Systematic interactome mapping and genetic perturbation analysis of a C. elegans TGF-beta signaling network

Integrating physical and genetic maps: from genomes to interaction networks

Physical and genetic mapping data have become as important to network biology as they once were to the Human Genome Project. Integrating physical and genetic networks currently faces several challenges: increasing the coverage of each type of network; establishing methods to assemble individual interaction measurements into contiguous pathway models; and annotating these pathways with detailed functional information. A particular challenge involves reconciling the wide variety of interaction types that are currently available. For this purpose, recent studies have sought to classify genetic and physical interactions along several complementary dimensions, such as ordered versus unordered, alleviating versus aggravating, and first versus second degree.

Caenorhabditis elegans SDF-9 enhances insulin/insulin-like signaling through interaction with DAF-2

SDF-9 is a modulator of Caenorhabditis elegans insulin/IGF-1 signaling that may interact directly with the DAF-2 receptor. SDF-9 is a tyrosine phosphatase-like protein that, when mutated, enhances many partial loss-of-function mutants in the dauer pathway except for the temperature-sensitive mutant daf-2(m41). We propose that SDF-9 stabilizes the active phosphorylated state of DAF-2 or acts as an adaptor protein to enhance insulin-like signaling.

Arkadia induces degradation of SnoN and c-Ski to enhance transforming growth factor-beta signaling

Transforming growth factor-beta (TGF-beta) signaling is controlled by a variety of regulators that target either signaling receptors or activated Smad complexes. Among the negative regulators, Smad7 antagonizes TGF-beta signaling mainly through targeting the signaling receptors, whereas SnoN and c-Ski repress signaling at the transcriptional level through inactivation of Smad complexes. We previously found that Arkadia is a positive regulator of TGF-beta signaling that induces ubiquitin-dependent degradation of Smad7 through its C-terminal RING domain. We report here that Arkadia induces degradation of SnoN and c-Ski in addition to Smad7. Arkadia interacts with SnoN and c-Ski in their free forms as well as in the forms bound to Smad proteins, and constitutively down-regulates levels of their expression. Arkadia thus appears to effectively enhance TGF-beta signaling through simultaneous down-regulation of two distinct types of negative regulators, Smad7 and SnoN/c-Ski, and may play an important role in determining the intensity of TGF-beta family signaling in target cells.

Natural and artificially induced genetic variability in crop and model plant species for plant systems biology

The sequencing of plant genomes which was completed a few years ago for Arabidopsis thaliana and Oryza sativa is currently underway for numerous crop plants of commercial value such as maize, poplar, tomato grape or tobacco. In addition, hundreds of thousands of expressed sequence tags (ESTs) are publicly available that may well represent 40-60% of the genes present in plant genomes. Despite its importance for life sciences, genome information is only an initial step towards understanding gene function (functional genomics) and deciphering the complex relationships between individual genes in the framework of gene networks. In this chapter we introduce and discuss means of generating and identifying genetic diversity, i.e., means to genetically perturb a biological system and to subsequently analyse the systems response, e.g., the changes in plant morphology and chemical composition. Generating and identifying genetic diversity is in its own right a highly powerful resource of information and is established as an invaluable tool for systems biology.

Proteomics of TGF-beta signaling and its impact on breast cancer

The complexity of mechanisms leading to the appearance and progression of cancer is a challenge being addressed by large-scale studies, such as proteomics. Simultaneous monitoring of thousands of proteins uncovers novel signaling mechanisms, thus revising our knowledge of tumorigenesis. Transforming growth factor (TGF)-beta is a secreted polypeptide that is known to inhibit tumor growth at the early stages of cancer, but promote metastasis at the later stages. Proteomics-based studies have significantly widened our knowledge of TGF-beta-dependent regulation of cell proliferation, apoptosis, DNA damage repair and transcription. This leads to better understanding of the TGF-beta role in human breast tumorigenesis, and opens the way for the development of novel anticancer treatments and drugs, with some of the drugs already entering clinics. This review discusses recent advances in proteomics studies of TGF-beta signaling and its contribution to the understanding and treatment of breast cancer.

