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Zebrafish have become a popular organism for the study of vertebrate gene function. The virtually transparent embryos of this species, and the ability to accelerate genetic studies by gene knockdown or overexpression, have led to the widespread use of zebrafish in the detailed investigation of vertebrate gene function and increasingly, the study of human genetic disease. However, for effective modelling of human genetic disease it is important to understand the extent to which zebrafish genes and gene structures are related to orthologous human genes. To examine this, we generated a high-quality sequence assembly of the zebrafish genome, made up of an overlapping set of completely sequenced large-insert clones that were ordered and oriented using a high-resolution high-density meiotic map. Detailed automatic and manual annotation provides evidence of more than 26,000 protein-coding genes, the largest gene set of any vertebrate so far sequenced. Comparison to the human reference genome shows that approximately 70% of human genes have at least one obvious zebrafish orthologue. In addition, the high quality of this genome assembly provides a clearer understanding of key genomic features such as a unique repeat content, a scarcity of pseudogenes, an enrichment of zebrafish-specific genes on chromosome 4 and chromosomal regions that influence sex determination.

Spatial organization of membranes into domains of distinct protein and lipid composition is a fundamental feature of biological systems. The plasma membrane is organized in such domains to efficiently orchestrate the many reactions occurring there simultaneously. Despite the almost universal presence of membrane domains, mechanisms of their formation are often unclear. Yeast cells feature prominent plasma membrane domain organization, which is at least partially mediated by eisosomes. Eisosomes are large protein complexes that are primarily composed of many subunits of two Bin-Amphiphysin-Rvs domain-containing proteins, Pil1 and Lsp1. In this paper, we show that these proteins self-assemble into higher-order structures and bind preferentially to phosphoinositide-containing membranes. Using a combination of electron microscopy approaches, we generate structural models of Pil1 and Lsp1 assemblies, which resemble eisosomes in cells. Our data suggest that the mechanism of membrane organization by eisosomes is mediated by self-assembly of its core components into a membrane-bound protein scaffold with lipid-binding specificity.

Plasma membranes are organized into domains of different protein and lipid composition. Eisosomes are key complexes for yeast plasma membrane organization, containing primarily Pil1 and Lsp1. Here we show that both proteins consist mostly of a banana-shaped BAR domain common to membrane sculpting proteins, most similar to the ones of amphiphysin, arfaptin 2 and endophilin 2. Our data reveal a previously unrecognized family of BAR-domain proteins involved in plasma membrane organization.

Most cellular processes are performed by proteomic units that interact with each other. These units are often stoichiometrically stable complexes comprised of several proteins. To obtain a faithful view of the protein interactome we must view it in terms of these basic units (complexes and proteins) and the interactions between them. This study makes two contributions toward this goal. First, it provides a new algorithm for reconstruction of stable complexes from a variety of heterogeneous biological assays; our approach combines state-of-the-art machine learning methods with a novel hierarchical clustering algorithm that allows clusters to overlap. We demonstrate that our approach constructs over 40% more known complexes than other recent methods and that the complexes it produces are more biologically coherent even compared with the reference set. We provide experimental support for some of our novel predictions, identifying both a new complex involved in nutrient starvation and a new component of the eisosome complex. Second, we provide a high accuracy algorithm for the novel problem of predicting transient interactions involving complexes. We show that our complex level network, which we call ComplexNet, provides novel insights regarding the protein-protein interaction network. In particular, we reinterpret the finding that "hubs" in the network are enriched for being essential, showing instead that essential proteins tend to be clustered together in essential complexes and that these essential complexes tend to be large.