocytes. Sunitinib inhibits TORC1 in insect cells, and sunitinib-mediated TORC1 inhibition requires an intact Tsc1/Tsc2 complex. Sunitinib similarly inhibited TORC1 in human endothelial cells in a Tsc1/Tsc2-dependent manner. Our findings provide insight into the mechanism of action of PVR and may have implications for understanding sunitinib sensitivity and resistance in tumors.

Shared protein complex subunits contribute to explaining disrupted co-occurrence

The gene composition of present-day genomes has been shaped by a complicated evolutionary history, resulting in diverse distributions of genes across genomes. The pattern of presence and absence of a gene in different genomes is called its phylogenetic profile. It has been shown that proteins whose encoding genes have highly similar profiles tend to be functionally related: As these genes were gained and lost together, their encoded proteins can probably only perform their full function if both are present. However, a large proportion of genes encoding interacting proteins do not have matching profiles. In this study, we analysed one possible reason for this, namely that phylogenetic profiles can be affected by multi-functional proteins such as shared subunits of two or more protein complexes. We found that by considering triplets of proteins, of which one protein is multi-functional, a large fraction of disturbed co-occurrence patterns can be explained.

Every genome of current day species contains a very unique selection of genes. Why a specific genome is composed of exactly those genes is determined by many factors, but often not resolvable. It seems plausible that interacting genes would either occur together or be absent together, because if one of them is alone, it might not be able to perform its function properly, just as a bolt can only perform its function together with a nut and vice versa. However, it turns out that interacting genes very often do not nicely co-occur across a wide range of species, and frequently one gene can be found but the other not. In this study, we investigated the co-occurrences of multi-functional proteins and found that they are often maintained in a genome, even if one of their interaction partners is lost. This is because they can still perform some functions with other interaction partners that are still present. We can show that this has a noticeable effect on genome compositions and can explain otherwise surprisingly mismatching co-occurrence patterns of interacting genes.

Inferring signalling networks from images

The mapping of signalling networks is one of biology's most important goals. However, given their size, complexity and dynamic nature, obtaining comprehensive descriptions of these networks has proven extremely challenging. A fast and cost-effective means to infer connectivity between genes on a systems-level is by quantifying the similarity between high-dimensional cellular phenotypes following systematic gene depletion. This review describes the methodology used to map signalling networks using data generated in the context of RNAi screens.

Widespread evidence of cooperative DNA binding by transcription factors in Drosophila development

Regulation of eukaryotic gene transcription is often combinatorial in nature, with multiple transcription factors (TFs) regulating common target genes, often through direct or indirect mutual interactions. Many individual examples of cooperative binding by directly interacting TFs have been identified, but it remains unclear how pervasive this mechanism is during animal development. Cooperative TF binding should be manifest in genomic sequences as biased arrangements of TF-binding sites. Here, we explore the extent and diversity of such arrangements related to gene regulation during Drosophila embryogenesis. We used the DNA-binding specificities of 322 TFs along with chromatin accessibility information to identify enriched spacing and orientation patterns of TF-binding site pairs. We developed a new statistical approach for this task, specifically designed to accurately assess inter-site spacing biases while accounting for the phenomenon of homotypic site clustering commonly observed in developmental regulatory regions. We observed a large number of short-range distance preferences between TF-binding site pairs, including examples where the preference depends on the relative orientation of the binding sites. To test whether these binding site patterns reflect physical interactions between the corresponding TFs, we analyzed 27 TF pairs whose binding sites exhibited short distance preferences. In vitro protein–protein binding experiments revealed that >65% of these TF pairs can directly interact with each other. For five pairs, we further demonstrate that they bind cooperatively to DNA if both sites are present with the preferred spacing. This study demonstrates how DNA-binding motifs can be used to produce a comprehensive map of sequence signatures for different mechanisms of combinatorial TF action.

