Comparative interactomics: comparing apples and pears?

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Network Analysis of Inflammatory Bowel Disease Research: Towards the Interactome

BACKGROUND AND AIMS

Modern views accept that inflammatory bowel diseases [IBD] emerge from complex interactions among the multiple components of a biological network known as the 'IBD interactome'. These diverse components belong to different functional levels including cells, molecules, genes and biological processes. This diversity can make it difficult to integrate available empirical information from human patients into a collective view of aetiopathogenesis, a necessary step to understand the interactome. Herein, we quantitatively analyse how the representativeness of components involved in human IBD and their relationships ha ve changed over time.

METHODS

A bibliographic search in PubMed retrieved 25 971 abstracts of experimental studies on IBD in humans, published between 1990 and 2020. Abstracts were scanned automatically for 1218 IBD interactome components proposed in recent reviews. The resulting databases are freely available and were visualized as networks indicating the frequency at which different components are referenced together within each abstract.

RESULTS

As expected, over time there was an increase in components added to the IBD network and heightened connectivity within and across functional levels. However, certain components were consistently studied together, forming preserved motifs in the networks. These overrepresented and highly linked components reflect main 'hypotheses' in IBD research in humans. Interestingly, 82% of the components cited in reviews were absent or showed low frequency, suggesting that many aspects of the proposed IBD interactome still have weak experimental support in humans.

CONCLUSIONS

A reductionist and fragmented approach to the study of IBD has prevailed in previous decades, highlighting the importance of transitioning towards a more integrated interactome framework.

Proteomics as a promising biomarker in food authentication, quality and safety: A review

Adulteration and mislabeling have become a very common global malpractice in food industry. Especially foods of animal origin are prepared from plant sources and intentionally mislabeled. This type of mislabeling is an important concern in food safety as the replaced ingredients may cause a food allergy or toxicity to vulnerable consumers. Moreover, foodborne pathogens also pose a major threat to food safety. There is a dire need to develop strong analytical tools to deal with related issues. In this context, proteomics stands out as a promising tool used to report the aforementioned issues. The development in the field of omics has inimitable advantages in enabling the understanding of various biological fields especially in the discipline of food science. In this review, current applications and the role of proteomics in food authenticity, safety, and quality and food traceability are highlighted comprehensively. Additionally, the other components of proteomics have also been comprehensively described. Furthermore, this review will be helpful in the provision of new intuition into the use of proteomics in food analysis. Moreover, the pathogens in food can also be identified based on differences in their protein profiling. Conclusively, proteomics, an indicator of food properties, its origin, the processes applied to food, and its composition are also the limelight of this article.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

Proteomics and Metabolomics for Cystic Fibrosis Research

The aim of this review article is to introduce the reader to the state-of-the-art of the contribution that proteomics and metabolomics sciences are currently providing for cystic fibrosis (CF) research: from the understanding of cystic fibrosis transmembrane conductance regulator (CFTR) biology to biomarker discovery for CF diagnosis. Our work particularly focuses on CFTR post-translational modifications and their role in cellular trafficking as well as on studies that allowed the identification of CFTR molecular interactors. We also show how metabolomics is currently helping biomarker discovery in CF. The most recent advances in these fields are covered by this review, as well as some considerations on possible future scenarios for new applications.

Endometriosis research in the -omics era

Endometriosis is a pathological condition extensively studied, but its pathogenesis is not completely understood, since its pathophysiology stems from a broad spectrum of environmental influences and genetic factors. Moreover, the nature of this condition is heterogeneous and includes different anatomical entities. Scientists actively pursue discovery of novel biomarkers in the hope of better identifying susceptible individuals in early stages of the disease. High-throughput technologies have substantially revolutionized medical research and, as a first step, the advent of genotyping arrays led to large-scale genome-wide association studies (GWAS) and enabled the assessment of global transcript levels, thus giving rise to integrative genetics. In this framework, comprehensive studies have been conducted at multiple biological levels by using the "omics" platforms, thus allowing to re-examine endometriosis at a greater degree of molecular resolution. -Omics technologies can detect and analyze hundreds of markers in the same experiment and their increasing use in the field of gynecology comes from an urgent need to find new diagnostic and therapeutic tools that improve the diagnosis of endometriosis and the efficacy of assisted reproductive techniques. Proteomics and metabolomics have been introduced recently into the every day methodology of researchers collaborating with gynecologists and, importantly, multi-omics approach is advantageous to gain insight of the total information that underlies endometriosis, compared to studies of any single -omics type. In this review, we expect to present multiple studies based on the high-throughput-omics technologies and to shed light in all considerable advantages that they may confer to a proper management of endometriosis.

