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Background: As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required., Results: We examined the yeast interactome from a new perspective, by taking into account how thoroughly proteins have been studied. We discovered that the set of literature-curated protein-protein interactions is qualitatively different when restricted to proteins that have received extensive attention from the scientific community. In particular, these interactions are less often supported by yeast two-hybrid, and more often by more complex experiments such as biochemical activity assays. Our analysis showed that high-throughput and literature-curated interactome datasets are more correlated than commonly assumed, but that this bias can be corrected for by focusing on well-studied proteins. We thus propose a simple and reliable method to estimate the size of an interactome, combining literature-curated data involving well-studied proteins with high-throughput data. It yields an estimate of at least 37, 600 direct physical protein-protein interactions in S. cerevisiae., Conclusions: Our method leads to higher and more accurate estimates of the interactome size, as it accounts for interactions that are genuine yet difficult to detect with commonly-used experimental assays. This shows that we are even further from completing the yeast interactome map than previously expected.

Biological systems are made up of very large numbers of different components interacting at various scales. Most genes, proteins and other cell components carry out their functions within a complex network of interactions and a single component can affect a wide range of other components. Interactions involved in biological processes have been first characterized individually but this "reductionist" approach suffers from a lack of information about time, space, and context in which the interactions occur in vivo. A global, integrative, approach has been developed for several years, focusing on the building of protein-protein interaction maps or interactomes. These interaction networks are complex systems, where new properties arise. They are part of the emergent field of systems biology, which focuses on studying complex biological systems such as a cell or organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions. Aging is associated with many diseases, such as cancer, diabetes, cardiovascular and neurodegenerative disorders and this limits the investigation of the mechanisms underlying the aging process when focusing on a single gene or a single biochemical pathway. The integration of existing intracellular interaction networks with the extracellular interaction network we have developed (MatrixDB, http://matrixdb.ibcp.fr ) will contribute to provide further insights into the global mechanisms of aging.

"Smart-pooling," in which test reagents are multiplexed in a highly redundant manner, is a promising strategy for achieving high efficiency, sensitivity, and specificity in systems-level projects. However, previous applications relied on low redundancy designs that do not leverage the full potential of smart-pooling, and more powerful theoretical constructions, such as the Shifted Transversal Design (STD), lack experimental validation. Here we evaluate STD smart-pooling in yeast two-hybrid (Y2H) interactome mapping. We employed two STD designs and two established methods to perform ORFeome-wide Y2H screens with 12 baits. We found that STD pooling achieves similar levels of sensitivity and specificity as one-on-one array-based Y2H, while the costs and workloads are divided by three. The screening-sequencing approach is the most cost- and labor-efficient, yet STD identifies about twofold more interactions. Screening-sequencing remains an appropriate method for quickly producing low-coverage interactomes, while STD pooling appears as the method of choice for obtaining maps with higher coverage.

The idea that genes and their products are the fundamental units of biology has profoundly influenced our scientific thinking during the second half of the past century. Today, this reductionism is challenged by a renaissance of a systems understanding of biology, focusing on the systems formed by interacting gene products rather than on individual gene products. This discipline, based on a complementary and more holistic approach, keeps expanding its scope thanks to biotechnological innovations as well as theoretical modeling. This review aims at showing how and why, since the beginning of the 21st century, in fundamental as well as biomedical research, systems biology is proving a promising paradigm for understanding emerging properties of complex biological systems.

Most genes, proteins and other components carry out their functions within a complex network of interactions and a single molecule can affect a wide range of other cell components. A global, integrative, approach has been developed for several years, including protein-protein interaction networks (interactomes). In this review, we describe the high-throughput methods used to identify new interactions and to build large interaction datasets. The minimum information required for reporting a molecular interaction experiment (MIMIx) has been defined as a standard for storing data in publicly available interaction databases. Several examples of interaction networks from molecular machines (proteasome) or organelles (phagosome, mitochondrion) to whole organisms (viruses, bacteria, yeast, fly, and worm) are given and attempts to cover the entire human interaction network are discussed. The methods used to perform the topological analysis of interaction networks and to extract biological information from them are presented. These investigations have provided clues on protein functions, signalling and metabolic pathways, and physiological processes, unraveled the molecular basis of some diseases (cancer, infectious diseases), and will be very useful to identify new therapeutic targets and for drug discovery. A major challenge is now to integrate data from different sources (interactome, transcriptome, phenome, localization) to switch from static to dynamic interaction networks. The merging of a viral interactome and the human interactome has been used to simulate viral infection, paving the way for future studies aiming at providing molecular basis of human diseases.

Obesity is associated with chronic inflammation in adipose tissue. Proinflammatory cytokines including tumor necrosis factor-alpha and interleukin-6 secreted by adipose tissue during the metabolic syndrome are proposed to cause local and general insulin resistance and promote development of type 2 diabetes. We have used a compound mutant mouse, Apoe(-/-)xCD4dnTGFbR, with dysregulation of T-cell activation, excessive production of proinflammatory cytokines, hyperlipidemia, and atherosclerosis, to dissect the role of inflammation in adipose tissue metabolism. These mice are lean, which avoids confounding effects of concomitant obesity. Expression and secretion of a set of proinflammatory factors including tumor necrosis factor-alpha, interferon-gamma, and monocyte chemoattractant protein-1 was increased in adipose tissue of Apoe(-/-)xCD4dnTGFbR mice, as was the enzyme 11beta-hydroxysteroid dehydrogenase type 1, which converts cortisone to bioactive cortisol. Interleukin-6, which has an inhibitory glucocorticoid response element in its promoter, was not upregulated. In spite of intense local inflammation, insulin sensitivity was not impaired in adipose tissue of Apoe(-/-)xCD4dnTGFbR mice unless exogenous interleukin-6 was administered. In conclusion, T-cell activation causes inflammation in adipose tissue but does not lead to insulin resistance in this tissue in the absence of interleukin-6.