From protein-protein complexes to interactomics

Protein-protein interactions (or PPIs) are key elements for the normal functioning of a living cell. A large description of the protein interactomics field is given in this review where different aspects will be discussed. We first give an introduction of the different large scale experimental approaches from yeast two-hybrid to mass spectrometry used to discover PPIs and build protein interaction maps. Single PPI validation techniques such as co-immunoprecipitation or fluorescence methods are then presented as they are more and more integrated in global PPI discovery strategy. Data from different experimental sets are compared and an assessment of the different large scale technologies is presented. Bioinformatics tools can also predict with a good accuracy PPIs in silico, PPIs databases are now numerous and topological analysis has led to interesting insights into the nature of network connection. Finally, PPI, as an association of two proteins, has been structurally characterized for many protein complexes and is largely discussed throughout existing examples. The results obtained so far already provide the biologist with a large set of structured data from which knowledge on pathways and associated protein function can be extracted.

Connexin43 and the brain transcriptome of newborn mice

Our previously reported cDNA array datasets from neonatal wild-type and Cx43-/- (approved gene symbol Gja1) mouse brains were further analyzed to identify underlying interlinkages in the brain transcriptome. The analysis revealed that no gene cohort sharing either primary function or chromosomal location was significantly altered (up-and down-regulation were roughly balanced) in Cx43-/- brains, but each cohort exhibited significant perturbation of transcript abundance proportions and reduced expression variability and coordination. By comparing pairwise expression correlations of all genes with one another in wild-type brains, we found genes exhibiting remarkable similarity or opposition to the coordination profile (set of synergistically, antagonistically, and independently expressed partners) of Cx43, one of the most similar being pannexin1, a vertebrate homolog of invertebrate gap junction proteins. This study indicates striking redundancy of expression controls over functional pathways and suggests that certain genes may play roles similar to or opposite that of Cx43 in organizing the brain transcriptome.

Protein interaction networks in plants

Protein-protein interactions are fundamental to virtually every aspect of cellular functions. With the development of high-throughput technologies of both the yeast two-hybrid system and tandem mass spectrometry, genome-wide protein-linkage mapping has become a major objective in post-genomic research. While at least partial "interactome" networks of several model organisms are already available, in the plant field, progress in this respect is slow. However, even with comprehensive protein interaction data still missing, substantial recent advance in the graph-theoretical functional interpretation of complex network architectures might pave the way for novel approaches in plant research. This article reviews current progress and discussions in network biology. Emphasis is put on the question of what can be learned about protein functions and cellular processes by studying the topology of complex protein interaction networks and the evolutionary mechanisms underlying their development. Particularly the intermediate and local levels of network organization--the modules, motifs and cliques--are increasingly recognized as the operational units of biological functions. As demonstrated by some recent results from systematic analyses of plant protein families, protein interaction networks promise to be a valuable tool for a molecular understanding of functional specificities and for identifying novel regulatory components and pathways.

Yeast two-hybrid contributions to interactome mapping

Interactome mapping, the systematic identification of protein interactions within an organism, promises to facilitate systems-level studies of biological processes. Using in vitro technologies that measure specific protein interactions, static maps are being generated that include many of the protein networks that occur in vivo. Most of the binary protein interaction data currently available was generated by large-scale yeast two-hybrid screens. Recent efforts to map interactions in model organisms and in humans illustrate the promise and some of the limitations of the two-hybrid approach. Although these maps are incomplete and include false positives, they are proving useful as a framework around which to elaborate and model the in vivo interactome.