Evolution of three parent genes and their retrogene copies in Drosophila species

Retrogenes form a class of gene duplicate lacking the regulatory sequences found outside of the mRNA-coding regions of the parent gene. It is not clear how a retrogene's lack of parental regulatory sequences affects the evolution of the gene pair. To explore the evolution of parent genes and retrogenes, we investigated three such gene pairs in the family Drosophilidae; in Drosophila melanogaster, these gene pairs are CG8331 and CG4960, CG17734 and CG11825, and Sep2 and Sep5. We investigated the embryonic expression patterns of these gene pairs across multiple Drosophila species. Expression patterns of the parent genes and their single copy orthologs are relatively conserved across species, whether or not a species has a retrogene copy, although there is some variation in CG8331 and CG17734. In contrast, expression patterns of the retrogene orthologs have diversified. We used the genome sequences of 20 Drosophila species to investigate coding sequence evolution. The coding sequences of the three gene pairs appear to be evolving predominantly under negative selection; however, the parent genes and retrogenes show some distinct differences in amino acid sequence. Therefore, in general, retrogene expression patterns and coding sequences are distinct compared to their parents and, in some cases, retrogene expression patterns diversify.

An extracellular interactome of immunoglobulin and LRR proteins reveals receptor-ligand networks

Extracellular domains of cell surface receptors and ligands mediate cell-cell communication, adhesion, and initiation of signaling events, but most existing protein-protein "interactome" data sets lack information for extracellular interactions. We probed interactions between receptor extracellular domains, focusing on a set of 202 proteins composed of the Drosophila melanogaster immunoglobulin superfamily (IgSF), fibronectin type III (FnIII), and leucine-rich repeat (LRR) families, which are known to be important in neuronal and developmental functions. Out of 20,503 candidate protein pairs tested, we observed 106 interactions, 83 of which were previously unknown. We "deorphanized" the 20 member subfamily of defective-in-proboscis-response IgSF proteins, showing that they selectively interact with an 11 member subfamily of previously uncharacterized IgSF proteins. Both subfamilies interact with a single common "orphan" LRR protein. We also observed interactions between Hedgehog and EGFR pathway components. Several of these interactions could be visualized in live-dissected embryos, demonstrating that this approach can identify physiologically relevant receptor-ligand pairs.

Modeling the genetic basis for human sleep disorders in Drosophila

Sleep research in Drosophila is not only here to stay, but is making impressive strides towards helping us understand the biological basis for and the purpose of sleep—perhaps one of the most complex and enigmatic of behaviors. Thanks to over a decade of sleep-related studies in flies, more molecular methods are being applied than ever before towards understanding the genetic basis of sleep disorders. The advent of high-throughput technologies that can rapidly interrogate whole genomes, epigenomes and proteomes, has also revolutionized our ability to detect genetic variants that might be causal for a number of sleep disorders. In the coming years, mutational studies in model organisms such as Drosophila will need to be functionally connected to information being generated from these whole-genome approaches in humans. This will necessitate the development of appropriate methods for interpolating data and increased analytical power to synthesize useful network(s) of sleep regulatory pathways—including appropriate discriminatory and predictive capabilities. Ultimately, such networks will also need to be interpreted in the context of fundamental neurobiological substrates for sleep in any given species. In this review, we highlight some emerging approaches, such as network analysis and mathematical modeling of sleep distributions, which can be applied to contemporary sleep research as a first step to achieving these aims. These methodologies should favorably impact not only a mechanistic understanding of sleep, but also future pharmacological intervention strategies to manage and treat sleep disorders in humans.

Genetic circuitry of Survival motor neuron, the gene underlying spinal muscular atrophy

The clinical severity of the neurodegenerative disorder spinal muscular atrophy (SMA) is dependent on the levels of functional Survival Motor Neuron (SMN) protein. Consequently, current strategies for developing treatments for SMA generally focus on augmenting SMN levels. To identify additional potential therapeutic avenues and achieve a greater understanding of SMN, we applied in vivo, in vitro, and in silico approaches to identify genetic and biochemical interactors of the Drosophila SMN homolog. We identified more than 300 candidate genes that alter an Smn-dependent phenotype in vivo. Integrating the results from our genetic screens, large-scale protein interaction studies, and bioinformatic analysis, we define a unique interactome for SMN that provides a knowledge base for a better understanding of SMA.

