The role of translationally controlled tumor protein in proliferation of Drosophila intestinal stem cells.

Translationally controlled tumor protein (TCTP) is a highly conserved protein functioning in multiple cellular processes, ranging from growth to immune responses. To explore the role of TCTP in tissue maintenance and regeneration, we employed the adult Drosophila midgut, where multiple signaling pathways interact to precisely regulate stem cell division for tissue homeostasis. Tctp levels were significantly increased in stem cells and enteroblasts upon tissue damage or activation of the Hippo pathway that promotes regeneration of intestinal epithelium. Stem cells with reduced Tctp levels failed to proliferate during normal tissue homeostasis and regeneration. Mechanistically, Tctp forms a complex with multiple proteins involved in translation and genetically interacts with ribosomal subunits. In addition, Tctp increases both Akt1 protein abundance and phosphorylation in vivo. Altogether, Tctp regulates stem cell proliferation by interacting with key growth regulatory signaling pathways and the translation process in vivo.

Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data.

Model organism and human databases are rich with information about genetic and physical interactions. These data can be used to interpret and guide the analysis of results from new studies and develop new hypotheses. Here, we report the development of the Molecular Interaction Search Tool (MIST; http://fgrtools.hms.harvard.edu/MIST/). The MIST database integrates biological interaction data from yeast, nematode, fly, zebrafish, frog, rat and mouse model systems, as well as human. For individual or short gene lists, the MIST user interface can be used to identify interacting partners based on protein-protein and genetic interaction (GI) data from the species of interest as well as inferred interactions, known as interologs, and to view a corresponding network. The data, interologs and search tools at MIST are also useful for analyzing 'omics datasets. In addition to describing the integrated database, we also demonstrate how MIST can be used to identify an appropriate cut-off value that balances false positive and negative discovery, and present use-cases for additional types of analysis. Altogether, the MIST database and search tools support visualization and navigation of existing protein and GI data, as well as comparison of new and existing data.

FlyRNAi.org-the database of the Drosophila RNAi screening center and transgenic RNAi project: 2017 update.

The FlyRNAi database of the Drosophila RNAi Screening Center (DRSC) and Transgenic RNAi Project (TRiP) at Harvard Medical School and associated DRSC/TRiP Functional Genomics Resources website (http://fgr.hms.harvard.edu) serve as a reagent production tracking system, screen data repository, and portal to the community. Through this portal, we make available protocols, online tools, and other resources useful to researchers at all stages of high-throughput functional genomics screening, from assay design and reagent identification to data analysis and interpretation. In this update, we describe recent changes and additions to our website, database and suite of online tools. Recent changes reflect a shift in our focus from a single technology (RNAi) and model species (Drosophila) to the application of additional technologies (e.g. CRISPR) and support of integrated, cross-species approaches to uncovering gene function using functional genomics and other approaches.

Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets.

The protein-protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as "indispensable," "neutral," or "dispensable," which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network's control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

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.

Identification of human proteins that modify misfolding and proteotoxicity of pathogenic ataxin-1.

Proteins with long, pathogenic polyglutamine (polyQ) sequences have an enhanced propensity to spontaneously misfold and self-assemble into insoluble protein aggregates. Here, we have identified 21 human proteins that influence polyQ-induced ataxin-1 misfolding and proteotoxicity in cell model systems. By analyzing the protein sequences of these modifiers, we discovered a recurrent presence of coiled-coil (CC) domains in ataxin-1 toxicity enhancers, while such domains were not present in suppressors. This suggests that CC domains contribute to the aggregation- and toxicity-promoting effects of modifiers in mammalian cells. We found that the ataxin-1-interacting protein MED15, computationally predicted to possess an N-terminal CC domain, enhances spontaneous ataxin-1 aggregation in cell-based assays, while no such effect was observed with the truncated protein MED15ΔCC, lacking such a domain. Studies with recombinant proteins confirmed these results and demonstrated that the N-terminal CC domain of MED15 (MED15CC) per se is sufficient to promote spontaneous ataxin-1 aggregation in vitro. Moreover, we observed that a hybrid Pum1 protein harboring the MED15CC domain promotes ataxin-1 aggregation in cell model systems. In strong contrast, wild-type Pum1 lacking a CC domain did not stimulate ataxin-1 polymerization. These results suggest that proteins with CC domains are potent enhancers of polyQ-mediated protein misfolding and aggregation in vitro and in vivo.