Two membrane-associated tyrosine phosphatase homologs potentiate C. elegans AKT-1/PKB signaling

Akt/protein kinase B (PKB) functions in conserved signaling cascades that regulate growth and metabolism. In humans, Akt/PKB is dysregulated in diabetes and cancer; in Caenorhabditis elegans, Akt/PKB functions in an insulin-like signaling pathway to regulate larval development. To identify molecules that modulate C. elegans Akt/PKB signaling, we performed a genetic screen for enhancers of the akt-1 mutant phenotype (eak). We report the analysis of three eak genes. eak-6 and eak-5/sdf-9 encode protein tyrosine phosphatase homologs; eak-4 encodes a novel protein with an N-myristoylation signal. All three genes are expressed primarily in the two endocrine XXX cells, and their predicted gene products localize to the plasma membrane. Genetic evidence indicates that these proteins function in parallel to AKT-1 to inhibit the FoxO transcription factor DAF-16. These results define two membrane-associated protein tyrosine phosphatase homologs that may potentiate C. elegans Akt/PKB signaling by cell autonomous and cell nonautonomous mechanisms. Similar molecules may modulate Akt/PKB signaling in human endocrine tissues.

Insulin and insulin-like growth factor (IGF) signaling regulates critical physiological processes in a wide variety of multicellular organisms. In humans, dysregulation of IGF signaling underlies the pathogenesis of cancer and diabetes. In the nematode Caenorhabditis elegans, the DAF-2 insulin-like pathway regulates development, metabolism, and longevity. All known components of DAF-2 insulin-like signaling are structurally and functionally conserved in mammals, suggesting that insights gained from studying this pathway in C. elegans may shed light on pathogenetic mechanisms underlying cancer and diabetes. In this study, the authors describe a genetic screen designed to identify novel components of DAF-2 insulin-like signaling in C. elegans. They have characterized three genes that may encode parts of a novel multimolecular membrane-associated complex that potentiates DAF-2 insulin-like signaling in two neuroendocrine cells, the XXX cells. Two of these genes encode proteins similar to mammalian protein tyrosine phosphatases. These results suggest that protein tyrosine phosphatase–like molecules may transduce IGF signals in mammalian endocrine cells and highlight the role of endocrine circuits in the pathogenesis of cancer and diabetes.

The model organism as a system: integrating 'omics' data sets

Various technologies can be used to produce genome-scale, or 'omics', data sets that provide systems-level measurements for virtually all types of cellular components in a model organism. These data yield unprecedented views of the cellular inner workings. However, this abundance of information also presents many hurdles, the main one being the extraction of discernable biological meaning from multiple omics data sets. Nevertheless, researchers are rising to the challenge by using omics data integration to address fundamental biological questions that would increase our understanding of systems as a whole.

A database and tool, IM Browser, for exploring and integrating emerging gene and protein interaction data for Drosophila

Background

Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets.

Description

Here we describe a comprehensive database of Drosophila gene and protein interactions collected from a variety of sources, including low and high throughput screens, genetic interactions, and computational predictions. We also present a program for exploring multiple interaction data sets and for combining data from different sources. The program, referred to as the Interaction Map (IM) Browser, is a web-based application for searching and visualizing interaction data stored in a relational database system. Use of the application requires no downloads and minimal user configuration or training, thereby enabling rapid initial access to interaction data. IM Browser was designed to readily accommodate and integrate new types of interaction data as it becomes available. Moreover, all information associated with interaction measurements or predictions and the genes or proteins involved are accessible to the user. This allows combined searches and analyses based on either common or technique-specific attributes. The data can be visualized as an editable graph and all or part of the data can be downloaded for further analysis with other tools for specific applications. The database is available at

Conclusion

The Drosophila Interactions Database described here places a variety of disparate data into one easily accessible location. The database has a simple structure that maintains all relevant information about how each interaction was determined. The IM Browser provides easy, complete access to this database and could readily be used to publish other sets of interaction data. By providing access to all of the available information from a variety of data types, the program will also facilitate advanced computational analyses.