Mind-life continuity: A qualitative study of conscious experience

There are two fundamental models to understanding the phenomenon of natural life. One is the computational model, which is based on the symbolic thinking paradigm. The other is the biological organism model. The common difficulty attributed to these paradigms is that their reductive tools allow the phenomenological aspects of experience to remain hidden behind yes/no responses (behavioral tests), or brain 'pictures' (neuroimaging). Hence, one of the problems regards how to overcome methodological difficulties towards a non-reductive investigation of conscious experience. It is our aim in this paper to show how cooperation between Eastern and Western traditions may shed light for a non-reductive study of mind and life. This study focuses on the first-person experience associated with cognitive and mental events. We studied phenomenal data as a crucial fact for the domain of living beings, which, we expect, can provide the ground for a subsequent third-person study. The intervention with Jhana meditation, and its qualitative assessment, provided us with experiential profiles based upon subjects' evaluations of their own conscious experiences. The overall results should move towards an integrated or global perspective on mind where neither experience nor external mechanisms have the final word.

Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks

Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

Review on proteomics for food authentication

Consumers have the right to know what is in the food they are eating. Accordingly, European and global food regulations require that the provenance of the food can be guaranteed from farm to fork. Many different instrumental techniques have been proposed for food authentication. Although traditional methods are still being used, new approaches such as genomics, proteomics, and metabolomics are helping to complement existing methodologies for verifying the claims made about certain food products. During the last decade, proteomics (the large-scale analysis of proteins in a particular biological system at a particular time) has been applied to different research areas within food technology. Since proteins can be used as markers for many properties of a food, even indicating processes to which the food has been subjected, they can provide further evidence of the foods labeling claim. This review is a comprehensive and updated overview of the applications, drawbacks, advantages, and challenges of proteomics for food authentication in the assessment of the foods compliance with labeling regulations and policies.

SIGNIFICANCE

This review paper provides a comprehensive and critical overview of the application of proteomics approaches to determine the authenticity of several food products updating the performances and current limitations of the applied techniques in both laboratory and industrial environments.

On the outside looking in: redefining the role of analytical chemistry in the biosciences

Analytical chemistry has much to offer to an improved understanding of biological systems.

Panorama of ancient metazoan macromolecular complexes

Macromolecular complexes are essential to conserved biological processes, but their prevalence across animals is unclear. By combining extensive biochemical fractionation with quantitative mass spectrometry, we directly examined the composition of soluble multiprotein complexes among diverse metazoan models. Using an integrative approach, we then generated a draft conservation map consisting of >1 million putative high-confidence co-complex interactions for species with fully sequenced genomes that encompasses functional modules present broadly across all extant animals. Clustering revealed a spectrum of conservation, ranging from ancient Eukaryal assemblies likely serving cellular housekeeping roles for at least 1 billion years, ancestral complexes that have accrued contemporary components, and rarer metazoan innovations linked to multicellularity. We validated these projections by independent co-fractionation experiments in evolutionarily distant species, by affinity-purification and by functional analyses. The comprehensiveness, centrality and modularity of these reconstructed interactomes reflect their fundamental mechanistic significance and adaptive value to animal cell systems.

The hierarchical organization of natural protein interaction networks confers self-organization properties on pseudocells

Background

Cell organization is governed and maintained via specific interactions among its constituent macromolecules. Comparison of the experimentally determined protein interaction networks in different model organisms has revealed little conservation of the specific edges linking ortholog proteins. Nevertheless, some topological characteristics of the graphs representing the networks - namely non-random degree distribution and high clustering coefficient - are shared by networks of distantly related organisms. Here we investigate the role of the topological features of the protein interaction network in promoting cell organization.

Methods

We have used a stochastic model, dubbed ProtNet representing a computer stylized cell to answer questions about the dynamic consequences of the topological properties of the static graphs representing protein interaction networks.