Summary: MatrixDB (http://matrixdb.ibcp.fr) is a database reporting mammalian protein-protein and protein-carbohydrate interactions involving extracellular molecules. It takes into account the full interaction repertoire of the extracellular matrix involving full-length molecules, fragments and multimers. The current version of MatrixDB contains 1972 interactions corresponding to 4412 experiments and involving 259 extracellular biomolecules., Availability: MatrixDB is freely available at http://matrixdb.ibcp.fr

Motivation: In high-throughput projects aiming to identify rare positives using a binary assay, smart-pooling constitutes an appealing strategy liable of significantly reducing the number of tests while correcting for experimental noise. In order to perform simulations for choosing an appropriate set of pools, and later to interpret the experimental results, the pool outcomes must be 'decoded'. The intuitive aim is clearly to identify the positives that gave rise to an observation, whether real or simulated. However, this goal is not well-formalized and has been the focus of very few studies., Results: We first provide a clear combinatorial formalization of the 'decoding problem'. We then present interpool, an exact algorithm to solve this problem. An efficient implementation is freely available. Its usefulness is illustrated in the context of yeast-two-hybrid interactome mapping with the Shifted Transversal Design., Availability: The implementation, licensed under the GNU GPL, can be downloaded from http://www-timc.imag.fr/Nicolas.Thierry-Mieg/.

Background: In binary high-throughput screening projects where the goal is the identification of low-frequency events, beyond the obvious issue of efficiency, false positives and false negatives are a major concern. Pooling constitutes a natural solution: it reduces the number of tests, while providing critical duplication of the individual experiments, thereby correcting for experimental noise. The main difficulty consists in designing the pools in a manner that is both efficient and robust: few pools should be necessary to correct the errors and identify the positives, yet the experiment should not be too vulnerable to biological shakiness. For example, some information should still be obtained even if there are slightly more positives or errors than expected. This is known as the group testing problem, or pooling problem., Results: In this paper, we present a new non-adaptive combinatorial pooling design: the "shifted transversal design" (STD). It relies on arithmetics, and rests on two intuitive ideas: minimizing the co-occurrence of objects, and constructing pools of constant-sized intersections. We prove that it allows unambiguous decoding of noisy experimental observations. This design is highly flexible, and can be tailored to function robustly in a wide range of experimental settings (i.e., numbers of objects, fractions of positives, and expected error-rates). Furthermore, we show that our design compares favorably, in terms of efficiency, to the previously described non-adaptive combinatorial pooling designs., Conclusion: This method is currently being validated by field-testing in the context of yeast-two-hybrid interactome mapping, in collaboration with Marc Vidal's lab at the Dana Farber Cancer Institute. Many similar projects could benefit from using the Shifted Transversal Design.

The ubiquitin-proteasome proteolytic pathway is pivotal in most biological processes. Despite a great level of information available for the eukaryotic 26S proteasome-the protease responsible for the degradation of ubiquitylated proteins-several structural and functional questions remain unanswered. To gain more insight into the assembly and function of the metazoan 26S proteasome, a two-hybrid-based protein interaction map was generated using 30 Caenorhabditis elegans proteasome subunits. The results recapitulate interactions reported for other organisms and reveal new potential interactions both within the 19S regulatory complex and between the 19S and 20S subcomplexes. Moreover, novel potential proteasome interactors were identified, including an E3 ubiquitin ligase, transcription factors, chaperone proteins and other proteins not yet functionally annotated. By providing a wealth of novel biological hypotheses, this interaction map constitutes a framework for further analysis of the ubiquitin-proteasome pathway in a multicellular organism amenable to both classical genetics and functional genomics.

The genome sequences of Caenorhabditis elegans, Drosophila melanogaster and Arabidopsis thaliana have been predicted to contain 19,000, 13,600 and 25,500 genes, respectively. Before this information can be fully used for evolutionary and functional studies, several issues need to be addressed. First, the gene number estimates obtained in silico and not yet supported by any experimental data need to be verified. For example, it seems biologically paradoxical that C. elegans would have 50% more genes than Drosophilia. Second, intron/exon predictions need to be tested experimentally. Third, complete sets of open reading frames (ORFs), or "ORFeomes," need to be cloned into various expression vectors. To address these issues simultaneously, we have designed and applied to C. elegans the following strategy. Predicted ORFs are amplified by PCR from a highly representative cDNA library using ORF-specific primers, cloned by Gateway recombination cloning and then sequenced to generate ORF sequence tags (OSTs) as a way to verify identity and splicing. In a sample (n=1,222) of the nearly 10,000 genes predicted ab initio (that is, for which no expressed sequence tag (EST) is available so far), at least 70% were verified by OSTs. We also observed that 27% of these experimentally confirmed genes have a structure different from that predicted by GeneFinder. We now have experimental evidence that supports the existence of at least 17,300 genes in C. elegans. Hence we suggest that gene counts based primarily on ESTs may underestimate the number of genes in human and in other organisms.

Protein interaction mapping using large-scale two-hybrid analysis has been proposed as a way to functionally annotate large numbers of uncharacterized proteins predicted by complete genome sequences. This approach was examined in Caenorhabditis elegans, starting with 27 proteins involved in vulval development. The resulting map reveals both known and new potential interactions and provides a functional annotation for approximately 100 uncharacterized gene products. A protein interaction mapping project is now feasible for C. elegans on a genome-wide scale and should contribute to the understanding of molecular mechanisms in this organism and in human diseases.