Phosphoproteomics in analyzing signaling pathways

A surprisingly fewer than expected number of genes in the human genome suggests that sophistication of its biologic system is, in part, due to complex regulation of protein activities. The activities of most cellular proteins are regulated by post-translational modifications. One of the most important post-translational modifications is reversible protein phosphorylation, which decorates more than 30% of the proteome and regulates signal transduction pathways under normal conditions as well as in disorders such as diabetes, neurodegenerative diseases, autoimmune diseases and several forms of cancers. This review examines the recent developments in mass spectrometry-based methods for phosphoproteome analysis and its applications for the study of signal transduction pathways. The basic principles of non-mass spectrometry-based methods, such as chemical genetics and flow cytometry-based approaches, are also discussed as well as their specific advantages to signaling studies. Finally, signaling pathways are discussed in the light of large-scale protein interaction studies. The proteomic methods addressed in this review are emerging as some of the essential components in systems biology, which seeks to describe signaling networks through integration of diverse types of data and, in the future, to allow computational simulations of complex biologic pathways in health and disease.

Cloned sequence repertoires for small- and large-scale biology

How to assign function to the tens of thousands of genes discovered in the chromosomes of a few model species? How to complement the classical genetic approaches that are not always ideally suited to decode complex mechanisms? The solutions to these pressing questions are not simple and rely on the development of novel resources and technologies. Here I critically review what clone collections are available and how they can be exploited for the systematic analysis of gene functions in plants.

The molecular immunology of mucositis: implications for evidence-based research in alternative and complementary palliative treatments

The terms 'mucositis' and 'stomatitis' are often used interchangeably. Mucositis, however, pertains to pharyngeal-esophago-gastrointestinal inflammation that manifests as red, burn-like sores or ulcerations throughout the mouth. Stomatitis is an inflammation of the oral tissues proper, which can present with or without sores, and is made worse by poor dental hygiene. Mucositis is observed in a variety of immunosuppressed patients, but is most often consequential to cancer therapy. It appears as early as the third day of intervention, and is usually established by Day 7 of treatment. Mucositis increases mortality and morbidity and contributes to rising health care costs. The precise immune components involved in the etiology of mucositis are unclear, but evidence-based research (EBR) data has shown that applications of granulocyte-macrophage-colony stimulating factor prevent the onset or the exacerbation of oropharyngeal mucositis. The molecular implications of this observation are discussed from the perspective of future developments of complementary and alternative treatments for this condition. It must be emphasized that this article is meant to be neither a review on mucositis and the various treatments for it, nor a discussion paper on its underlying molecular immunology. It is a statement of the implications of EBR for CAM-based interventions for mucositis. It explores and discusses the specific domain of molecular immunology in the context of mucositis and its direct implications for EBR research in CAM-based treatments for mucositis.

Microfluidics-based systems biology

Systems biology seeks to develop a complete understanding of cellular mechanisms by studying the functions of intra- and inter-cellular molecular interactions that trigger and coordinate cellular events. However, the complexity of biological systems causes accurate and precise systems biology experimentation to be a difficult task. Most biological experimentation focuses on highly detailed investigation of a single signaling mechanism, which lacks the throughput necessary to reconstruct the entirety of the biological system, while high-throughput testing often lacks the fidelity and detail necessary to fully comprehend the mechanisms of signal propagation. Systems biology experimentation, however, can benefit greatly from the progress in the development of microfluidic devices. Microfluidics provides the opportunity to study cells effectively on both a single- and multi-cellular level with high-resolution and localized application of experimental conditions with biomimetic physiological conditions. Additionally, the ability to massively array devices on a chip opens the door for high-throughput, high fidelity experimentation to aid in accurate and precise unraveling of the intertwined signaling systems that compose the inner workings of the cell.

Two-hybrid and its recent adaptations

Interactions between proteins play a pivotal role in virtually all cellular processes, and many of these interactions represent interesting targets for drug development. Among the wide array of interactor-hunting technologies that has emerged, genetic two-hybrid methods account for a large amount of the currently available interaction data and is being successfully applied in interactome-scale mapping projects. Reverse two-hybrid approaches have been developed that allow selected interactions to be assayed for disrupting compounds.:

Mating worms and the cystic kidney: Caenorhabditis elegans as a model for renal disease

