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.

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.

Protein function is often modulated by protein-protein interactions (PPIs) and therefore defining the partners of a protein helps to understand its activity. PPIs can be detected through different experimental approaches and are collected in several expert curated databases. These databases are used by researchers interested in examining detailed information on particular proteins. In many analyses the reliability of the characterization of the interactions becomes important and it might be necessary to select sets of PPIs of different confidence levels. To this goal, we generated HIPPIE (Human Integrated Protein-Protein Interaction rEference), a human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIE's scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool (available at http://cbdm.mdc-berlin.de/tools/hippie) allows researchers to do network analyses focused on likely true PPI sets by generating subnetworks around proteins of interest at a specified confidence level.

Phosphate is required for many important cellular processes and having too little phosphate or too much can cause disease and reduce life span in humans. However, the mechanisms underlying homeostatic control of extracellular phosphate levels and cellular effects of phosphate are poorly understood. Here, we establish Drosophila melanogaster as a model system for the study of phosphate effects. We found that Drosophila larval development depends on the availability of phosphate in the medium. Conversely, life span is reduced when adult flies are cultured on high phosphate medium or when hemolymph phosphate is increased in flies with impaired malpighian tubules. In addition, RNAi-mediated inhibition of MAPK-signaling by knockdown of Ras85D, phl/D-Raf or Dsor1/MEK affects larval development, adult life span and hemolymph phosphate, suggesting that some in vivo effects involve activation of this signaling pathway by phosphate. To identify novel genetic determinants of phosphate responses, we used Drosophila hemocyte-like cultured cells (S2R+) to perform a genome-wide RNAi screen using MAPK activation as the readout. We identified a number of candidate genes potentially important for the cellular response to phosphate. Evaluation of 51 genes in live flies revealed some that affect larval development, adult life span and hemolymph phosphate levels.

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.

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.

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.

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.

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.

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.

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.

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.