Recurated protein interaction datasets

Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients

Type 1 Gaucher disease (GD1) is a rare, autosomal recessive disorder caused by glucocerebrosidase deficiency. Skeletal manifestations represent one of the most debilitating and potentially irreversible complications of GD1. Although imaging studies are the gold standard, early diagnostic/prognostic tools, such as molecular biomarkers, are needed for the rapid management of skeletal complications. This study aimed to identify potential protein biomarkers capable of predicting the early diagnosis of bone skeletal complications in GD1 patients using artificial intelligence. An in silico study was performed using the novel Therapeutic Performance Mapping System methodology to construct mathematical models of GD1-associated complications at the protein level. Pathophysiological characterization was performed before modeling, and a data science strategy was applied to the predicted protein activity for each protein in the models to identify classifiers. Statistical criteria were used to prioritize the most promising candidates, and 18 candidates were identified. Among them, PDGFB, IL1R2, PTH and CCL3 (MIP-1α) were highlighted due to their ease of measurement in blood. This study proposes a validated novel tool to discover new protein biomarkers to support clinician decision-making in an area where medical needs have not yet been met. However, confirming the results using in vitro and/or in vivo studies is necessary.

Proteomics based drug repositioning applied to improve in vitro fertilization implantation: an artificial intelligence model

Embryo implantation is one of the most inefficient steps in assisted reproduction, so the identifying drugs with a potential clinical application to improve it has a strong interest. This work applies artificial intelligence and systems biology-based mathematical modeling strategies to unveil potential treatments by computationally analyzing and integrating available molecular and clinical data from patients. The mathematical models of embryo implantation computationally generated here simulate the molecular networks underneath this biological process. Once generated, these models were analyzed in order to identify potential repositioned drugs (drugs already used for other indications) able to improve embryo implantation by modulating the molecular pathways involved. Interestingly, the repositioning analysis has identified drugs considering two endpoints: (1) drugs able to modulate the activity of proteins whose role in embryo implantation is already bibliographically acknowledged, and (2) drugs that modulate key proteins in embryo implantation previously predicted through a mechanistic analysis of the mathematical models. This second approach increases the scope open for examination and potential novelty of the repositioning strategy. As a result, a list of 23 drug candidates to improve embryo implantation after IVF was identified by the mathematical models. This list includes many of the compounds already tested for this purpose, which reinforces the predictive capacity of our approach, together with novel repositioned candidates (e.g., Infliximab, Polaprezinc, and Amrinone). In conclusion, the present study exploits existing molecular and clinical information to offer new hypotheses regarding molecular mechanisms in embryo implantation and therapeutic candidates to improve it. This information will be very useful to guide future research.Abbreviations: IVF: in vitro fertilization; EI: Embryo implantation; TPMS: Therapeutic Performance Mapping System; MM: mathematical models; ANN: Artificial Neuronal Networks; TNFα: tumour necrosis factor factor-alpha; HSPs: heat shock proteins; VEGF: vascular endothelial growth factor; PPARA: peroxisome proliferator activated receptor-α PXR: pregnane X receptor; TTR: transthyretin; BED: Biological Effectors Database; MLP: multilayer perceptron.

Prioritizing Molecular Biomarkers in Asthma and Respiratory Allergy Using Systems Biology

Highly prevalent respiratory diseases such as asthma and allergy remain a pressing health challenge. Currently, there is an unmet need for precise diagnostic tools capable of predicting the great heterogeneity of these illnesses. In a previous study of 94 asthma/respiratory allergy biomarker candidates, we defined a group of potential biomarkers to distinguish clinical phenotypes (i.e. nonallergic asthma, allergic asthma, respiratory allergy without asthma) and disease severity. Here, we analyze our experimental results using complex algorithmic approaches that establish holistic disease models (systems biology), combining these insights with information available in specialized databases developed worldwide. With this approach, we aim to prioritize the most relevant biomarkers according to their specificity and mechanistic implication with molecular motifs of the diseases. The Therapeutic Performance Mapping System (Anaxomics’ TPMS technology) was used to generate one mathematical model per disease: allergic asthma (AA), non-allergic asthma (NA), and respiratory allergy (RA), defining specific molecular motifs for each. The relationship of our molecular biomarker candidates and each disease was analyzed by artificial neural networks (ANNs) scores. These analyses prioritized molecular biomarkers specific to the diseases and to particular molecular motifs. As a first step, molecular characterization of the pathophysiological processes of AA defined 16 molecular motifs: 2 specific for AA, 2 shared with RA, and 12 shared with NA. Mechanistic analysis showed 17 proteins that were strongly related to AA. Eleven proteins were associated with RA and 16 proteins with NA. Specificity analysis showed that 12 proteins were specific to AA, 7 were specific to RA, and 2 to NA. Finally, a triggering analysis revealed a relevant role for AKT1, STAT1, and MAPK13 in all three conditions and for TLR4 in asthmatic diseases (AA and NA). In conclusion, this study has enabled us to prioritize biomarkers depending on the functionality associated with each disease and with specific molecular motifs, which could improve the definition and usefulness of new molecular biomarkers.