Polycystic kidney disease (PKD) is caused by a group of variably inherited human disorders that are major causes of end-stage renal disease in both children and adults. The genetic culprits responsible for autosomal-dominant PKD (ADPKD), the polycystins, have been identified, yet still little is known about the molecular mechanisms that result in the disease phenotype. Polycystin homologs have been isolated in the model genetic organism Caenorhabditis elegans and, interestingly, play a specific role in C. elegans male mating behavior. Despite the recruitment of the polycystins for divergent functions in worms and humans it appears that the fundamental molecular and genetic interactions of these genes are evolutionarily conserved. In addition, studies in the worm have contributed to an understanding of the emerging role for cilia in the function of the polycystin pathway, expanding a promising frontier in PKD research. C. elegans has also been used to identify a gene family which may have significance for understanding the formation and maintenance of renal tubules.

Probabilistic model of the human protein-protein interaction network

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans-a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.

Interactome: gateway into systems biology

Protein-protein interactions are fundamental to all biological processes, and a comprehensive determination of all protein-protein interactions that can take place in an organism provides a framework for understanding biology as an integrated system. The availability of genome-scale sets of cloned open reading frames has facilitated systematic efforts at creating proteome-scale data sets of protein-protein interactions, which are represented as complex networks or 'interactome' maps. Protein-protein interaction mapping projects that follow stringent criteria, coupled with experimental validation in orthogonal systems, provide high-confidence data sets immanently useful for interrogating developmental and disease mechanisms at a system level as well as elucidating individual protein function and interactome network topology. Although far from complete, currently available maps provide insight into how biochemical properties of proteins and protein complexes are integrated into biological systems. Such maps are also a useful resource to predict the function(s) of thousands of genes.

A pair as a minimum: the two fibroblast growth factors of the nematode Caenorhabditis elegans

Fibroblast growth factors (FGFs) regulate many important developmental and homeostatic physiological events. The FGF superfamily contains several families. In this review, we present recent findings on the two FGFs of the nematode Caenorhabditis elegans from both functional and phylogenic points of view. C. elegans has a single FGFR (EGL-15) with two functionally exclusive isoforms, and two FGFs (LET-756 and EGL-17), which play distinct roles: an essential function for the former, and guidance of the migrating sex myoblasts for the latter. Regulation of homeostasis by control of the fluid balance could be the basis for the essential function of LET-756. Phylogenetic and functional studies suggest that LET-756, like vertebrate FGF9, -16, and -20, belongs to the FGF9 family, whereas EGL-17, like vertebrate FGF8, -17, and -18, could be included in the FGF8 family.

Multiparameter Imaging for the Analysis of Intracellular Signaling

In biological experimentation and especially in drug discovery there is a trend towards more complex test systems. Cell-based assays are replacing conventional binding or enzyme assays more and more. This development is strongly driven by novel fluorescent probes that give insight into cellular processes. Target proteins are studied in their natural environment; this gives much more realistic test results, especially with respect to enzyme location and kinetics. However, in the complex environment of cells, many parameters contribute to the performance of the protein of interest. Therefore, it would be desirable to monitor simultaneously as many of the relevant cellular processes as possible. Here, we discuss the possibilities and limitations provided by multiparameter monitoring of cellular events with fluorescent probes. Some novel examples of the use of fluorescent probes and multiparameter imaging are shown.

New directions in incidence-dose modeling

Many cellular responses are quantal; that is, they either take place or they do not. Examples of "either-or" responses include cell replication, differentiation and apoptosis. Surprisingly, induction of suites of genes and coordinated phenotypic changes in cells are also often quantal, where embedded molecular circuitry creates on-off switches. Mechanistic incidence-dose (ID) models need to account for the quantal characteristics of cellular switches that contribute, in turn, to dose thresholds and to the incidence of biological responses in individuals. Interdisciplinary systems biology approaches create mechanistic ID models based on: (i) detailed knowledge of the cellular circuitry controlling signal transduction; (ii) evolving biological modeling tools describing cellular circuits and their perturbations by chemicals and (iii) high throughput, high coverage "omic" screens for examining cell signaling pathways and biological responses. These interdisciplinary approaches should produce novel, quantitative ID models for biological responses and greatly improve the biological basis of safety and risk assessments.

