The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidation of how genome variation contributes to disease1–3. Here, we present BioPlex 2.0 (Biophysical Interactions of ORFEOME-derived complexes), which employs robust affinity purification-mass spectrometry (AP-MS) methodology4 to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein coding genes from the human genome, and constitutes the largest such network to date. With >56,000 candidate interactions, BioPlex 2.0 contains >29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering (MCL)5 of interacting proteins identified more than 1300 protein communities representing diverse cellular activities. Genes essential for cell fitness6,7 are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

SUMMARY Determining the composition of protein complexes is an essential step towards understanding the cell as an integrated system. Using co-affinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly five thousand individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a novel statistical framework to define individual protein-protein interactions, led to the generation of a Drosophila Protein interaction Map (DPiM) encompassing 556 protein complexes. The high quality of DPiM and its usefulness as a paradigm for metazoan proteomes is apparent from the recovery of many known complexes, significant enrichment for shared functional attributes and validation in human cells. DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution.

Notch signalling links the fate of one cell to that of an immediate neighbour and consequently controls differentiation, proliferation and apoptotic events in multiple metazoan tissues. Perturbations in this pathway activity have been linked to several human genetic disorders and cancers. Recent genome-scale studies in Drosophila melanogaster have revealed an extraordinarily complex network of genes that can affect Notch activity. This highly interconnected network contrasts our traditional view of the Notch pathway as a simple linear sequence of events. Although we now have an unprecedented insight into the way in which such a fundamental signalling mechanism is controlled by the genome, we are faced with serious challenges in analysing the underlying molecular mechanisms of Notch signal control.

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.pinal muscular atrophy (SMA), the leading genetic cause of infant mortality, results from the partial loss of Survival Motor Neuron (SMN) gene activity (1). Numerous studies indicate that SMN functions as a central component of a complex that is responsible for the assembly of spliceosomal small nuclear ribonucleoproteins (snRNPs) (reviewed in ref.2). SMN is also reported to play additional roles, including mRNA trafficking in the axon (3). In humans, SMN is encoded by two nearly identical genes, SMN1 and SMN2, which are located on chromosome 5 (4). SMN2 differs from SMN1 in that only 10% of SMN2 transcripts produce functional SMN due to a single-nucleotide polymorphism that results in inefficient splicing of exon 7 and translation of a truncated, unstable SMN protein (1,5,6). The clinical severity of SMA correlates with the SMN2 copy number, which varies between individuals (7). As the small amount of functional SMN2 protein produced by each copy of the gene is capable of partially compensating for the loss of the SMN1 gene function, higher copy numbers of SMN2 typically result in milder forms of SMA. Therefore, genetic modifiers capable of increasing the abundance and/or specific activity of SMN hold promise as therapeutic targets.The Drosophila genome harbors a single, highly conserved ortholog of SMN1/2, the Smn gene. SMN is essential for cell viability in vertebrates and Drosophila (8, 9). In Drosophila, zygotic loss of Smn function results in recessive larval lethality (not embryonic as might be expected) due to the rescue of early development by maternal contribution of Smn. The larval phenotype includes neuromuscular junction (NMJ) abnormalities that are reminiscent of those associated with the human disease, rendering this invertebrate organism an excellent system to model SMN biology (9-11). We previously described a genetic screen for modifiers of the lethal phenotype resulting from a complete lossof-function Smn allele (12). This screen, although it probed half of the Drosophila genome, identified only a relatively small number of genes that affected the NMJ phenotype associated with Smn loss of function (12). In particular, it did not identify genes involved in snRNP biogenesis, the molecular func...

In eukaryotic cells, RNAs exist as ribonucleoprotein particles (RNPs). Despite the importance of these complexes in many biological processes, including splicing, polyadenylation, stability, transportation, localization, and translation, their compositions are largely unknown. We affinity-purified 20 distinct RNA-binding proteins (RBPs) from cultured Drosophila melanogaster cells under native conditions and identified both the RNA and protein compositions of these RNP complexes. We identified "high occupancy target" (HOT) RNAs that interact with the majority of the RBPs we surveyed. HOT RNAs encode components of the nonsense-mediated decay and splicing machinery, as well as RNA-binding and translation initiation proteins. The RNP complexes contain proteins and mRNAs involved in RNA binding and post-transcriptional regulation. Genes with the capacity to produce hundreds of mRNA isoforms, ultracomplex genes, interact extensively with heterogeneous nuclear ribonuclear proteins (hnRNPs). Our data are consistent with a model in which subsets of RNPs include mRNA and protein products from the same gene, indicating the widespread existence of auto-regulatory RNPs. From the simultaneous acquisition and integrative analysis of protein and RNA constituents of RNPs, we identify extensive crossregulatory and hierarchical interactions in post-transcriptional control.