Characterization of molecules binding to the 70K N-terminal region of fibronectin by IFAST purification coupled with mass spectrometry

Fibronectin (Fn) is a large glycoprotein present in plasma and extracellular matrix and is important for many processes. Within Fn the 70 kDa N-terminal region (70k-Fn) is involved in cell-mediated Fn assembly, a process that contributes to embryogenesis, development, and platelet thrombus formation. In addition, major human pathogens including Staphlycoccus aureus and Streptococcus pyogenes bind the 70k-Fn region by a novel form of protein-protein interaction called β-zipper formation, facilitating bacterial spread and colonization. Knowledge of blood plasma and platelet proteins that interact with 70k-Fn by β-zipper formation is incomplete. In the current study, we aimed to characterize these proteins through affinity purification. For this affinity purification, we used a novel purification technique termed immiscible filtration assisted by surface tension (IFAST). The foundation of this technology is immiscible phase filtration, using a magnet to draw paramagnetic particle (PMP)-bound analyte through an immiscible barrier (oil or organic solvent) that separates an aqueous sample from an aqueous eluting buffer. The immiscible barrier functions to remove unbound proteins via exclusion rather than dilutive washing used in traditional isolation methods. We identified 31 interactors from plasma, of which only seven were previously known to interact with Fn. Furthermore, five proteins were identified to interact with 70k-Fn from platelet lysate, of which one was previously known. These results demonstrate that IFAST offers advantages for proteomic studies of interacting molecules in that the technique requires small sample volumes, can be done with high enough throughput to sample multiple interaction conditions, and is amenable to exploratory mass spectrometric and confirmatory immuno-blotting read-outs.

Overexpression of Down syndrome cell adhesion molecule impairs precise synaptic targeting

Fragile X syndrome is caused by loss of Fragile X Mental Retardation Protein (FMRP), an RNA binding protein that suppresses protein translation. Here, we identified Down Syndrome Cell Adhesion Molecule (Dscam) RNA, a molecule involved in neural development and implicated in Down syndrome, bound to FMRP. Elevated Dscam protein levels in Drosophila FMRP null animals and in animals with three copies of the Dscam gene both produced specific and similar synaptic targeting errors in a hard-wired neural circuit which impaired the animal’s sensory perception. Reducing Dscam levels in FMRP null animals reduced synaptic targeting errors and rescued behavioral responses. Our results demonstrate that excess Dscam protein may be a common molecular mechanism underlying altered neural wiring in major causes of intellectual disability.

Chromatin-associated proteins HP1 and Mod(mdg4) modify Y-linked regulatory variation in the drosophila testis

Chromatin remodeling is crucial for gene regulation. Remodeling is often mediated through chemical modifications of the DNA template, DNA-associated proteins, and RNA-mediated processes. Y-linked regulatory variation (YRV) refers to the quantitative effects that polymorphic tracts of Y-linked chromatin exert on gene expression of X-linked and autosomal genes. Here we show that naturally occurring polymorphisms in the Drosophila melanogaster Y chromosome contribute disproportionally to gene expression variation in the testis. The variation is dependent on wild-type expression levels of mod(mdg4) as well as Su(var)205; the latter gene codes for heterochromatin protein 1 (HP1) in Drosophila. Testis-specific YRV is abolished in genotypes with heterozygous loss-of-function mutations for mod(mdg4) and Su(var)205 but not in similar experiments with JIL-1. Furthermore, the Y chromosome differentially regulates several ubiquitously expressed genes. The results highlight the requirement for wild-type dosage of Su(var)205 and mod(mdg4) in enabling naturally occurring Y-linked regulatory variation in the testis. The phenotypes that emerge in the context of wild-type levels of the HP1 and Mod(mdg4) proteins might be part of an adaptive response to the environment.