Dynamic rewiring of the human interactome by interferon signaling

BACKGROUND

The type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes. Comprehensive catalogs of IFN-stimulated genes have been established across species and cell types by transcriptomic and biochemical approaches, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to describe the effects of IFN signaling on the human proteome, and apply protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.

RESULTS

We identify > 26,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer IFN-stimulated gene protein synthesis.

CONCLUSIONS

Our map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing IFN-stimulated genes in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

Constructing knowledge graphs and their biomedical applications

Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated systems. A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications. This representation is designed to preserve a knowledge graph's local and/or global structure. Additional machine learning methods can be applied to this representation to make predictions within genomic, pharmaceutical, and clinical domains. We frame our discussion first around knowledge graph construction and then around unifying representational learning techniques and unifying applications. Advances in machine learning for biomedicine are creating new opportunities across many domains, and we note potential avenues for future work with knowledge graphs that appear particularly promising.

Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization

Identifying immune correlates of protection and mechanisms of immunity accelerates and streamlines the development of vaccines. RTS,S/AS01E, the most clinically advanced malaria vaccine, has moderate efficacy in African children. In contrast, immunization with sporozoites under antimalarial chemoprophylaxis (CPS immunization) can provide 100% sterile protection in naïve adults. We used systems biology approaches to identifying correlates of vaccine-induced immunity based on transcriptomes of peripheral blood mononuclear cells from individuals immunized with RTS,S/AS01E or chemoattenuated sporozoites stimulated with parasite antigens in vitro. Specifically, we used samples of individuals from two age cohorts and three African countries participating in an RTS,S/AS01E pediatric phase 3 trial and malaria-naïve individuals participating in a CPS trial. We identified both preimmunization and postimmunization transcriptomic signatures correlating with protection. Signatures were validated in independent children and infants from the RTS,S/AS01E phase 3 trial and individuals from an independent CPS trial with high accuracies (>70%). Transcription modules revealed interferon, NF-κB, Toll-like receptor (TLR), and monocyte-related signatures associated with protection. Preimmunization signatures suggest that priming the immune system before vaccination could potentially improve vaccine immunogenicity and efficacy. Last, signatures of protection could be useful to determine efficacy in clinical trials, accelerating vaccine candidate testing. Nevertheless, signatures should be tested more extensively across multiple cohorts and trials to demonstrate their universal predictive capacity.

In silico epigenetics of metal exposure and subclinical atherosclerosis in middle aged men: pilot results from the Aragon Workers Health Study

We explored the association of metal levels with subclinical atherosclerosis and epigenetic changes in relevant biological pathways. Whole blood DNA Infinium Methylation 450 K data were obtained from 23 of 73 middle age men without clinically evident cardiovascular disease (CVD) who participated in the Aragon Workers Health Study in 2009 (baseline visit) and had available baseline urinary metals and subclinical atherosclerosis measures obtained in 2010-2013 (follow-up visit). The median metal levels were 7.36 µg g^-1, 0.33 µg g^-1, 0.11 µg g^-1 and 0.07 µg g^-1, for arsenic (sum of inorganic and methylated species), cadmium, antimony and tungsten, respectively. Urine cadmium and tungsten were associated with femoral and carotid intima-media thickness, respectively (Pearson's r = 0.27; p = 0.03 in both cases). Among nearest genes to identified differentially methylated regions (DMRs), 46% of metal-DMR genes overlapped with atherosclerosis-DMR genes (p < 0.001). Pathway enrichment analysis of atherosclerosis-DMR genes showed a role in inflammatory, metabolic and transport pathways. In in silico protein-to-protein interaction networks among proteins encoded by 162 and 108 genes attributed to atherosclerosis- and metal-DMRs, respectively, with proteins known to have a role in atherosclerosis pathways, we observed hub proteins in the network associated with both atherosclerosis and metal-DMRs (e.g. SMAD3 and NOP56), and also hub proteins associated with metal-DMRs only but with relevant connections with atherosclerosis effectors (e.g. SSTR5, HDAC4, AP2A2, CXCL12 and SSTR4). Our integrative in silico analysis demonstrates the feasibility of identifying epigenomic regions linked to environmental exposures and potentially involved in relevant pathways for human diseases. While our results support the hypothesis that metal exposures can influence health due to epigenetic changes, larger studies are needed to confirm our pilot results.This article is part of a discussion meeting issue 'Frontiers in epigenetic chemical biology'.