Hsp90 is essential in the filarial nematode Brugia pahangi

The development of a compound with activity against filarial nematodes (a 'macrofilaricide') has been a long-standing goal of the World Health Organization. However, adult filariae have proved remarkably difficult to kill. To some extent this reflects a lack of understanding of key pathways and processes in filarial nematodes that may be suitable targets for chemotherapy. In this paper we show that geldanamycin (GA), a specific inhibitor of the activity of the heat shock protein 90 (Hsp90) family, kills adult worms and microfilariae (Mf) of Brugia pahangi at nanomolar concentrations. In addition, release of Mf from adult worms is inhibited within 24 h of exposure to GA and is not recoverable, demonstrating that GA effectively sterilises the worm. Similar results were obtained with a second filarial worm Acanthocheilonema viteae. In contrast GA has no effect on the free-living nematode Caenorhabditis elegans despite a high degree of conservation between the nematode Hsp90 sequences. In keeping with these findings, Brugia Hsp90 binds GA in a solid phase pull-down assay while the binding of C. elegans Hsp90 to immobilised GA is undetectable. In other eukaryotes, GA is known to bind in the N-terminal ATP pocket of Hsp90, disrupting its interactions with client proteins which are then targeted for degradation via the proteasome pathway. Thus, Hsp90 or some of its client proteins may provide novel targets for the chemotherapy of filarial infection.

Integrating a functional proteomic approach into the target discovery process

Functional proteomics is a promising technique for the rational identification of novel therapeutic targets by elucidation of the function of newly identified proteins in disease-relevant cellular pathways. Of the recently described high-throughput approaches for analyzing protein-protein interactions, the yeast two-hybrid (Y2H) system has turned out to be one of the most suitable for genome-wide analysis. However, this system presents a challenging technical problem: the high prevalence of false positives and false negatives in datasets due to intrinsic limitations of the technology and the use of a high-throughput, genetic assay. We discuss here the different experimental strategies applied to Y2H assays, their general limitations and advantages. We also address the issue of the contribution of protein interaction mapping to functional biology, especially when combined with complementary genomic and proteomic analyses. Finally, we illustrate how the combination of protein interaction maps with relevant functional assays can provide biological support to large-scale protein interaction datasets and contribute to the identification and validation of potential therapeutic targets.

RNA interference in Caenorhabditis elegans

RNA interference (RNAi) was first discovered in the nematode Caenorhabditis elegans (Fire et al., 1998; Guo and Kemphues, 1995). The completion of the C. elegans genome in 1998 coupled with the advent of RNAi techniques to knock down gene function ushered in a new age in the field of functional genomics. There are four methods for double-stranded RNA (dsRNA) delivery in C. elegans: (1) injection of dsRNA into any region of the animal (Fire et al., 1998), (2) feeding with bacteria producing dsRNA (Timmons et al., 2001), (3) soaking in dsRNA (Tabara et al., 1998), and (4) in vivo production of dsRNA from transgenic promoters (Tavernarakis et al., 2000). In this chapter, we discuss the molecular genetic mechanisms, techniques, and applications of RNAi in C. elegans.

From protein networks to biological systems

A system-level understanding of any biological process requires a map of the relationships among the various molecules involved. Technologies to detect and predict protein interactions have begun to produce very large maps of protein interactions, some including most of an organism's proteins. These maps can be used to study how proteins work together to form molecular machines and regulatory pathways. They also provide a framework for constructing predictive models of how information and energy flow through biological networks. In many respects, protein interaction maps are an entrée into systems biology.

Interactome modeling

A long-term goal of the field of interactome modeling is to understand how global and local properties of complex macromolecular networks impact on observable biological properties, and how changes in such properties can lead to human diseases. The information available at this stage of development of the field provides strong evidence for the existence of such interesting global and local properties, but also demonstrates that many more datasets will be needed to provide accurate models with increasingly predictive capacity. This review focuses on an early attempt at mapping a multicellular interactome network and on the lessons learned from that attempt.