Protein-protein interaction networks: probing disease mechanisms using model systems

Protein-protein interactions (PPIs) and multi-protein complexes perform central roles in the cellular systems of all living organisms. In humans, disruptions of the normal patterns of PPIs and protein complexes can be causative or indicative of a disease state. Recent developments in the biological applications of mass spectrometry (MS)-based proteomics have expanded the horizon for the application of systematic large-scale mapping of physical interactions to probe disease mechanisms. In this review, we examine the application of MS-based approaches for the experimental analysis of PPI networks and protein complexes, focusing on the different model systems (including human cells) used to study the molecular basis of common diseases such as cancer, cardiomyopathies, diabetes, microbial infections, and genetic and neurodegenerative disorders.

Drafting the CLN3 protein interactome in SH-SY5Y human neuroblastoma cells: a label-free quantitative proteomics approach

Neuronal ceroid lipofuscinoses (NCL) are the most common inherited progressive encephalopathies of childhood. One of the most prevalent forms of NCL, Juvenile neuronal ceroid lipofuscinosis (JNCL) or CLN3 disease (OMIM: 204200), is caused by mutations in the CLN3 gene on chromosome 16p12.1. Despite progress in the NCL field, the primary function of ceroid-lipofuscinosis neuronal protein 3 (CLN3) remains elusive. In this study, we aimed to clarify the role of human CLN3 in the brain by identifying CLN3-associated proteins using a Tandem Affinity Purification coupled to Mass Spectrometry (TAP-MS) strategy combined with Significance Analysis of Interactome (SAINT). Human SH-SY5Y-NTAP-CLN3 stable cells were used to isolate native protein complexes for subsequent TAP-MS. Bioinformatic analyses of isolated complexes yielded 58 CLN3 interacting partners (IP) including 42 novel CLN3 IP, as well as 16 CLN3 high confidence interacting partners (HCIP) previously identified in another high-throughput study by Behrends et al., 2010. Moreover, 31 IP of ceroid-lipofuscinosis neuronal protein 5 (CLN5) were identified (18 of which were in common with the CLN3 bait). Our findings support previously suggested involvement of CLN3 in transmembrane transport, lipid homeostasis and neuronal excitability, as well as link it to G-protein signaling and protein folding/sorting in the ER.

The Drosophila over compensating males gene genetically inhibits dosage compensation in males

Male Drosophila are monosomic for the X chromosome, but survive due to dosage compensation. They use the Male Specific Lethal (MSL) complex composed of noncoding roX RNA and histone modifying enzymes to hypertranscribe most genes along the X ∼1.6–1.8 fold relative to each female allele. It is not known how the MSL complex achieves this precise adjustment to a large and diverse set of target genes. We carried out a genetic screen searching for novel factors that regulate dosage compensation in flies. This strategy generated thirty alleles in a previously uncharacterized gene, over compensating males (ocm) that antagonizes some aspect of MSL activity. The mutations were initially recovered because they derepressed an MSL-dependent eye color reporter. Null ocm mutations are lethal to both sexes early in development revealing an essential function. Combinations of hypomorphic ocm alleles display a male specific lethality similar to mutations in the classic msl genes, but ocm males die due to excessive, rather than lack of dosage compensation. Males that die due to very low MSL activity can be partially rescued by ocm mutations. Likewise, males that would die from ocm mutations can be rescued by reducing the dose of various msl and roX genes. ocm encodes a large nuclear protein that shares a novel cysteine rich motif with known transcription factors.

Following the 'tracks': Tramtrack69 regulates epithelial tube expansion in the Drosophila ovary through Paxillin, Dynamin, and the homeobox protein Mirror

