The Modular Organization of Protein Interactions in Escherichia coli.

Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism, signalling and gene expression. Understanding the function and organization of proteins within these processes is an important step towards a 'systems' view of E. coli. Integrating experimental and computational interaction data, we present a reliable network of 3,989 functional interactions between 1,941 E. coli proteins ( approximately 45% of its proteome). These were combined with a recently generated set of 3,888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules. In addition to known protein complexes (e.g., RNA and DNA polymerases), we identified modules that represent biochemical pathways (e.g., nitrate regulation and cell wall biosynthesis) as well as batteries of functionally and evolutionarily related processes. To aid the interpretation of modular relationships, several case examples are presented, including both well characterized and novel biochemical systems. Together these data provide a global view of the modular organization of the E. coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks.

The conservation and evolutionary modularity of metabolism.

BACKGROUND: Cellular metabolism is a fundamental biological system consisting of myriads of enzymatic reactions that together fulfill the basic requirements of life. The recent availability of vast amounts of sequence data from diverse sets of organisms provides an opportunity to systematically examine metabolism from a comparative perspective. Here we supplement existing genome and protein resources with partial genome datasets derived from 193 eukaryotes to present a comprehensive survey of the conservation of metabolism across 26 taxa representing the three domains of life. RESULTS: In general, metabolic enzymes are highly conserved. However, organizing these enzymes within the context of functional pathways revealed a spectrum of conservation from those that are highly conserved (for example, carbohydrate, energy, amino acid and nucleotide metabolism enzymes) to those specific to individual taxa (for example, those involved in glycan metabolism and secondary metabolite pathways). Applying a novel co-conservation analysis, KEGG defined pathways did not generally display evolutionary coherence. Instead, such modularity appears restricted to smaller subsets of enzymes. Expanding analyses to a global metabolic network revealed a highly conserved, but nonetheless flexible, 'core' of enzymes largely involved in multiple reactions across different pathways. Enzymes and pathways associated with the periphery of this network were less well conserved and associated with taxon-specific innovations. CONCLUSIONS: These findings point to an emerging picture in which a core of enzyme activities involving amino acid, energy, carbohydrate and lipid metabolism have evolved to provide the basic functions required for life. However, the precise complement of enzymes associated within this core for each species is flexible.

The Symbiosis Interactome: a computational approach reveals novel components, functional interactions and modules in Sinorhizobium meliloti.

BACKGROUND: Rhizobium-Legume symbiosis is an attractive biological process that has been studied for decades because of its importance in agriculture. However, this system has undergone extensive study and although many of the major factors underpinning the process have been discovered using traditional methods, much remains to be discovered. RESULTS: Here we present an analysis of the 'Symbiosis Interactome' using novel computational methods in order to address the complex dynamic interactions between proteins involved in the symbiosis of the model bacteria Sinorhizobium meliloti with its plant hosts. Our study constitutes the first large-scale analysis attempting to reconstruct this complex biological process, and to identify novel proteins involved in establishing symbiosis. We identified 263 novel proteins potentially associated with the Symbiosis Interactome. The topology of the Symbiosis Interactome was used to guide experimental techniques attempting to validate novel proteins involved in different stages of symbiosis. The contribution of a set of novel proteins was tested analyzing the symbiotic properties of several S. meliloti mutants. We found mutants with altered symbiotic phenotypes suggesting novel proteins that provide key complementary roles for symbiosis. CONCLUSION: Our 'systems-based model' represents a novel framework for studying host-microbe interactions, provides a theoretical basis for further experimental validations, and can also be applied to the study of other complex processes such as diseases.

Phylogenomic analysis of EST datasets.

To date the genomes of over 600 organisms have been generated of which 100 are from eukaryotes. Together with partial genome data for an additional 700 eukaryotic organisms, these exceptional sequence resources offer new opportunities to explore phylogenetic relationships and species diversity. The identification of highly diverse sequences specific to an EST-based sequence dataset offers insights into the extent of genetic novelty within that dataset. Sequences that are only shared with other related species from the same taxon might represent genes associated with taxon-specific innovations. On the other hand, sequences that are highly conserved across many other species offer valuable resources for performing more in-depth phylogenetic analyses. In the following chapter, we guide the reader through the process of examining their sequence datasets in the context of phylogenetic relationships. Performed across large-scale datasets, such analyses are termed Phylogenomics. Two complementary approaches are described, both based on the use of BLAST similarity metrics. The first uses an established Java tool - SimiTri - to visualize sequence similarity relationships between the EST dataset and three user-defined datasets. The second focuses on the use of phylogenetic profiles to identify groups of taxonomically related sequences.

Phylogenetic profiling.

Phylogenetic profiles describe the presence or absence of a protein in a set of reference genomes. Similarity between profiles is an indicator of functional coupling between gene products: the greater the similarity, the greater the likelihood of proteins sharing membership in the same pathway or cellular system. By virtue of this property, uncharacterized proteins can be assigned putative functions, based on the similarity of their profiles with those of known proteins. Profile comparisons, when extended to the entire genome, have the power to reveal functional linkages on a genome-wide scale (the functional "interactome"), elucidating both known and novel pathways and cellular systems.