The BioGRID interaction database: 2019 update

The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2018 (build 3.4.164), BioGRID contains records for 1 598 688 biological interactions manually annotated from 55 809 publications for 71 species, as classified by an updated set of controlled vocabularies for experimental detection methods. BioGRID also houses records for >700 000 post-translational modification sites. BioGRID now captures chemical interaction data, including chemical-protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature. A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene-phenotype and gene-gene relationships. An extension of the BioGRID resource called the Open Repository for CRISPR Screens (ORCS) database (https://orcs.thebiogrid.org) currently contains over 500 genome-wide screens carried out in human or mouse cell lines. All data in BioGRID is made freely available without restriction, is directly downloadable in standard formats and can be readily incorporated into existing applications via our web service platforms. BioGRID data are also freely distributed through partner model organism databases and meta-databases.

A network-based meta-analysis for characterizing the genetic landscape of human aging

Great amounts of omics data are generated in aging research, but their diverse and partly complementary nature requires integrative analysis approaches for investigating aging processes and connections to age-related diseases. To establish a broader picture of the genetic and epigenetic landscape of human aging we performed a large-scale meta-analysis of 6600 human genes by combining 35 datasets that cover aging hallmarks, longevity, changes in DNA methylation and gene expression, and different age-related diseases. To identify biological relationships between aging-associated genes we incorporated them into a protein interaction network and characterized their network neighborhoods. In particular, we computed a comprehensive landscape of more than 1000 human aging clusters, network regions where genes are highly connected and where gene products commonly participate in similar processes. In addition to clusters that capture known aging processes such as nutrient-sensing and mTOR signaling, we present a number of clusters with a putative functional role in linking different aging processes as promising candidates for follow-up studies. To enable their detailed exploration, all datasets and aging clusters are made freely available via an interactive website (https://gemex.eurac.edu/bioinf/age/).

A scored human protein-protein interaction network to catalyze genomic interpretation

Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.

A Network Pharmacology Approach to Uncover the Pharmacological Mechanism of XuanHuSuo Powder on Osteoarthritis

As the most familiar type of arthritis and a chronic illness of the joints, Osteoarthritis (OA) affects a great number of people on the global scale. XuanHuSuo powder (XHSP), a conventional herbal formula from China, has been extensively applied in OA treatment. Nonetheless, its pharmacological mechanism has not been completely expounded. In this research, a network pharmacology approach has been chosen to study the pharmacological mechanism of XHSP on OA, and the pharmacology networks were established based on the relationship between four herbs found in XHSP, compound targets, and OA targets. The pathway enrichment analysis revealed that the significant bioprocess networks of XHSP on OA were regulation of inflammation, interleukin-1β (IL-1β) production and nitric oxide (NO) biosynthetic process, response to cytokine or estrogen stimuli, and antiapoptosis. These effects have not been reported previously. The comprehensive network pharmacology approach developed by our research has revealed, for the first time, a connection between four herbs found in XHSP, corresponding compound targets, and OA pathway systems that are conducive to expanding the clinical application of XHSP. The proposed network pharmacology approach could be a promising complementary method by which researchers might better evaluate multitarget or multicomponent drugs on a systematic level.

Novel Neuroprotective Multicomponent Therapy for Amyotrophic Lateral Sclerosis Designed by Networked Systems

Amyotrophic Lateral Sclerosis is a fatal, progressive neurodegenerative disease characterized by loss of motor neuron function for which there is no effective treatment. One of the main difficulties in developing new therapies lies on the multiple events that contribute to motor neuron death in amyotrophic lateral sclerosis. Several pathological mechanisms have been identified as underlying events of the disease process, including excitotoxicity, mitochondrial dysfunction, oxidative stress, altered axonal transport, proteasome dysfunction, synaptic deficits, glial cell contribution, and disrupted clearance of misfolded proteins. Our approach in this study was based on a holistic vision of these mechanisms and the use of computational tools to identify polypharmacology for targeting multiple etiopathogenic pathways. By using a repositioning analysis based on systems biology approach (TPMS technology), we identified and validated the neuroprotective potential of two new drug combinations: Aliretinoin and Pranlukast, and Aliretinoin and Mefloquine. In addition, we estimated their molecular mechanisms of action in silico and validated some of these results in a well-established in vitro model of amyotrophic lateral sclerosis based on cultured spinal cord slices. The results verified that Aliretinoin and Pranlukast, and Aliretinoin and Mefloquine promote neuroprotection of motor neurons and reduce microgliosis.