Epithelial tubes are the infrastructure for organs and tissues, and tube morphogenesis requires precise orchestration of cell signaling, shape, migration, and adhesion. Follicle cells in the Drosophila ovary form a pair of epithelial tubes whose lumens act as molds for the eggshell respiratory filaments, or dorsal appendages (DAs). DA formation is a robust and accessible model for studying the patterning, formation, and expansion of epithelial tubes. Tramtrack69 (TTK69), a transcription factor that exhibits a variable embryonic DNA-binding preference, controls DA lumen volume and shape by promoting tube expansion; the tramtrack mutation twin peaks (ttk(twk)) reduces TTK69 levels late in oogenesis, inhibiting this expansion. Microarray analysis of wild-type and ttk(twk) ovaries, followed by in situ hybridization and RNAi of candidate genes, identified the Phospholipase B-like protein Lamina ancestor (LAMA), the scaffold protein Paxillin, the endocytotic regulator Shibire (Dynamin), and the homeodomain transcription factor Mirror, as TTK69 effectors of DA-tube expansion. These genes displayed enriched expression in DA-tube cells, except lama, which was expressed in all follicle cells. All four genes showed reduced expression in ttk(twk) mutants and exhibited RNAi phenotypes that were enhanced in a ttk(twk)/+ background, indicating ttk(twk) genetic interactions. Although previous studies show that Mirror patterns the follicular epithelium prior to DA tubulogenesis, we show that Mirror has an independent, novel role in tube expansion, involving positive regulation of Paxillin. Thus, characterization of ttk(twk)-differentially expressed genes expands the network of TTK69 effectors, identifies novel epithelial tube-expansion regulators, and significantly advances our understanding of this vital developmental process.

The contribution of systematic approaches to characterizing the proteins and functions of the endoplasmic reticulum

The endoplasmic reticulum (ER) is a complex organelle responsible for a range of functions including protein folding and secretion, lipid biosynthesis, and ion homeostasis. Despite its central and essential roles in eukaryotic cells during development, growth, and disease, many ER proteins are poorly characterized. Moreover, the range of biochemical reactions that occur within the ER membranes, let alone how these different activities are coordinated, is not yet defined. In recent years, focused studies on specific ER functions have been complemented by systematic approaches and innovative technologies for high-throughput analysis of the location, levels, and biological impact of given components. This article focuses on the recent progress of these efforts, largely pioneered in the budding yeast Saccharomyces cerevisiae, and also addresses how future systematic studies can be geared to uncover the "dark matter" of uncharted ER functions.

Protein complex-based analysis framework for high-throughput data sets

Analysis of high-throughput data increasingly relies on pathway annotation and functional information derived from Gene Ontology. This approach has limitations, in particular for the analysis of network dynamics over time or under different experimental conditions, in which modules within a network rather than complete pathways might respond and change. We report an analysis framework based on protein complexes, which are at the core of network reorganization. We generated a protein complex resource for human, Drosophila, and yeast from the literature and databases of protein-protein interaction networks, with each species having thousands of complexes. We developed COMPLEAT (http://www.flyrnai.org/compleat), a tool for data mining and visualization for complex-based analysis of high-throughput data sets, as well as analysis and integration of heterogeneous proteomics and gene expression data sets. With COMPLEAT, we identified dynamically regulated protein complexes among genome-wide RNA interference data sets that used the abundance of phosphorylated extracellular signal-regulated kinase in cells stimulated with either insulin or epidermal growth factor as the output. The analysis predicted that the Brahma complex participated in the insulin response.

The revolution and evolution of shotgun proteomics for large-scale proteome analysis

Mass spectrometry has evolved at an exponential rate over the last 100 years. Innovations in the development of mass spectrometers have created powerful instruments capable of analyzing a wide range of targets, from rare atoms and molecules to very large molecules, such as a proteins, protein complexes, and DNA. These performance gains have been driven by sustaining innovations, punctuated by the occasional disruptive innovation. The use of mass spectrometry for proteome analysis was driven by disruptive innovations that created a capability for large-scale analysis of proteins and modifications.

Changes in higher order structures of chromatin by RNP complexes

More than four decades ago, it was shown that RNA stably associates with chromatin. These studies indicated that chromatin-associated RNAs (caRNA) might be involved in the organization of chromatin structure. However, it is only recently that pools of chromatin-associated RNAs were characterized and functional studies were initiated. In Drosophila cells, an RNP complex consisting of snoRNAs and Decondensation factor 31 (Df31) is stably tethered to chromatin, mediated by the RNA- and histone-binding activities of Df31. Biochemical and functional characterizations suggest a structural role of this complex in chromatin organization. The binding of the Df31-snoRNA complex to chromatin results in the opening and the maintenance of accessible higher order structures of chromatin. We suggest that different classes of chromatin-associated RNPs are required for the targeted opening of higher order structures of chromatin, enabling the activation of DNA-dependent processes such as transcription.