Results

By using a novel metrics of cell organization, we show that natural networks, differently from random networks, can promote cell self-organization. Furthermore the ensemble of protein complexes that forms in pseudocells, which self-organize according to the interaction rules of natural networks, are more robust to perturbations.

Conclusions

The analysis of the dynamic properties of networks with a variety of topological characteristics lead us to conclude that self organization is a consequence of the high clustering coefficient, whereas the scale free degree distribution has little influence on this property.

Rapid, optimized interactomic screening

We must reliably map the interactomes of cellular macromolecular complexes in order to fully explore and understand biological systems. However, there are no methods to accurately predict how to capture a given macromolecular complex with its physiological binding partners. Here, we present a screen that comprehensively explores the parameters affecting the stability of interactions in affinity-captured complexes, enabling the discovery of physiological binding partners and the elucidation of their functional interactions in unparalleled detail. We have implemented this screen on several macromolecular complexes from a variety of organisms, revealing novel profiles even for well-studied proteins. Our approach is robust, economical and automatable, providing an inroad to the rigorous, systematic dissection of cellular interactomes.

Systems biology perspectives on minimal and simpler cells

The concept of the minimal cell has fascinated scientists for a long time, from both fundamental and applied points of view. This broad concept encompasses extreme reductions of genomes, the last universal common ancestor (LUCA), the creation of semiartificial cells, and the design of protocells and chassis cells. Here we review these different areas of research and identify common and complementary aspects of each one. We focus on systems biology, a discipline that is greatly facilitating the classical top-down and bottom-up approaches toward minimal cells. In addition, we also review the so-called middle-out approach and its contributions to the field with mathematical and computational models. Owing to the advances in genomics technologies, much of the work in this area has been centered on minimal genomes, or rather minimal gene sets, required to sustain life. Nevertheless, a fundamental expansion has been taking place in the last few years wherein the minimal gene set is viewed as a backbone of a more complex system. Complementing genomics, progress is being made in understanding the system-wide properties at the levels of the transcriptome, proteome, and metabolome. Network modeling approaches are enabling the integration of these different omics data sets toward an understanding of the complex molecular pathways connecting genotype to phenotype. We review key concepts central to the mapping and modeling of this complexity, which is at the heart of research on minimal cells. Finally, we discuss the distinction between minimizing the number of cellular components and minimizing cellular complexity, toward an improved understanding and utilization of minimal and simpler cells.

Interactomics: toward protein function and regulation

Protein-protein interactions are central to all cellular processes. Understanding of protein-protein interactions is therefore fundamental for many areas of biochemical and biomedical research and will facilitate an understanding of the cell process-regulating machinery, disease causative mechanisms, biomarkers, drug target discovery and drug development. In this review, we summarize methods for populating and analyzing the interactome, highlighting their advantages and disadvantages. Applications of interactomics in both the biochemical and clinical arenas are presented, illustrating important recent advances in the field.

Increasing the precision of orthology-based complex prediction through network alignment

Macromolecular assemblies play an important role in almost all cellular processes. However, despite several large-scale studies, our current knowledge about protein complexes is still quite limited, thus advocating the use of in silico predictions to gather information on complex composition in model organisms. Since protein–protein interactions present certain constraints on the functional divergence of macromolecular assemblies during evolution, it is possible to predict complexes based on orthology data. Here, we show that incorporating interaction information through network alignment significantly increases the precision of orthology-based complex prediction. Moreover, we performed a large-scale in silico screen for protein complexes in human, yeast and fly, through the alignment of hundreds of known complexes to whole organism interactomes. Systematic comparison of the resulting network alignments to all complexes currently known in those species revealed many conserved complexes, as well as several novel complex components. In addition to validating our predictions using orthogonal data, we were able to assign specific functional roles to the predicted complexes. In several cases, the incorporation of interaction data through network alignment allowed to distinguish real complex components from other orthologous proteins. Our analyses indicate that current knowledge of yeast protein complexes exceeds that in other organisms and that predicting complexes in fly based on human and yeast data is complementary rather than redundant. Lastly, assessing the conservation of protein complexes of the human pathogen Mycoplasma pneumoniae, we discovered that its complexes repertoire is different from that of eukaryotes, suggesting new points of therapeutic intervention, whereas targeting the pathogen’s Restriction enzyme complex might lead to adverse effects due to its similarity to ATP-dependent metalloproteases in the human host.