Implying analytic measures for unraveling rheumatoid arthritis significant proteins through drug target interaction

Rheumatoid arthritis (RA) is a systemic auto-immune and inflammatory disease that mainly alters the synovial joints and ultimately leads to their destruction. The involvement of the immune system and its related cells is a basic trademark of auto-immune associated diseases. The present work focuses on network analysis and its functional characterization to predict novel targets for RA. The interactive model called as Rheumatoid Arthritis Drug-Target-Protein (RA-DTP) is built of 1727 nodes and 7954 edges followed the power law distribution. RADTP comprised of 20 islands, 55 modules and 123 sub modules. Good interactome coverage of target-protein was detected in Island 2 (Q-Score 0.875) which includes 673 molecules with 20 modules and 68 sub modules. The biological landscape of these modules was examined based on the participation molecules in specific cellular localization, molecular function and biological pathway with favourable p value. Functional characterization and pathway analysis through KEGG, Biocarta and Reactome also showed their involvement in relation to the immune system and inflammatory processes and biological processes such as cell signalling and communication, glucosamine metabolic process, Renin Angiotensin system, BCR signals, Galactose metabolism, MAPK signalling, Complement and Coagulation system and NGF signalling pathways. Traffic values and centrality parameters were applied as the selection criteria for identifying potential targets from the important hubs which resulted into FOS, KNG1, PTGDS, HSP90AA1, REN, POMC, FCER1G, IL6, ICAM1, SGK1, NOS3 and PLA2G4A. This approach provides an insight to experimental validation of these associations of potential targets for clinical value to find their effect on animal studies.

Curation accuracy of model organism databases

Manual extraction of information from the biomedical literature-or biocuration-is the central methodology used to construct many biological databases. For example, the UniProt protein database, the EcoCyc Escherichia coli database and the Candida Genome Database (CGD) are all based on biocuration. Biological databases are used extensively by life science researchers, as online encyclopedias, as aids in the interpretation of new experimental data and as golden standards for the development of new bioinformatics algorithms. Although manual curation has been assumed to be highly accurate, we are aware of only one previous study of biocuration accuracy. We assessed the accuracy of EcoCyc and CGD by manually selecting curated assertions within randomly chosen EcoCyc and CGD gene pages and by then validating that the data found in the referenced publications supported those assertions. A database assertion is considered to be in error if that assertion could not be found in the publication cited for that assertion. We identified 10 errors in the 633 facts that we validated across the two databases, for an overall error rate of 1.58%, and individual error rates of 1.82% for CGD and 1.40% for EcoCyc. These data suggest that manual curation of the experimental literature by Ph.D-level scientists is highly accurate. Database URL: http://ecocyc.org/, http://www.candidagenome.org//

Protein-protein interactions and genetic diseases: The interactome

Protein-protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little question that the latest screens are of high quality. Moreover, common data standards and coordinated curation practices between the databases that collect the interactions have made these valuable data available to a wide group of researchers. Here, I will review how protein-protein interactions are measured, collected and quality controlled. I discuss how the architecture of molecular protein networks has informed disease biology, and how these data are now being computationally integrated with the newest genomic technologies, in particular genome-wide association studies and exome-sequencing projects, to improve our understanding of molecular processes perturbed by genetics in human diseases. This article is part of a Special Issue entitled: From Genome to Function.

Navigating the global protein-protein interaction landscape using iRefWeb

iRefWeb is a bioinformatics resource that offers access to a large collection of data on protein-protein interactions in over a thousand organisms. This collection is consolidated from 14 major public databases that curate the scientific literature. The collection is enhanced with a range of versatile data filters and search options that categorize various types of protein-protein interactions and protein complexes. Users of iRefWeb are able to retrieve all curated interactions for a given organism or those involving a given protein (or a list of proteins), narrow down their search results based on different supporting evidence, and assess the reliability of these interactions using various criteria. They may also examine all data and annotations related to any publication that described the interaction-detection experiments. iRefWeb is freely available to the research community worldwide at http://wodaklab.org/iRefWeb .

Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments

Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., co-occurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability. The scoring methods and platforms described include: CompPASS, SAINT, Decontaminator, MINT, IntAct, STRING, and FunCoup. References to related work are provided throughout in order to provide a concise but thorough introduction to a rapidly growing interdisciplinary field of investigation.

Protein interaction data curation: the International Molecular Exchange (IMEx) consortium

The International Molecular Exchange (IMEx) consortium is an international collaboration between major public interaction data providers to share literature-curation efforts and make a nonredundant set of protein interactions available in a single search interface on a common website (http://www.imexconsortium.org/). Common curation rules have been developed, and a central registry is used to manage the selection of articles to enter into the dataset. We discuss the advantages of such a service to the user, our quality-control measures and our data-distribution practices.

Network compression as a quality measure for protein interaction networks

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients.

