Berend Snel

Comparative assessment of large-scale data sets of protein–protein interactions

Comprehensive protein–protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions.

Toward automatic reconstruction of a highly resolved tree of life

We have developed an automatable procedure for reconstructing the tree of life with branch lengths comparable across all three domains. The tree has its basis in a concatenation of 31 orthologs occurring in 191 species with sequenced genomes. It revealed interdomain discrepancies in taxonomic classification. Systematic detection and subsequent exclusion of products of horizontal gene transfer increased phylogenetic resolution, allowing us to confirm accepted relationships and resolve disputed and preliminary classifications. For example, we place the phylum Acidobacteria as a sister group of δ-Proteobacteria, support a Gram-positive origin of Bacteria, and suggest a thermophilic last universal common ancestor.

Conservation of gene order: a fingerprint of proteins that physically interact

A systematic comparison of nine bacterial and archaeal genomes reveals a low level of gene-order (and operon architecture) conservation. Nevertheless, a number of gene pairs are conserved. The proteins encoded by conserved gene pairs appear to interact physically. This observation can therefore be used to predict functions of, and interactions between, prokaryotic gene products.

The genome sequence of Bifidobacterium longum reflects its adaptation to the human gastrointestinal tract

Bifidobacteria are Gram-positive prokaryotes that naturally colonize the human gastrointestinal tract (GIT) and vagina. Although not numerically dominant in the complex intestinal microflora, they are considered as key commensals that promote a healthy GIT. We determined the 2.26-Mb genome sequence of an infant-derived strain of Bifidobacterium longum, and identified 1,730 possible coding sequences organized in a 60%–GC circular chromosome. Bioinformatic analysis revealed several physiological traits that could partially explain the successful adaptation of this bacteria to the colon. An unexpectedly large number of the predicted proteins appeared to be specialized for catabolism of a variety of oligosaccharides, some possibly released by rare or novel glycosyl hydrolases acting on “nondigestible” plant polymers or host-derived glycoproteins and glycoconjugates. This ability to scavenge from a large variety of nutrients likely contributes to the competitiveness and persistence of bifidobacteria in the colon. Many genes for oligosaccharide metabolism were found in self-regulated modules that appear to have arisen in part from gene duplication or horizontal acquisition. Complete pathways for all amino acids, nucleotides, and some key vitamins were identified; however, routes for Asp and Cys were atypical. More importantly, genome analysis provided insights into the reciprocal interactions of bifidobacteria with their hosts. We identified polypeptides that showed homology to most major proteins needed for production of glycoprotein-binding fimbriae, structures that could possibly be important for adhesion and persistence in the GIT. We also found a eukaryotic-type serine protease inhibitor (serpin) possibly involved in the reported immunomodulatory activity of bifidobacteria.

STRING: known and predicted protein–protein associations, integrated and transferred across organisms

A full description of a proteins function requires knowledge of all partner proteins with which it specifically associates. From a functional perspective, ‘association’ can mean direct physical binding, but can also mean indirect interaction such as participation in the same metabolic pathway or cellular process. Currently, information about protein association is scattered over a wide variety of resources and model organisms. STRING aims to simplify access to this information by providing a comprehensive, yet quality-controlled collection of protein–protein associations for a large number of organisms. The associations are derived from high-throughput experimental data, from the mining of databases and literature, and from predictions based on genomic context analysis. STRING integrates and ranks these associations by benchmarking them against a common reference set, and presents evidence in a consistent and intuitive web interface. Importantly, the associations are extended beyond the organism in which they were originally described, by automatic transfer to orthologous protein pairs in other organisms, where applicable. STRING currently holds 730 000 proteins in 180 fully sequenced organisms, and is available at http://string.embl.de/.

Deciphering the evolution and metabolism of an anammox bacterium from a community genome

Anaerobic ammonium oxidation (anammox) has become a main focus in oceanography and wastewater treatment. It is also the nitrogen cycles major remaining biochemical enigma. Among its features, the occurrence of hydrazine as a free intermediate of catabolism, the biosynthesis of ladderane lipids and the role of cytoplasm differentiation are unique in biology. Here we use environmental genomics—the reconstruction of genomic data directly from the environment—to assemble the genome of the uncultured anammox bacterium Kuenenia stuttgartiensis from a complex bioreactor community. The genome data illuminate the evolutionary history of the Planctomycetes and allow us to expose the genetic blueprint of the organisms special properties. Most significantly, we identified candidate genes responsible for ladderane biosynthesis and biological hydrazine metabolism, and discovered unexpected metabolic versatility.