Identification and characterization of proteins involved in nuclear organization using Drosophila GFP protein trap lines

Background

Strains from a collection of Drosophila GFP protein trap lines express GFP in the normal tissues where the endogenous protein is present. This collection can be used to screen for proteins distributed in the nucleus in a non-uniform pattern.

Methodology/Principal Findings

We analyzed four lines that show peripheral or punctate nuclear staining. One of these lines affects an uncharacterized gene named CG11138. The CG11138 protein shows a punctate distribution in the nuclear periphery similar to that of Drosophila insulator proteins but does not co-localize with known insulators. Interestingly, mutations in Lamin proteins result in alterations in CG11138 localization, suggesting that this protein may be a novel component of the nuclear lamina. A second line affects the Decondensation factor 31 (Df31) gene, which encodes a protein with a unique nuclear distribution that appears to segment the nucleus into four different compartments. The X-chromosome of males is confined to one of these compartments. We also find that Drosophila Nucleoplasmin (dNlp) is present in regions of active transcription. Heat shock leads to loss of dNlp from previously transcribed regions of polytene chromosome without redistribution to the heat shock genes. Analysis of Stonewall (Stwl), a protein previously found to be necessary for the maintenance of germline stem cells, shows that Stwl is present in a punctate pattern in the nucleus that partially overlaps with that of known insulator proteins. Finally we show that Stwl, dNlp, and Df31 form part of a highly interactive network. The properties of other components of this network may help understand the role of these proteins in nuclear biology.

Conclusions/Significance

These results establish screening of GFP protein trap alleles as a strategy to identify factors with novel cellular functions. Information gained from the analysis of CG11138 Stwl, dNlp, and Df31 sets the stage for future studies of these proteins.

In Drosophila, RhoGEF2 cooperates with activated Ras in tumorigenesis through a pathway involving Rho1-Rok-Myosin-II and JNK signalling

The Ras oncogene contributes to ≈ 30% of human cancers, but alone is not sufficient for tumorigenesis. In a Drosophila screen for oncogenes that cooperate with an activated allele of Ras (Ras(ACT)) to promote tissue overgrowth and invasion, we identified the GTP exchange factor RhoGEF2, an activator of Rho-family signalling. Here, we show that RhoGEF2 also cooperates with an activated allele of a downstream effector of Ras, Raf (Raf(GOF)). We dissect the downstream pathways through which RhoGEF2 cooperates with Ras(ACT) (and Raf(GOF)), and show that RhoGEF2 requires Rho1, but not Rac, for tumorigenesis. Furthermore, of the Rho1 effectors, we show that RhoGEF2 + Ras (Raf)-mediated tumorigenesis requires the Rho kinase (Rok)-Myosin-II pathway, but not Diaphanous, Lim kinase or protein kinase N. The Rho1-Rok-Myosin-II pathway leads to the activation of Jun kinase (JNK), in cooperation with Ras(ACT). Moreover, we show that activation of Rok or Myosin II, using constitutively active transgenes, is sufficient for cooperative tumorigenesis with Ras(ACT), and together with Ras(ACT) leads to strong activation of JNK. Our results show that Rok-Myosin-II activity is necessary and sufficient for Ras-mediated tumorigenesis. Our observation that activation of Myosin II, which regulates Filamentous actin (F-actin) contractility without affecting F-actin levels, cooperates with Ras(ACT) to promote JNK activation and tumorigenesis, suggests that increased cell contractility is a key factor in tumorigenesis. Furthermore, we show that signalling via the Tumour necrosis factor (TNF; also known as Egr)-ligand-JNK pathway is most likely the predominant pathway that activates JNK upon Rok activation. Overall, our analysis highlights the need for further analysis of the Rok-Myosin-II pathway in cooperation with Ras in human cancers.