An interaction map of endoplasmic reticulum chaperones and foldases

Chaperones and foldases in the endoplasmic reticulum (ER) ensure correct protein folding. Extensive protein-protein interaction maps have defined the organization and function of many cellular complexes, but ER complexes are under-represented. Consequently, chaperone and foldase networks in the ER are largely uncharacterized. Using complementary ER-specific methods, we have mapped interactions between ER-lumenal chaperones and foldases and describe their organization in multiprotein complexes. We identify new functional chaperone modules, including interactions between protein-disulfide isomerases and peptidyl-prolyl cis-trans-isomerases. We have examined in detail a novel ERp72-cyclophilin B complex that enhances the rate of folding of immunoglobulin G. Deletion analysis and NMR reveal a conserved surface of cyclophilin B that interacts with polyacidic stretches of ERp72 and GRp94. Mutagenesis within this highly charged surface region abrogates interactions with its chaperone partners and reveals a new mechanism of ER protein-protein interaction. This ability of cyclophilin B to interact with different partners using the same molecular surface suggests that ER-chaperone/foldase partnerships may switch depending on the needs of different substrates, illustrating the flexibility of multichaperone complexes of the ER folding machinery.

Analyzing protein-protein interaction networks

The advent of the "omics" era in biology research has brought new challenges and requires the development of novel strategies to answer previously intractable questions. Molecular interaction networks provide a framework to visualize cellular processes, but their complexity often makes their interpretation an overwhelming task. The inherently artificial nature of interaction detection methods and the incompleteness of currently available interaction maps call for a careful and well-informed utilization of this valuable data. In this tutorial, we aim to give an overview of the key aspects that any researcher needs to consider when working with molecular interaction data sets and we outline an example for interactome analysis. Using the molecular interaction database IntAct, the software platform Cytoscape, and its plugins BiNGO and clusterMaker, and taking as a starting point a list of proteins identified in a mass spectrometry-based proteomics experiment, we show how to build, visualize, and analyze a protein-protein interaction network.

Toward a systems level view of the ECM and related proteins: a framework for the systematic definition and analysis of biological systems

Advances in high throughput 'omic technologies are starting to provide unprecedented insights into how components of biological systems are organized and interact. Key to exploiting these datasets is the definition of the components that comprise the system of interest. Although a variety of knowledge bases exist that capture such information, a major challenge is determining how these resources may be best utilized. Here we present a systematic curation strategy to define a systems-level view of the human extracellular matrix (ECM)--a three-dimensional meshwork of proteins and polysaccharides that impart structure and mechanical stability to tissues. Employing our curation strategy we define a set of 357 proteins that represent core components of the ECM, together with an additional 524 genes that mediate related functional roles, and construct a map of their physical interactions. Topological properties help identify modules of functionally related proteins, including those involved in cell adhesion, bone formation and blood clotting. Because of its major role in cell adhesion, proliferation and morphogenesis, defects in the ECM have been implicated in cancer, atherosclerosis, asthma, fibrosis, and arthritis. We use MeSH annotations to identify modules enriched for specific disease terms that aid to strengthen existing as well as predict novel gene-disease associations. Mapping expression and conservation data onto the network reveal modules evolved in parallel to convey tissue-specific functionality on otherwise broadly expressed units. In addition to demonstrating an effective workflow for defining biological systems, this study crystallizes our current knowledge surrounding the organization of the ECM.

Protein interactions: mapping interactome networks to support drug target discovery and selection

Proteins are biomolecular structures that build the microscopic working machinery of any living system. Proteins within the cells and biological systems do not act alone, but rather team up into macromolecular structures enclosing intricate physicochemical dynamic connections to undertake biological functions. A critical step towards unraveling the complex molecular relationships in living systems is the mapping of protein-to-protein physical "interactions". The complete map of protein interactions that can occur in a living organism is called the "interactome". Achieving an adequate atlas of all the protein interactions within a living system should allow to build its interaction network and to identity the "central nodes" that can be critical for the function, the homeostasis, and the movement of such system. Focusing on human studies, the data about the human interactome are most relevant for current biomedical research, because it is clear that the location of the proteins in the interactome network will allow to evaluate their centrality and to redefine the potential value of each protein as a drug target. This chapter presents our current knowledge on the human protein-protein interactome and explains how such knowledge can help us to select adequate targets for drugs.

Network systems biology for targeted cancer therapies

The era of targeted cancer therapies has arrived. However, due to the complexity of biological systems, the current progress is far from enough. From biological network modeling to structural/dynamic network analysis, network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells. It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.