Single-molecule detection and tracking in plants

Combining optical properties with a limited choice of fluorophores turns single-molecule imaging in plants into a challenging task. This explains why the technique, despite its success in the field of animal cell biology, is far from being routinely applied in plant cell research. The same challenges, however, also apply to the application of single-molecule microscopy to any intact tissue or multicellular 3D cell culture. As recent and upcoming progress in fluorescence microscopy will permit single-molecule detection in the context of multicellular systems, plant tissue imaging will experience a huge benefit from this progress. In this review, we address every step of a single-molecule experiment, highlight the critical aspects of each and elaborate on optimizations and developments required for improvements. We relate each step to recent achievements, which have so far been conducted exclusively on the root epidermis of Arabidopsis thaliana seedlings with inclined illumination and show examples of single-molecule measurements using different cells or illumination schemes.

The SH2 domain interaction landscape

Members of the SH2 domain family modulate signal transduction by binding to short peptides containing phosphorylated tyrosines. Each domain displays a distinct preference for the sequence context of the phosphorylated residue. We have developed a high-density peptide chip technology that allows for probing of the affinity of most SH2 domains for a large fraction of the entire complement of tyrosine phosphopeptides in the human proteome. Using this technique, we have experimentally identified thousands of putative SH2-peptide interactions for more than 70 different SH2 domains. By integrating this rich data set with orthogonal context-specific information, we have assembled an SH2-mediated probabilistic interaction network, which we make available as a community resource in the PepspotDB database. A predicted dynamic interaction between the SH2 domains of the tyrosine phosphatase SHP2 and the phosphorylated tyrosine in the extracellular signal-regulated kinase activation loop was validated by experiments in living cells.

Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system

The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.

Comparative analysis of virus-host interactomes with a mammalian high-throughput protein complementation assay based on Gaussia princeps luciferase

Comparative interactomics is a strategy for inferring potential interactions among orthologous proteins or "interologs". Herein we focus, in contrast to standard homology-based inference, on the divergence of protein interaction profiles among closely related organisms, showing that the approach can correlate specific traits to phenotypic differences. As a model, this new comparative interactomic approach was applied at a large scale to human papillomaviruses (HPVs) proteins. The oncogenic potential of HPVs is mainly determined by the E6 and E7 early proteins. We have mapped and overlapped the virus-host protein interaction networks of E6 and E7 proteins from 11 distinct HPV genotypes, selected for their different tropisms and pathologies. We generated robust and comprehensive datasets by combining two orthogonal protein interaction assays: yeast two-hybrid (Y2H), and our recently described "high-throughput Gaussia princeps protein complementation assay" (HT-GPCA). HT-GPCA detects protein interaction by measuring the interaction-mediated reconstitution of activity of a split G. princeps luciferase. Hierarchical clustering of interaction profiles recapitulated HPV phylogeny and was used to correlate specific virus-host interaction profiles with pathological traits, reflecting the distinct carcinogenic potentials of different HPVs. This comparative interactomics constitutes a reliable and powerful strategy to decipher molecular relationships in virtually any combination of microorganism-host interactions.

AlignNemo: a local network alignment method to integrate homology and topology

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.

NetAligner--a network alignment server to compare complexes, pathways and whole interactomes

The many ongoing genome sequencing initiatives are delivering comprehensive lists of the individual molecular components present in an organism, but these reveal little about how they work together. Follow-up initiatives are revealing thousands of interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Recently, we developed NetAligner, a novel network alignment tool that allows the identification of conserved protein complexes and pathways across organisms, providing valuable hints as to how those interaction networks evolved. NetAligner includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which increases its performance with respect to existing tools. The web server implementation of the NetAligner algorithm presented here features complex, pathway and interactome to interactome alignments for seven model organisms, namely Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Saccharomyces cerevisiae and Escherichia coli. The user can query complexes and pathways of arbitrary topology against a target species interactome, or directly compare two complete interactomes to identify conserved complexes and subnetworks. Alignment solutions can be downloaded or directly visualized in the browser. The NetAligner web server is publicly available at http://netaligner.irbbarcelona.org/.