An empirical framework for binary interactome mapping.

Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.

An integrative approach to ortholog prediction for disease-focused and other functional studies.

BACKGROUND: Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. RESULTS: We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist). CONCLUSIONS: DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.

Bioinformatics Analysis of PTM-Modified Protein Interaction Networks and Complexes.

Normal cellular functioning is maintained by macromolecular machines that control both core and specialized molecular tasks. These machines are in large part multi-subunit protein complexes that undergo regulation at multiple levels, from expression of requisite components to a vast array of post-translational modifications (PTMs). PTMs such as phosphorylation, ubiquitination, and acetylation currently number more than 200,000 in the human proteome and function within all molecular pathways. Here we provide a framework for systematically studying these PTMs in the context of global protein-protein interaction networks. This analytical framework allows insight into which functions specific PTMs tend to cluster in, and furthermore which complexes either single or multiple PTM signaling pathways converge on.

Proteomic and Metabolomic Characterization of a Mammalian Cellular Transition from Quiescence to Proliferation.

There exist similarities and differences in metabolism and physiology between normal proliferative cells and tumor cells. Once a cell enters the cell cycle, metabolic machinery is engaged to facilitate various processes. The kinetics and regulation of these metabolic changes have not been properly evaluated. To correlate the orchestration of these processes with the cell cycle, we analyzed the transition from quiescence to proliferation of a non-malignant murine pro-B lymphocyte cell line in response to IL-3. Using multiplex mass-spectrometry-based proteomics, we show that the transition to proliferation shares features generally attributed to cancer cells: upregulation of glycolysis, lipid metabolism, amino-acid synthesis, and nucleotide synthesis and downregulation of oxidative phosphorylation and the urea cycle. Furthermore, metabolomic profiling of this transition reveals similarities to cancer-related metabolic pathways. In particular, we find that methionine is consumed at a higher rate than that of other essential amino acids, with a potential link to maintenance of the epigenome.

GOPET: a tool for automated predictions of Gene Ontology terms.

BACKGROUND: Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions. DESCRIPTION: We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO). Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool). It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar CONCLUSION: Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user.

Genomic analysis of Xenopus organizer function.

BACKGROUND: Studies of the Xenopus organizer have laid the foundation for our understanding of the conserved signaling pathways that pattern vertebrate embryos during gastrulation. The two primary activities of the organizer, BMP and Wnt inhibition, can regulate a spectrum of genes that pattern essentially all aspects of the embryo during gastrulation. As our knowledge of organizer signaling grows, it is imperative that we begin knitting together our gene-level knowledge into genome-level signaling models. The goal of this paper was to identify complete lists of genes regulated by different aspects of organizer signaling, thereby providing a deeper understanding of the genomic mechanisms that underlie these complex and fundamental signaling events. RESULTS: To this end, we ectopically overexpress Noggin and Dkk-1, inhibitors of the BMP and Wnt pathways, respectively, within ventral tissues. After isolating embryonic ventral halves at early and late gastrulation, we analyze the transcriptional response to these molecules within the generated ectopic organizers using oligonucleotide microarrays. An efficient statistical analysis scheme, combined with a new Gene Ontology biological process annotation of the Xenopus genome, allows reliable and faithful clustering of molecules based upon their roles during gastrulation. From this data, we identify new organizer-related expression patterns for 19 genes. Moreover, our data sub-divides organizer genes into separate head and trunk organizing groups, which each show distinct responses to Noggin and Dkk-1 activity during gastrulation. CONCLUSION: Our data provides a genomic view of the cohorts of genes that respond to Noggin and Dkk-1 activity, allowing us to separate the role of each in organizer function. These patterns demonstrate a model where BMP inhibition plays a largely inductive role during early developmental stages, thereby initiating the suites of genes needed to pattern dorsal tissues. Meanwhile, Wnt inhibition acts later during gastrulation, and is essential for maintenance of organizer gene expression throughout gastrulation, a role which may depend on its ability to block the expression of a host of ventral, posterior, and lateral fate-specifying factors.