Conserved BK channel-protein interactions reveal signals relevant to cell death and survival

The large-conductance Ca²⁺-activated K⁺ (BK) channel and its β-subunit underlie tuning in non-mammalian sensory or hair cells, whereas in mammals its function is less clear. To gain insights into species differences and to reveal putative BK functions, we undertook a systems analysis of BK and BK-Associated Proteins (BKAPS) in the chicken cochlea and compared these results to other species. We identified 110 putative partners from cytoplasmic and membrane/cytoskeletal fractions, using a combination of coimmunoprecipitation, 2-D gel, and LC-MS/MS. Partners included 14-3-3γ, valosin-containing protein (VCP), stathmin (STMN), cortactin (CTTN), and prohibitin (PHB), of which 16 partners were verified by reciprocal coimmunoprecipitation. Bioinformatics revealed binary partners, the resultant interactome, subcellular localization, and cellular processes. The interactome contained 193 proteins involved in 190 binary interactions in subcellular compartments such as the ER, mitochondria, and nucleus. Comparisons with mice showed shared hub proteins that included N-methyl-D-aspartate receptor (NMDAR) and ATP-synthase. Ortholog analyses across six species revealed conserved interactions involving apoptosis, Ca²⁺ binding, and trafficking, in chicks, mice, and humans. Functional studies using recombinant BK and RNAi in a heterologous expression system revealed that proteins important to cell death/survival, such as annexinA5, γ-actin, lamin, superoxide dismutase, and VCP, caused a decrease in BK expression. This revelation led to an examination of specific kinases and their effectors relevant to cell viability. Sequence analyses of the BK C-terminus across 10 species showed putative binding sites for 14-3-3, RAC-α serine/threonine-protein kinase 1 (Akt), glycogen synthase kinase-3β (GSK3β) and phosphoinositide-dependent kinase-1 (PDK1). Knockdown of 14-3-3 and Akt caused an increase in BK expression, whereas silencing of GSK3β and PDK1 had the opposite effect. This comparative systems approach suggests conservation in BK function across different species in addition to novel functions that may include the initiation of signals relevant to cell death/survival.

Drug-target network in myocardial infarction reveals multiple side effects of unrelated drugs

The systems-level characterization of drug-target associations in myocardial infarction (MI) has not been reported to date. We report a computational approach that combines different sources of drug and protein interaction information to assemble the myocardial infarction drug-target interactome network (My-DTome). My-DTome comprises approved and other drugs interlinked in a single, highly-connected network with modular organization. We show that approved and other drugs may both be highly connected and represent network bottlenecks. This highlights influential roles for such drugs on seemingly unrelated targets and pathways via direct and indirect interactions. My-DTome modules are associated with relevant molecular processes and pathways. We find evidence that these modules may be regulated by microRNAs with potential therapeutic roles in MI. Different drugs can jointly impact a module. We provide systemic insights into cardiovascular effects of non-cardiovascular drugs. My-DTome provides the basis for an alternative approach to investigate new targets and multidrug treatment in MI.

Information encoded in a network of inflammation proteins predicts clinical outcome after myocardial infarction

Background

Inflammation plays an important role in cardiac repair after myocardial infarction (MI). Nevertheless, the systems-level characterization of inflammation proteins in MI remains incomplete. There is a need to demonstrate the potential value of molecular network-based approaches to translational research. We investigated the interplay of inflammation proteins and assessed network-derived knowledge to support clinical decisions after MI. The main focus is the prediction of clinical outcome after MI.

Methods

We assembled My-Inflamome, a network of protein interactions related to inflammation and prognosis in MI. We established associations between network properties, disease biology and capacity to distinguish between prognostic categories. The latter was tested with classification models built on blood-derived microarray data from post-MI patients with different outcomes. This was followed by experimental verification of significant associations.

Results

My-Inflamome is organized into modules highly specialized in different biological processes relevant to heart repair. Highly connected proteins also tend to be high-traffic components. Such bottlenecks together with genes extracted from the modules provided the basis for novel prognostic models, which could not have been uncovered by standard analyses. Modules with significant involvement in transcriptional regulation are targeted by a small set of microRNAs. We suggest a new panel of gene expression biomarkers (TRAF2, SHKBP1 and UBC) with high discriminatory capability. Follow-up validations reported promising outcomes and motivate future research.

Conclusion

This study enhances understanding of the interaction network that executes inflammatory responses in human MI. Network-encoded information can be translated into knowledge with potential prognostic application. Independent evaluations are required to further estimate the clinical relevance of the new prognostic genes.

Predictive integration of gene functional similarity and co-expression defines treatment response of endothelial progenitor cells

Background

Endothelial progenitor cells (EPCs) have been implicated in different processes crucial to vasculature repair, which may offer the basis for new therapeutic strategies in cardiovascular disease. Despite advances facilitated by functional genomics, there is a lack of systems-level understanding of treatment response mechanisms of EPCs. In this research we aimed to characterize the EPCs response to adenosine (Ado), a cardioprotective factor, based on the systems-level integration of gene expression data and prior functional knowledge. Specifically, we set out to identify novel biosignatures of Ado-treatment response in EPCs.