A novel framework for the comparative analysis of biological networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes.

Proteomics in evolutionary ecology: linking the genotype with the phenotype

The study of the proteome (proteomics), which includes the dynamics of protein expression, regulation, interactions and its function, has played a less prominent role in evolutionary and ecological investigations in comparison with the study of the genome and transcriptome. There are, however, a number of arguments suggesting that this situation should change. First, the proteome is closer to the phenotype than the genome or the transcriptome, and as such may be more directly responsive to natural selection, and thus closely linked to adaptation. Second, there is evidence of a low correlation between protein and transcript expression levels across genes in many different organisms. Finally, there have been some recent important technological improvements in proteomics methods that make them feasible, practical and useful to address a wide range of evolutionary questions even in nonmodel organisms. The different proteomic methods, their limitations and problems when interpreting empirical data are described and discussed. In addition, the proteomic literature pertaining to evolutionary ecology is reviewed with examples, and potential applications of proteomics in a variety of evolutionary contexts are outlined. New proteomic research trends such as the study of posttranslational modifications and protein-protein interactions, as well as the combined use of the different -omics approaches, are discussed in relation to the development of a more functional and integrated perspective, needed for achieving a more comprehensive knowledge of evolutionary change.

Functional proteomics: application of mass spectrometry to the study of enzymology in complex mixtures

This review covers recent developments in mass spectrometry-based applications dealing with functional proteomics with special emphasis on enzymology. The introduction of mass spectrometry into this research field has led to an enormous increase in knowledge in recent years. A major challenge is the identification of "biologically active substances" in complex mixtures. These biologically active substances are, on the one hand, potential regulators of enzymes. Elucidation of function and identity of those regulators may be accomplished by different strategies, which are discussed in this review. The most promising approach thereby seems to be the one-step procedure, because it enables identification of the functionality and identity of biologically active substances in parallel and thus avoids misinterpretation. On the other hand, besides the detection of regulators, the identification of endogenous substrates for known enzymes is an emerging research field, but in this case studies are quite rare. Moreover, the term biologically active substances may also encompass proteins with diverse biological functions. Elucidation of the functionality of those-so far unknown-proteins in complex mixtures is another branch of functional proteomics and those investigations will also be discussed in this review.

Exploring the conservation of synthetic lethal genetic interaction networks

High-throughput studies have enabled the large-scale mapping of synthetic lethal genetic interaction networks in the budding yeast Saccharomyces cerevisiae (S. cerevisiae). Recently, complementary high-throughput methods have been developed to map genetic interactions in the fission yeast Schizosaccharomyces pombe (S. pombe), enabling comparative analyses of genetic interaction networks between S. pombe and S. cerevisiae, two species separated by hundreds of millions of years of evolution. The resultant data has providing our first view of a possible core genetic interaction network shared between two distantly related eukaryotes, and identified numerous species-specific interactions that may contribute to the unique biology of these two different organisms. These and other results suggest that comparative interactomic studies will provide novel insights into the structure of genetic interaction networks.

The protein interaction network mediated by human SH3 domains

Families of conserved protein domains, specialized in mediating interactions with short linear peptide motifs, are responsible for the formation of a variety of dynamic complexes in the cell. An important subclass of these motifs are characterized by a high proline content and play a pivotal role in biological processes requiring the coordinated assembly of multi-protein complexes. This is achieved via interaction of proteins containing modules such as Src Homology-3 (SH3) or WW domains and specific proline rich patterns. Here we make available via a publicly accessible database a synopsis of our current understanding of the interaction landscape of the human SH3 protein family. This is achieved by integrating an information extraction strategy with a new experimental approach. In a first approach we have used a text mining strategy to capture a large number of manuscripts reporting interactions between SH3 domains and target peptides. Relevant information was annotated in the MINT database. In a second experimental approach we have used a variant of the WISE (Whole Interactome Scanning Experiment) strategy to probe a large number of naturally occurring and chemically-synthesized peptides arrayed at high density on a glass surface. By this method we have tested 60 human SH3 domains for their ability to bind a collection of 9192 poly-proline containing peptides immobilized on a glass chip. To evaluate the quality of the resulting interaction dataset, we retested some of the interactions on a smaller scale and performed a series of pull down experiments on native proteins. Peptide chips, pull down assays, SPOT synthesis and phage display experiments have allowed us to further characterize the specificity and promiscuity of proline-rich binding domains and to map their interaction network. Both the information captured from the literature and the interactions inferred from the peptide chip experiments were collected and stored in the PepspotDB (http://mint.bio.uniroma2.it/PepspotDB/).