Global gene expression profiling and cluster analysis in Xenopus laevis.

We have undertaken a large-scale microarray gene expression analysis using cDNAs corresponding to 21,000 Xenopus laevis ESTs. mRNAs from 37 samples, including embryos and adult organs, were profiled. Cluster analysis of embryos of different stages was carried out and revealed expected affinities between gastrulae and neurulae, as well as between advanced neurulae and tadpoles, while egg and feeding larvae were clearly separated. Cluster analysis of adult organs showed some unexpected tissue-relatedness, e.g. kidney is more related to endodermal than to mesodermal tissues and the brain is separated from other neuroectodermal derivatives. Cluster analysis of genes revealed major phases of co-ordinate gene expression between egg and adult stages. During the maternal-early embryonic phase, genes maintaining a rapidly dividing cell state are predominantly expressed (cell cycle regulators, chromatin proteins). Genes involved in protein biosynthesis are progressively induced from mid-embryogenesis onwards. The larval-adult phase is characterised by expression of genes involved in metabolism and terminal differentiation. Thirteen potential synexpression groups were identified, which encompass components of diverse molecular processes or supra-molecular structures, including chromatin, RNA processing and nucleolar function, cell cycle, respiratory chain/Krebs cycle, protein biosynthesis, endoplasmic reticulum, vesicle transport, synaptic vesicle, microtubule, intermediate filament, epithelial proteins and collagen. Data filtering identified genes with potential stage-, region- and organ-specific expression. The dataset was assembled in the iChip microarray database, , which allows user-defined queries. The study provides insights into the higher order of vertebrate gene expression, identifies synexpression groups and marker genes, and makes predictions for the biological role of numerous uncharacterized genes.

An Integrative Analysis of the InR/PI3K/Akt Network Identifies the Dynamic Response to Insulin Signaling.

Insulin regulates an essential conserved signaling pathway affecting growth, proliferation, and metabolism. To expand our understanding of the insulin pathway, we combine biochemical, genetic, and computational approaches to build a comprehensive Drosophila InR/PI3K/Akt network. First, we map the dynamic protein-protein interaction network surrounding the insulin core pathway using bait-prey interactions connecting 566 proteins. Combining RNAi screening and phospho-specific antibodies, we find that 47% of interacting proteins affect pathway activity, and, using quantitative phosphoproteomics, we demonstrate that ∼10% of interacting proteins are regulated by insulin stimulation at the level of phosphorylation. Next, we integrate these orthogonal datasets to characterize the structure and dynamics of the insulin network at the level of protein complexes and validate our method by identifying regulatory roles for the Protein Phosphatase 2A (PP2A) and Reptin-Pontin chromatin-remodeling complexes as negative and positive regulators of ribosome biogenesis, respectively. Altogether, our study represents a comprehensive resource for the study of the evolutionary conserved insulin network.

Applying Support Vector Machines for Gene Ontology based gene function prediction.