Derivation of genetic interaction networks from quantitative phenotype data

Genetic interaction networks were derived from quantitative phenotype data by analyzing agar-invasion phenotypes of mutant yeast strains, which showed specific modes of genetic interaction with specific biological processes.

We have generalized the derivation of genetic-interaction networks from quantitative phenotype data. Familiar and unfamiliar modes of genetic interaction were identified and defined. A network was derived from agar-invasion phenotypes of mutant yeast. Mutations showed specific modes of genetic interaction with specific biological processes. Mutations formed cliques of significant mutual information in their large-scale patterns of genetic interaction. These local and global interaction patterns reflect the effects of gene perturbations on biological processes and pathways.

Reconstruction of cellular signalling networks and analysis of their properties

The study of cellular signalling over the past 20 years and the advent of high-throughput technologies are enabling the reconstruction of large-scale signalling networks. After careful reconstruction of signalling networks, their properties must be described within an integrative framework that accounts for the complexity of the cellular signalling network and that is amenable to quantitative modelling.

Genome-wide high-throughput screens in functional genomics

The availability of complete genome sequences from many organisms has yielded the ability to perform high-throughput, genome-wide screens of gene function. Within the past year, rapid advances have been made towards this goal in many major model systems, including yeast, worms, flies, and mammals. Yeast genome-wide screens have taken advantage of libraries of deletion strains, but RNA-interference has been used in other organisms to knockdown gene function. Examples of recent large-scale functional genetic screens include drug-target identification in yeast, regulators of fat accumulation in worms, growth and viability in flies, and proteasome-mediated degradation in mammalian cells. Within the next five years, such screens are likely to lead to annotation of function of most genes across multiple organisms. Integration of such data with other genomic approaches will extend our understanding of cellular networks.

The Proteomics of Neurodegeneration

The continuing improvement and refinement of proteomic and bioinformatic tools has made it possible to obtain increasing amounts of structural and functional information about proteins on a global scale. The emerging field of neuroproteomics promises to provide powerful strategies for further characterizing neuronal dysfunction and cell loss associated with neurodegenerative diseases. Neuroproteomic studies have thus far revealed relatively comprehensive quantitative changes and post-translational modifications (mostly oxidative damage) of high abundance proteins, confirming deficits in energy production, protein degradation, antioxidant protein function, and cytoskeletal regulation associated with neurodegenerative diseases such as Alzheimer and Parkinson disease. The identification of changes in low-abundance proteins and characterization of their functions based on protein-protein interactions still await further development of proteomic methodologies and more dedicated application of these technologies by neuroscientists. Once accomplished, however, the resulting information will certainly provide a truly comprehensive view of neurodegeneration-associated changes in protein expression, facilitating the identification of novel biomarkers for the early detection of neurodegenerative diseases and new targets for therapeutic intervention.

A Drosophila protein-interaction map centered on cell-cycle regulators

BACKGROUND

Maps depicting binary interactions between proteins can be powerful starting points for understanding biological systems. A proven technology for generating such maps is high-throughput yeast two-hybrid screening. In the most extensive screen to date, a Gal4-based two-hybrid system was used recently to detect over 20,000 interactions among Drosophila proteins. Although these data are a valuable resource for insights into protein networks, they cover only a fraction of the expected number of interactions.

RESULTS

To complement the Gal4-based interaction data, we used the same set of Drosophila open reading frames to construct arrays for a LexA-based two-hybrid system. We screened the arrays using a novel pooled mating approach, initially focusing on proteins related to cell-cycle regulators. We detected 1,814 reproducible interactions among 488 proteins. The map includes a large number of novel interactions with potential biological significance. Informative regions of the map could be highlighted by searching for paralogous interactions and by clustering proteins on the basis of their interaction profiles. Surprisingly, only 28 interactions were found in common between the LexA- and Gal4-based screens, even though they had similar rates of true positives.