Omics technologies, data and bioinformatics principles

We provide an overview on the state of the art for the Omics technologies, the types of omics data and the bioinformatics resources relevant and related to Omics. We also illustrate the bioinformatics challenges of dealing with high-throughput data. This overview touches several fundamental aspects of Omics and bioinformatics: data standardisation, data sharing, storing Omics data appropriately and exploring Omics data in bioinformatics. Though the principles and concepts presented are true for the various different technological fields, we concentrate in three main Omics fields namely: genomics, transcriptomics and proteomics. Finally we address the integration of Omics data, and provide several useful links for bioinformatics and Omics.

The use and abuse of -omes

The diverse fields of Omics research share a common logical structure combining a cataloging effort for a particular class of molecules or interactions, the underlying -ome, and a quantitative aspect attempting to record spatiotemporal patterns of concentration, expression, or variation. Consequently, these fields also share a common set of difficulties and limitations. In spite of the great success stories of Omics projects over the last decade, much remains to be understood not only at the technological, but also at the conceptual level. Here, we focus on the dark corners of Omics research, where the problems, limitations, conceptual difficulties, and lack of knowledge are hidden.

The evolution of protein interaction networks

The availability of high-throughput methods to detect protein interactions made construction of comprehensive protein interaction networks for several important model organisms possible. Many studies have since focused on uncovering the structural principles of these networks and relating these structures to biological processes. On a global scale, there are striking similarities in the structure of different protein interaction networks, even when distantly related species, such as the yeast Saccharomyces cerevisiae and the fruit fly Drosophila melanogaster, are compared. However, there is also considerable variance in network structures caused by the gain and loss of genes and mutations which alter the interaction behavior of the encoded proteins. Here, we focus on the current state of knowledge on the structure of protein interaction networks and the evolutionary processes that shaped these structures.

Mapping the ribosomal protein S7 regulatory binding site on mRNA of the E. coli streptomycin operon

In this work it is shown by deletion analysis that an intercistronic region (ICR) approximately 80 nucleotides in length is necessary for interaction with recombinant E. coli S7 protein (r6hEcoS7). A model is proposed for the interaction of S7 with two ICR sites-region of hairpin bifurcations and Shine-Dalgarno sequence of cistron S7. A de novo RNA binding site for heterologous S7 protein of Thermus thermophilus (r6hTthS7) was constructed by selection of a combinatorial RNA library based on E. coli ICR: it has only a single supposed protein recognition site in the region of bifurcation. The SERW technique was used for selection of two intercistronic RNA libraries in which five nucleotides of a double-stranded region, adjacent to the bifurcation, had the randomized sequence. One library contained an authentic AG (-82/-20) pair, while in the other this pair was replaced by AU. A serwamer capable of specific binding to r6hTthS7 was selected; it appeared to be the RNA68 mutant with eight nucleotide mutations. The serwamer binds to r6hTthS7 with the same affinity as homologous authentic ICR of str mRNA binds to r6hEcoS7; apparent dissociation constants are 89 +/- 43 and 50 +/- 24 nM, respectively.

The bovine milk proteome: cherishing, nourishing and fostering molecular complexity. An interactomics and functional overview

Bovine milk represents an essential source of nutrients for lactating calves and a key raw material for human food preparations. A wealth of data are present in the literature dealing with massive proteomic analyses of milk fractions and independent targeted studies on specific groups of proteins, such as caseins, globulins, hormones and cytokines. In this study, we merged data from previous investigations to compile an exhaustive list of 573 non-redundant annotated protein entries. This inventory was exploited for integrated in silico studies, including functional GO term enrichment (FatiGO/Babelomics), multiple pathway and network analyses. As expected, most of the milk proteins were grouped under pathways/networks/ontologies referring to nutrient transport, lipid metabolism and objectification of the immune system response. Notably enough, another functional family was observed as the most statistically significant one, which included proteins involved in the induction of cellular proliferation processes as well as in anatomical and haematological system development. Although the latter function for bovine milk proteins has long been postulated, studies reported so far mainly focused on a handful of molecules and missed the whole overview resulting from an integrated holistic analysis. A preliminary map of the bovine milk proteins interactome was also built up, which will be refined in future as result of the widespread use of quantitative methods in protein interaction studies and consequent reduction of false-positives within associated databases.

