A protein complex network of Drosophila melanogaster.

Determining the composition of protein complexes is an essential step toward understanding the cell as an integrated system. Using coaffinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly 5,000 individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a statistical framework designed 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 the DPiM and its usefulness as a paradigm for metazoan proteomes are apparent from the recovery of many known complexes, significant enrichment for shared functional attributes, and validation in human cells. The DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. The DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution.

Architecture of the human interactome defines protein communities and disease networks.

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 elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification-mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 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 of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 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.

Extensive cross-regulation of post-transcriptional regulatory networks in Drosophila.

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 cross-regulatory and hierarchical interactions in post-transcriptional control.

The Notch signalling system: recent insights into the complexity of a conserved pathway.

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 Notch Interactome: Complexity in Signaling Circuitry.

The Notch pathway controls a very broad spectrum of cell fates in metazoans during development, influencing proliferation, differentiation and cell death. Given its central role in normal development and homeostasis, misregulation of Notch signals can lead to various disorders including cancer. How the Notch pathway mediates such pleiotropic and differential effects is of fundamental importance. It is becoming increasingly clear through a number of large-scale genetic and proteomic studies that Notch interacts with a staggeringly large number of other genes and pathways in a context-dependent, complex, and highly regulated network, which determines the ultimate biological outcome. How best to interpret and analyze the continuously increasing wealth of data on Notch interactors remains a challenge. Here we review the current state of genetic and proteomic data related to the Notch interactome.

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.

Proteomic analysis of the Notch interactome.

Recent large-scale studies have provided a global description of the interactome-the whole network of protein interactions in a cell or an organism-for several model organisms. Defining protein interactions on a proteome-wide scale has led to a better understanding of the cellular functions of many proteins, especially those that have not been studied by classical molecular genetic approaches. Here we describe the resources, methods, and techniques necessary for generation of such a proteome-scale interactome in a high throughput manner. These procedures will also be applicable to low or medium throughput focused studies aimed at understanding interactions between members of specific pathways such as Notch signaling.

Drosophila protein interaction map (DPiM): a paradigm for metazoan protein complex interactions.

Proteins perform essential cellular functions as part of protein complexes, often in conjunction with RNA, DNA, metabolites and other small molecules. The genome encodes thousands of proteins but not all of them are expressed in every cell type; and expressed proteins are not active at all times. Such diversity of protein expression and function accounts for the level of biological intricacy seen in nature. Defining protein-protein interactions in protein complexes, and establishing the when, what and where of potential interactions, is therefore crucial to understanding the cellular function of any protein-especially those that have not been well studied by traditional molecular genetic approaches. We generated a large-scale resource of affinity-tagged expression-ready clones and used co-affinity purification combined with tandem mass-spectrometry to identify protein partners of nearly 5,000 Drosophila melanogaster proteins. The resulting protein complex "map" provided a blueprint of metazoan protein complex organization. Here we describe how the map has provided valuable insights into protein function in addition to generating hundreds of testable hypotheses. We also discuss recent technological advancements that will be critical in addressing the next generation of questions arising from the map.

The complex spatio-temporal regulation of the Drosophila myoblast attractant gene duf/kirre.

A key early player in the regulation of myoblast fusion is the gene dumbfounded (duf, also known as kirre). Duf must be expressed, and function, in founder cells (FCs). A fixed number of FCs are chosen from a pool of equivalent myoblasts and serve to attract fusion-competent myoblasts (FCMs) to fuse with them to form a multinucleate muscle-fibre. The spatial and temporal regulation of duf expression and function are important and play a deciding role in choice of fibre number, location and perhaps size. We have used a combination of bioinformatics and functional enhancer deletion approaches to understand the regulation of duf. By transgenic enhancer-reporter deletion analysis of the duf regulatory region, we found that several distinct enhancer modules regulate duf expression in specific muscle founders of the embryo and the adult. In addition to existing bioinformatics tools, we used a new program for analysis of regulatory sequence, PhyloGibbs-MP, whose development was largely motivated by the requirements of this work. The results complement our deletion analysis by identifying transcription factors whose predicted binding regions match with our deletion constructs. Experimental evidence for the relevance of some of these TF binding sites comes from available ChIP-on-chip from the literature, and from our analysis of localization of myogenic transcription factors with duf enhancer reporter gene expression. Our results demonstrate the complex regulation in each founder cell of a gene that is expressed in all founder cells. They provide evidence for transcriptional control--both activation and repression--as an important player in the regulation of myoblast fusion. The set of enhancer constructs generated will be valuable in identifying novel trans-acting factor-binding sites and chromatin regulation during myoblast fusion in Drosophila. Our results and the bioinformatics tools developed provide a basis for the study of the transcriptional regulation of other complex genes.