CONCLUSIONS

The substantial number of new interactions discovered here supports the conclusion that previous interaction mapping studies were far from complete and that many more interactions remain to be found. Our results indicate that different two-hybrid systems and screening approaches applied to the same proteome can generate more comprehensive datasets with more cross-validated interactions. The cell-cycle map provides a guide for further defining important regulatory networks in Drosophila and other organisms.

Toward improving Caenorhabditis elegans phenome mapping with an ORFeome-based RNAi library

The recently completed Caenorhabditis elegans genome sequence allows application of high-throughput (HT) approaches for phenotypic analyses using RNA interference (RNAi). As large phenotypic data sets become available, "phenoclustering" strategies can be used to begin understanding the complex molecular networks involved in development and other biological processes. The current HT-RNAi resources represent a great asset for phenotypic profiling but are limited by lack of flexibility. For instance, existing resources do not take advantage of the latest improvements in RNAi technology, such as inducible hairpin RNAi. Here we show that a C. elegans ORFeome resource, generated with the Gateway cloning system, can be used as a starting point to generate alternative HT-RNAi resources with enhanced flexibility. The versatility inherent to the Gateway system suggests that additional HT-RNAi libraries can now be readily generated to perform gene knockdowns under various conditions, increasing the possibilities for phenome mapping in C. elegans.

ORFeome cloning and systems biology: standardized mass production of the parts from the parts-list

Together with metabolites, proteins and RNAs form complex biological systems through highly intricate networks of physical and functional interactions. Large-scale studies aimed at a molecular understanding of the structure, function, and dynamics of proteins and RNAs in the context of cellular networks require novel approaches and technologies. This Special Issue of Genome Research features strategies for the high-throughput construction and manipulation of complete sets of protein-encoding open reading frames (ORFeome), gene promoters (promoterome), and noncoding RNAs, as predicted from genome and transcriptome sequences. Here we discuss the use of a recombinational cloning system that allows efficiency, adaptability, and compatibility in the generation of ORFeome, promoterome, and other resources.

Using RNAi to catch Drosophila genes in a web of interactions: insights into cancer research

The completion of whole-genome sequencing of various model organisms and the recent explosion of new technologies in the field of Functional Genomics and Proteomics is poised to revolutionize the way scientists identify and characterize gene function. One of the most significant advances in recent years has been the application of RNA interference (RNAi) as a means of assaying gene function. In the post-genomic era, advances in the field of cancer biology will rely upon the rapid identification and characterization of genes that regulate cell growth, proliferation, and apoptosis. Significant efforts are being directed towards cancer therapy and devising efficient means of selectively delivering drugs to cancerous cells. In this review, we discuss the promise of integrating genome-wide RNAi screens with proteomic approaches and small-molecule chemical genetic screens, towards improving our ability to understand and treat cancer.

Applying proteomics to signaling networks

The information from genome sequencing provides a new framework for a systems-wide understanding of protein networks and cellular function. Whereas microarray technologies provide information about global gene expression within cells, complementary proteomic strategies monitor expression of proteins and their posttranslational modifications. Improved technologies that have emerged for comprehensive and high-throughput protein analysis yield novel insights into cell regulation.

Functional proteomics mapping of a human signaling pathway

Access to the human genome facilitates extensive functional proteomics studies. Here, we present an integrated approach combining large-scale protein interaction mapping, exploration of the interaction network, and cellular functional assays performed on newly identified proteins involved in a human signaling pathway. As a proof of principle, we studied the Smad signaling system, which is regulated by members of the transforming growth factor beta (TGFbeta) superfamily. We used two-hybrid screening to map Smad signaling protein-protein interactions and to establish a network of 755 interactions, involving 591 proteins, 179 of which were poorly or not annotated. The exploration of such complex interaction databases is improved by the use of PIMRider, a dedicated navigation tool accessible through the Web. The biological meaning of this network is illustrated by the presence of 18 known Smad-associated proteins. Functional assays performed in mammalian cells including siRNA knock-down experiments identified eight novel proteins involved in Smad signaling, thus validating this integrated functional proteomics approach.