Virus-host protein interactions in RNA viruses

RNA viruses exhibit small-sized genomes that only encode a limited number of viral proteins, but still establish complex networks of interactions with host cell components. Here we summarize recent reports that aim at understanding general features of RNA virus infection networks at the protein level.

Evolutionary analysis reveals low coverage as the major challenge for protein interaction network alignment

Local alignments of protein interaction networks have found little conservation among several species. While this could be a consequence of the incompleteness of interaction data-sets and presence of error, an intriguing prospect is that the process of network evolution is sufficient to erase any evidence of conservation. Here, we aim to test this hypothesis using models of network evolution and also investigate the role of error in the results of network alignment. We devised a distance metric based on summary statistics to assess the fit between experimental and simulated network alignments. Our results indicate that network evolution alone is unlikely to account for the poor quality alignments given by real data. Alignments of simulated networks undergoing evolution are considerably (4 to 5 times) larger than real alignments. We compare several error models in their ability to explain this discrepancy. Our estimates of false negative rates vary from 20 to 60% dependent on whether incomplete proteome sampling is taken into account or not. We also find that false positives appear to affect network alignments little compared to false negatives indicating that incompleteness, not spurious links, is the major challenge for interactome-level comparisons.

Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods

We apply our recently developed information-theoretic measures for the characterisation and comparison of protein-protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large-scale analysis of protein-protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast-two-hybrid methods are sufficiently consistent to allow for intra-species comparisons (between different experiments) and inter-species comparisons, while data from affinity-purification mass-spectrometry methods show large differences even within intra-species comparisons.

Applications of proteomic technologies for understanding the premature proteolysis of CFTR

Cystic fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator (CFTR) gene, which encodes an ATP-dependent anion channel. Disease-causing mutations can affect channel biogenesis, trafficking or function, and result in reduced ion transport at the apical surface of many tissues. The most common CFTR mutation is a deletion of phenylalanine at position 508 (DeltaF508), which results in a misfolded protein that is prematurely targeted for degradation. This article focuses on how proteomic approaches have been utilized to explore the mechanisms of premature proteolysis in CF. Additionally, we emphasize the potential for proteomic-based technologies in expanding our understanding of CF pathophysiology and therapeutic approaches.

Proteomics of plant pathogenic fungi

Plant pathogenic fungi cause important yield losses in crops. In order to develop efficient and environmental friendly crop protection strategies, molecular studies of the fungal biological cycle, virulence factors, and interaction with its host are necessary. For that reason, several approaches have been performed using both classical genetic, cell biology, and biochemistry and the modern, holistic, and high-throughput, omic techniques. This work briefly overviews the tools available for studying Plant Pathogenic Fungi and is amply focused on MS-based Proteomics analysis, based on original papers published up to December 2009. At a methodological level, different steps in a proteomic workflow experiment are discussed. Separate sections are devoted to fungal descriptive (intracellular, subcellular, extracellular) and differential expression proteomics and interactomics. From the work published we can conclude that Proteomics, in combination with other techniques, constitutes a powerful tool for providing important information about pathogenicity and virulence factors, thus opening up new possibilities for crop disease diagnosis and crop protection.

Predicting protein-protein interactions in the context of protein evolution

Here we review the methods for the prediction of protein interactions and the ideas in protein evolution that relate to them. The evolutionary assumptions implicit in many of the protein interaction prediction methods are elucidated. We draw attention to the caution needed in deploying certain evolutionary assumptions, in particular cross-organism transfer of interactions by sequence homology, and discuss the known issues in deriving interaction predictions from evidence of co-evolution. We also conject that there is evolutionary knowledge yet to be exploited in the prediction of interactions, in particular the heterogeneity of interactions, the increasing availability of interaction data from multiple species, and the models of protein interaction network growth.