Results

The predictive integration of gene expression data and standardized functional similarity information enabled us to identify new treatment response biosignatures. Gene expression data originated from Ado-treated and -untreated EPCs samples, and functional similarity was estimated with Gene Ontology (GO)-based similarity information. These information sources enabled us to implement and evaluate an integrated prediction approach based on the concept of k-nearest neighbours learning (kNN). The method can be executed by expert- and data-driven input queries to guide the search for biologically meaningful biosignatures. The resulting integrated kNN system identified new candidate EPC biosignatures that can offer high classification performance (areas under the operating characteristic curve > 0.8). We also showed that the proposed models can outperform those discovered by standard gene expression analysis. Furthermore, we report an initial independent in vitro experimental follow-up, which provides additional evidence of the potential validity of the top biosignature.

Conclusion

Response to Ado treatment in EPCs can be accurately characterized with a new method based on the combination of gene co-expression data and GO-based similarity information. It also exploits the incorporation of human expert-driven queries as a strategy to guide the automated search for candidate biosignatures. The proposed biosignature improves the systems-level characterization of EPCs. The new integrative predictive modeling approach can also be applied to other phenotype characterization or biomarker discovery problems.

Assembling a protein-protein interaction map of the SSU processome from existing datasets

Background

The small subunit (SSU) processome is a large ribonucleoprotein complex involved in small ribosomal subunit assembly. It consists of the U3 snoRNA and ∼72 proteins. While most of its components have been identified, the protein-protein interactions (PPIs) among them remain largely unknown, and thus the assembly, architecture and function of the SSU processome remains unclear.

Methodology

We queried PPI databases for SSU processome proteins to quantify the degree to which the three genome-wide high-throughput yeast two-hybrid (HT-Y2H) studies, the genome-wide protein fragment complementation assay (PCA) and the literature-curated (LC) datasets cover the SSU processome interactome.

Conclusions

We find that coverage of the SSU processome PPI network is remarkably sparse. Two of the three HT-Y2H studies each account for four and six PPIs between only six of the 72 proteins, while the third study accounts for as little as one PPI and two proteins. The PCA dataset has the highest coverage among the genome-wide studies with 27 PPIs between 25 proteins. The LC dataset was the most extensive, accounting for 34 proteins and 38 PPIs, many of which were validated by independent methods, thereby further increasing their reliability. When the collected data were merged, we found that at least 70% of the predicted PPIs have yet to be determined and 26 proteins (36%) have no known partners. Since the SSU processome is conserved in all Eukaryotes, we also queried HT-Y2H datasets from six additional model organisms, but only four orthologues and three previously known interologous interactions were found. This provides a starting point for further work on SSU processome assembly, and spotlights the need for a more complete genome-wide Y2H analysis.

Large-scale protein interactome mapping: strategies and opportunities

Interactions between proteins are central to any cellular process, and mapping these into a protein network is informative both for the function of individual proteins and the functional organization of the cell as a whole. Many strategies have been developed that are up to this task, and the last 10 years have seen the high-throughput application of a number of those in large-scale, sometimes proteome-wide, interactome mapping efforts. Although initially the quality of the data produced in these screening campaigns has been questioned, quality standards and empirical validation schemes are now in place to ensure high-quality data generation. Through their integration with other 'omics' data, interactomics datasets have proven highly valuable towards applications in different areas of clinical importance.

High-throughput analyses and curation of protein interactions in yeast

The yeast Saccharomyces cerevisiae is the model organism in which protein interactions have been most extensively analyzed. The vast majority of these interactions have been characterized by a variety of sophisticated high-throughput techniques probing different aspects of protein association. This chapter summarizes the major techniques, highlights their complementary nature, discusses the data they produce, and highlights some of the biases from which they suffer. A main focus is the key role played by computational methods for processing, analyzing, and validating the large body of noisy data produced by the experimental procedures. It also describes how computational methods are used to extend the coverage and reliability of protein interaction data by integrating information from heterogeneous sources and reviews the current status of literature-curated data on yeast protein interactions stored in specialized databases.

Literature curation of protein interactions: measuring agreement across major public databases

Literature curation of protein interaction data faces a number of challenges. Although curators increasingly adhere to standard data representations, the data that various databases actually record from the same published information may differ significantly. Some of the reasons underlying these differences are well known, but their global impact on the interactions collectively curated by major public databases has not been evaluated. Here we quantify the agreement between curated interactions from 15 471 publications shared across nine major public databases. Results show that on average, two databases fully agree on 42% of the interactions and 62% of the proteins curated from the same publication. Furthermore, a sizable fraction of the measured differences can be attributed to divergent assignments of organism or splice isoforms, different organism focus and alternative representations of multi-protein complexes. Our findings highlight the impact of divergent curation policies across databases, and should be relevant to both curators and data consumers interested in analyzing protein-interaction data generated by the scientific community.

Database URL:http://wodaklab.org/iRefWeb

New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size

Background

As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required.