Inferring ancestral protein interaction networks.

With the recent sequencing of numerous complete genomes and the advent of high throughput technologies (e.g., yeast two-hybrid assays or tandem-affinity purification experiments), it is now possible to estimate the ancestral form of protein interaction networks. This chapter combines protein interaction data and comparative genomics techniques in an attempt to reconstruct a network of core proteins and interactions in yeast that potentially represents an ancestral state of the budding yeast protein interaction network.

Bacteriome.org--an integrated protein interaction database for E. coli.

High throughput methods are increasingly being used to examine the functions and interactions of gene products on a genome-scale. These include systematic large-scale proteomic studies of protein complexes and protein-protein interaction networks, functional genomic studies examining patterns of gene expression and comparative genomics studies examining patterns of conservation. Since these datasets offer different yet highly complementary perspectives on cell behavior it is expected that integration of these datasets will lead to conceptual advances in our understanding of the fundamental design and evolutionary principles that underlie the organization and function of proteins within biochemical pathways. Here we present Bacteriome.org, a resource that combines locally generated interaction and evolutionary datasets with a previously generated knowledgebase, to provide an integrated view of the Escherichia coli interactome. Tools are provided which allow the user to select and visualize functional, evolutionary and structural relationships between groups of interacting proteins and to focus on genes of interest. Currently the database contains three interaction datasets: a functional dataset consisting of 3989 interactions between 1927 proteins; a 'core' high quality experimental dataset of 4863 interactions between 1100 proteins and an 'extended' experimental dataset of 9860 interactions between 2131 proteins. Bacteriome.org is available online at http://www.bacteriome.org.

The comparative genomics of protein interactions.

The detection of gene fusion events across genomes can be used for the prediction of functional associations of proteins, including physical interactions or complex formation. These predictions are obtained by the detection of similarity for pairs of 'component' proteins to 'composite' proteins. Since the amount of composite proteins is limited in nature, we augment this set by creating artificial fusion proteins from experimentally determined protein interacting pairs. The goal is to study the extent of protein interaction partners with increasing phylogenetic distance, using an automated method. We have thus detected component pairs within seven entire genome sequences of similar size, using artificially generated composite proteins that have been shown to interact experimentally. Our results indicate that protein interactions are not conserved over large phylogenetic distances. In addition, we provide a set of predictions for functionally associated proteins across seven species using experimental information and demonstrate the applicability of fusion analysis for the comparative genomics of protein interactions.

Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.

Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

CoGenT++: an extensive and extensible data environment for computational genomics.

MOTIVATION: CoGenT++ is a data environment for computational research in comparative and functional genomics, designed to address issues of consistency, reproducibility, scalability and accessibility. DESCRIPTION: CoGenT++ facilitates the re-distribution of all fully sequenced and published genomes, storing information about species, gene names and protein sequences. We describe our scalable implementation of ProXSim, a continually updated all-against-all similarity database, which stores pairwise relationships between all genome sequences. Based on these similarities, derived databases are generated for gene fusions--AllFuse, putative orthologs--OFAM, protein families--TRIBES, phylogenetic profiles--ProfUse and phylogenetic trees. Extensions based on the CoGenT++ environment include disease gene prediction, pattern discovery, automated domain detection, genome annotation and ancestral reconstruction. CONCLUSION: CoGenT++ provides a comprehensive environment for computational genomics, accessible primarily for large-scale analyses as well as manual browsing.

An exponential core in the heart of the yeast protein interaction network.

Protein interactions in the budding yeast have been shown to form a scale-free network, a feature of other organized networks such as bacterial and archaeal metabolism and the World Wide Web. Here, we study the connections established by yeast proteins and discover a preferential attachment between essential proteins. The essential-essential connections are long ranged and form a subnetwork where the giant component includes 97% of these proteins. Unexpectedly, this subnetwork displays an exponential connectivity distribution, in sharp contrast to the scale-free topology of the complete network. Furthermore, the wide phylogenetic extent of these core proteins and interactions provides evidence that they represent the ancestral state of the yeast protein interaction network. Finally, we propose that this core exponential network may represent a generic scaffold around which organism-specific and taxon-specific proteins and interactions coalesce.

PartiGeneDB--collating partial genomes.

Owing to the high costs involved, only 28 eukaryotic genomes have been fully sequenced to date. On the other hand, an increasing number of projects have been initiated to generate survey sequence data for a large number of other eukaryotic organisms. For the most part, these data are poorly organized and difficult to analyse. Here, we present PartiGeneDB (http://www.partigenedb.org), a publicly available database resource, which collates and processes these sequence datasets on a species-specific basis to form non-redundant sets of gene objects-which we term partial genomes. Users may query the database to identify particular genes of interest either on the basis of sequence similarity or via the use of simple text searches for specific patterns of BLAST annotation. Alternatively, users can examine entire partial genome datasets on the basis of relative expression of gene objects or by the use of an interactive Java-based tool (SimiTri), which displays sequence similarity relationships for a large number of sequence objects in a single graphic. PartiGeneDB facilitates regular incremental updates of new sequence datasets associated with both new and exisitng species. PartiGeneDB currently contains the assembled partial genomes derived from 1.83 million sequences associated with 247 different eukaryotes.