Yeast two-hybrid, a powerful tool for systems biology

A key property of complex biological systems is the presence of interaction networks formed by its different components, primarily proteins. These are crucial for all levels of cellular function, including architecture, metabolism and signalling, as well as the availability of cellular energy. Very stable, but also rather transient and dynamic protein-protein interactions generate new system properties at the level of multiprotein complexes, cellular compartments or the entire cell. Thus, interactomics is expected to largely contribute to emerging fields like systems biology or systems bioenergetics. The more recent technological development of high-throughput methods for interactomics research will dramatically increase our knowledge of protein interaction networks. The two most frequently used methods are yeast two-hybrid (Y2H) screening, a well established genetic in vivo approach, and affinity purification of complexes followed by mass spectrometry analysis, an emerging biochemical in vitro technique. So far, a majority of published interactions have been detected using an Y2H screen. However, with the massive application of this method, also some limitations have become apparent. This review provides an overview on available yeast two-hybrid methods, in particular focusing on more recent approaches. These allow detection of protein interactions in their native environment, as e.g. in the cytosol or bound to a membrane, by using cytosolic signalling cascades or split protein constructs. Strengths and weaknesses of these genetic methods are discussed and some guidelines for verification of detected protein-protein interactions are provided.

Protein function prediction--the power of multiplicity

Advances in experimental and computational methods have quietly ushered in a new era in protein function annotation. This 'age of multiplicity' is marked by the notion that only the use of multiple tools, multiple evidence and considering the multiple aspects of function can give us the broad picture that 21st century biology will need to link and alter micro- and macroscopic phenotypes. It might also help us to undo past mistakes by removing errors from our databases and prevent us from producing more. On the downside, multiplicity is often confusing. We therefore systematically review methods and resources for automated protein function prediction, looking at individual (biochemical) and contextual (network) functions, respectively.

Targeting and tinkering with interaction networks

Biological interaction networks have been in the scientific limelight for nearly a decade. Increasingly, the concept of network biology and its various applications are becoming more commonplace in the community. Recent years have seen networks move from pretty pictures with limited application to solid concepts that are increasingly used to understand the fundamentals of biology. They are no longer merely results of postgenome analysis projects, but are now the starting point of many of the most exciting new scientific developments. We discuss here recent progress in identifying and understanding interaction networks, new tools that use them in predictive ways in exciting areas of biology, and how they have become the focus of many efforts to study, design and tinker with biological systems, with applications in biomedicine, bioengineering, ecology and beyond.

Discrimination between stable and dynamic components of protein complexes by means of quantitative proteomics

To discriminate between stable and dynamic protein-protein interactions, we propose a strategy in which cells with and without tagged bait are differentially labeled with stable isotope and combined prior to complex purification. Mass-spectrometric analysis of the purified complexes identifies stable and dynamic components as those derived exclusively from the tagged cells and those from both cells, respectively. We successfully applied this strategy to analyze two yeast protein complexes, eIF2B-eIF2 and cyclin-Cdc28.

Charting plant interactomes: possibilities and challenges

Protein-protein interactions are essential for nearly all cellular processes. Therefore, an important goal of post-genomic research for defining gene function and understanding the function of macromolecular complexes involves creating 'interactome' maps from empirical or inferred datasets. Systematic efforts to conduct high-throughput surveys of protein-protein interactions in plants are needed to chart the complex and dynamic interaction networks that occur throughout plant development. However, no single approach can build a complete map of the interactome. Here, we review the utility and potential of various experimental approaches for creating large-scale protein-protein interaction maps in plants. Bioinformatics approaches for curating and assessing the confidence of these datasets through inter-species comparisons will be crucial in achieving a complete understanding of protein interaction networks in plants.

Towards a molecular characterisation of pathological pathways

The dominant conceptual reductionism in drug discovery has resulted in many promising drug candidates to fail during the last clinical phases, mainly due to a lack of knowledge about the patho-physiological pathways they are acting on. Consequently, to increase the revenues of the drug discovery process, we need to improve our understanding of the molecular mechanisms underlying complex cellular processes and consider each potential drug target in its full biological context. Here, we review several strategies that combine computational and experimental techniques, and suggest a systems pathology approach that will ultimately lead to a better comprehension of the molecular bases of disease.