BACKGROUND: The current progress in sequencing projects calls for rapid, reliable and accurate function assignments of gene products. A variety of methods has been designed to annotate sequences on a large scale. However, these methods can either only be applied for specific subsets, or their results are not formalised, or they do not provide precise confidence estimates for their predictions. RESULTS: We have developed a large-scale annotation system that tackles all of these shortcomings. In our approach, annotation was provided through Gene Ontology terms by applying multiple Support Vector Machines (SVM) for the classification of correct and false predictions. The general performance of the system was benchmarked with a large dataset. An organism-wise cross-validation was performed to define confidence estimates, resulting in an average precision of 80% for 74% of all test sequences. The validation results show that the prediction performance was organism-independent and could reproduce the annotation of other automated systems as well as high-quality manual annotations. We applied our trained classification system to Xenopus laevis sequences, yielding functional annotation for more than half of the known expressed genome. Compared to the currently available annotation, we provided more than twice the number of contigs with good quality annotation, and additionally we assigned a confidence value to each predicted GO term. CONCLUSIONS: We present a complete automated annotation system that overcomes many of the usual problems by applying a controlled vocabulary of Gene Ontology and an established classification method on large and well-described sequence data sets. In a case study, the function for Xenopus laevis contig sequences was predicted and the results are publicly available at ftp://genome.dkfz-heidelberg.de/pub/agd/gene_association.agd_Xenopus.

A rapid genome-wide microRNA screen identifies miR-14 as a modulator of Hedgehog signaling.

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression by binding to sequences within the 3' UTR of mRNAs. Because miRNAs bind to short sequences with partial complementarity, target identification is challenging. To complement the existing target prediction algorithms, we devised a systematic "reverse approach" screening platform that allows the empirical prediction of miRNA-target interactions. Using Drosophila cells, we screened the 3' untranslated regions (3' UTRs) of the Hedgehog pathway genes against a genome-wide miRNA library and identified both predicted and many nonpredicted miRNA-target interactions. We demonstrate that miR-14 is essential for maintaining the proper level of Hedgehog signaling activity by regulating its physiological target, hedgehog. Furthermore, elevated levels of miR-14 suppress Hedgehog signaling activity by cotargeting its apparent nonphysiological targets, patched and smoothened. Altogether, our systematic screening platform is a powerful approach to identifying both physiological and apparent nonphysiological targets of miRNAs, which are relevant in both normal and diseased tissues.

Combining genetic perturbations and proteomics to examine kinase-phosphatase networks in Drosophila embryos.

Connecting phosphorylation events to kinases and phosphatases is key to understanding the molecular organization and signaling dynamics of networks. We have generated a validated set of transgenic RNA-interference reagents for knockdown and characterization of all protein kinases and phosphatases present during early Drosophila melanogaster development. These genetic tools enable collection of sufficient quantities of embryos depleted of single gene products for proteomics. As a demonstration of an application of the collection, we have used multiplexed isobaric labeling for quantitative proteomics to derive global phosphorylation signatures associated with kinase-depleted embryos to systematically link phosphosites with relevant kinases. We demonstrate how this strategy uncovers kinase consensus motifs and prioritizes phosphoproteins for kinase target validation. We validate this approach by providing auxiliary evidence for Wee kinase-directed regulation of the chromatin regulator Stonewall. Further, we show how correlative phosphorylation at the site level can indicate function, as exemplified by Sterile20-like kinase-dependent regulation of Stat92E.

A regulatory network of Drosophila germline stem cell self-renewal.

Stem cells possess the capacity to generate two cells of distinct fate upon division: one cell retaining stem cell identity and the other cell destined to differentiate. These cell fates are established by cell-type-specific genetic networks. To comprehensively identify components of these networks, we performed a large-scale RNAi screen in Drosophila female germline stem cells (GSCs) covering ∼25% of the genome. The screen identified 366 genes that affect GSC maintenance, differentiation, or other processes involved in oogenesis. Comparison of GSC regulators with neural stem cell self-renewal factors identifies common and cell-type-specific self-renewal genes. Importantly, we identify the histone methyltransferase Set1 as a GSC-specific self-renewal factor. Loss of Set1 in neural stem cells does not affect cell fate decisions, suggesting a differential requirement of H3K4me3 in different stem cell lineages. Altogether, our study provides a resource that will help to further dissect the networks underlying stem cell self-renewal.

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 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.