Genome phylogeny based on gene content

Species phylogenies derived from comparisons of single genes are rarely consistent with each other, due to horizontal gene transfer, unrecognized paralogy and highly variable rates of evolution. The advent of completely sequenced genomes allows the construction of a phylogeny that is less sensitive to such inconsistencies and more representative of whole-genomes than are single-gene trees. Here, we present a distance-based phylogeny constructed on the basis of gene content, rather than on sequence identity, of 13 completely sequenced genomes of unicellular species. The similarity between two species is defined as the number of genes that they have in common divided by their total number of genes. In this type of phylogenetic analysis, evolutionary distance can be interpreted in terms of evolutionary events such as the acquisition and loss of genes, whereas the underlying properties (the gene content) can be interpreted in terms of function. As such, it takes a position intermediate to phylogenies based on single genes and phylogenies based on phenotypic characteristics. Although our comprehensive genome phylogeny is independent of phylogenies based on the level of sequence identity of individual genes, it correlates with the standard reference of prokarytic phylogeny based on sequence similarity of 16s rRNA (ref. 4). Thus, shared gene content between genomes is quantitatively determined by phylogeny, rather than by phenotype, and horizontal gene transfer has only a limited role in determining the gene content of genomes.

STRING 7—recent developments in the integration and prediction of protein interactions

Information on protein–protein interactions is still mostly limited to a small number of model organisms, and originates from a wide variety of experimental and computational techniques. The database and online resource STRING generalizes access to protein interaction data, by integrating known and predicted interactions from a variety of sources. The underlying infrastructure includes a consistent body of completely sequenced genomes and exhaustive orthology classifications, based on which interaction evidence is transferred between organisms. Although primarily developed for protein interaction analysis, the resource has also been successfully applied to comparative genomics, phylogenetics and network studies, which are all facilitated by programmatic access to the database backend and the availability of compact download files. As of release 7, STRING has almost doubled to 373 distinct organisms, and contains more than 1.5 million proteins for which associations have been pre-computed. Novel features include AJAX-based web-navigation, inclusion of additional resources such as BioGRID, and detailed protein domain annotation. STRING is available at

Predicting disease genes using protein–protein interactions

Background: The responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to similar disease phenotypes. Objective: To investigate whether protein–protein interactions can predict genes for genetically heterogeneous diseases. Methods: 72 940 protein–protein interactions between 10 894 human proteins were used to search 432 loci for candidate disease genes representing 383 genetically heterogeneous hereditary diseases. For each disease, the protein interaction partners of its known causative genes were compared with the disease associated loci lacking identified causative genes. Interaction partners located within such loci were considered candidate disease gene predictions. Prediction accuracy was tested using a benchmark set of known disease genes. Results: Almost 300 candidate disease gene predictions were made. Some of these have since been confirmed. On average, 10% or more are expected to be genuine disease genes, representing a 10-fold enrichment compared with positional information only. Examples of interesting candidates are AKAP6 for arrythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy with variable foci. Conclusions: Exploiting protein–protein interactions can greatly increase the likelihood of finding positional candidate disease genes. When applied on a large scale they can lead to novel candidate gene predictions.

The identification of functional modules from the genomic association of genes

By combining the pairwise interactions between proteins, as predicted by the conserved co-occurrence of their genes in operons, we obtain protein interaction networks. Here we study the properties of such networks to identify functional modules: sets of proteins that together are involved in a biological process. The complete network contains 3,033 orthologous groups of proteins in 38 genomes. It consists of one giant component, containing 1,611 orthologous groups, and of 516 small disjointed clusters that, on average, contain only 2.7 orthologous groups. These small clusters have a homogeneous functional composition and thus represent functional modules in themselves. Analysis of the giant component reveals that it is a scale-free, small-world network with a high degree of local clustering (C = 0.6). It consists of locally highly connected subclusters that are connected to each other by linker proteins. The linker proteins tend to have multiple functions, or are involved in multiple processes and have an above average probability of being essential. By splitting up the giant component at these linker proteins, we identify 265 subclusters that tend to have a homogeneous functional composition. The rare functional inhomogeneities in our subclusters reflect the mixing of different types of (molecular) functions in a single cellular process, exemplified by subclusters containing both metabolic enzymes as well as the transcription factors that regulate them. Comparative genome analysis, thus, allows identification of a level of functional interaction between that of pairwise interactions, and of the complete genome.