Results

We examined the yeast interactome from a new perspective, by taking into account how thoroughly proteins have been studied. We discovered that the set of literature-curated protein-protein interactions is qualitatively different when restricted to proteins that have received extensive attention from the scientific community. In particular, these interactions are less often supported by yeast two-hybrid, and more often by more complex experiments such as biochemical activity assays. Our analysis showed that high-throughput and literature-curated interactome datasets are more correlated than commonly assumed, but that this bias can be corrected for by focusing on well-studied proteins. We thus propose a simple and reliable method to estimate the size of an interactome, combining literature-curated data involving well-studied proteins with high-throughput data. It yields an estimate of at least 37, 600 direct physical protein-protein interactions in S. cerevisiae.

Conclusions

Our method leads to higher and more accurate estimates of the interactome size, as it accounts for interactions that are genuine yet difficult to detect with commonly-used experimental assays. This shows that we are even further from completing the yeast interactome map than previously expected.

Revealing the molecular relationship between type 2 diabetes and the metabolic changes induced by a very-low-carbohydrate low-fat ketogenic diet

Background

The prevalence of type 2 diabetes is increasing worldwide, accounting for 85-95% of all diagnosed cases of diabetes. Clinical trials provide evidence of benefits of low-carbohydrate ketogenic diets in terms of clinical outcomes on type 2 diabetes patients. However, the molecular events responsible for these improvements still remain unclear in spite of the high amount of knowledge on the primary mechanisms of both the diabetes and the metabolic state of ketosis. Molecular network analysis of conditions, diseases and treatments might provide new insights and help build a better understanding of clinical, metabolic and molecular relationships among physiological conditions. Accordingly, our aim is to reveal such a relationship between a ketogenic diet and type 2 diabetes through systems biology approaches.

Methods

Our systemic approach is based on the creation and analyses of the cell networks representing the metabolic state in a very-low-carbohydrate low-fat ketogenic diet. This global view might help identify unnoticed relationships often overlooked in molecule or process-centered studies.

Results

A strong relationship between the insulin resistance pathway and the ketosis main pathway was identified, providing a possible explanation for the improvement observed in clinical trials. Moreover, the map analyses permit the formulation of some hypothesis on functional relationships between the molecules involved in type 2 diabetes and induced ketosis, suggesting, for instance, a direct implication of glucose transporters or inflammatory processes. The molecular network analysis performed in the ketogenic-diet map, from the diabetes perspective, has provided insights on the potential mechanism of action, but also has opened new possibilities to study the applications of the ketogenic diet in other situations such as CNS or other metabolic dysfunctions.

iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence

We present iRefWeb, a web interface to protein interaction data consolidated from 10 public databases: BIND, BioGRID, CORUM, DIP, IntAct, HPRD, MINT, MPact, MPPI and OPHID. iRefWeb enables users to examine aggregated interactions for a protein of interest, and presents various statistical summaries of the data across databases, such as the number of organism-specific interactions, proteins and cited publications. Through links to source databases and supporting evidence, researchers may gauge the reliability of an interaction using simple criteria, such as the detection methods, the scale of the study (high- or low-throughput) or the number of cited publications. Furthermore, iRefWeb compares the information extracted from the same publication by different databases, and offers means to follow-up possible inconsistencies. We provide an overview of the consolidated protein–protein interaction landscape and show how it can be automatically cropped to aid the generation of meaningful organism-specific interactomes. iRefWeb can be accessed at: http://wodaklab.org/iRefWeb.

Database URL: http://wodaklab.org/iRefWeb/

Curating the innate immunity interactome

BACKGROUND

The innate immune response is the first line of defence against invading pathogens and is regulated by complex signalling and transcriptional networks. Systems biology approaches promise to shed new light on the regulation of innate immunity through the analysis and modelling of these networks. A key initial step in this process is the contextual cataloguing of the components of this system and the molecular interactions that comprise these networks. InnateDB (http://www.innatedb.com) is a molecular interaction and pathway database developed to facilitate systems-level analyses of innate immunity.

RESULTS

Here, we describe the InnateDB curation project, which is manually annotating the human and mouse innate immunity interactome in rich contextual detail, and present our novel curation software system, which has been developed to ensure interactions are curated in a highly accurate and data-standards compliant manner. To date, over 13,000 interactions (protein, DNA and RNA) have been curated from the biomedical literature. Here, we present data, illustrating how InnateDB curation of the innate immunity interactome has greatly enhanced network and pathway annotation available for systems-level analysis and discuss the challenges that face such curation efforts. Significantly, we provide several lines of evidence that analysis of the innate immunity interactome has the potential to identify novel signalling, transcriptional and post-transcriptional regulators of innate immunity. Additionally, these analyses also provide insight into the cross-talk between innate immunity pathways and other biological processes, such as adaptive immunity, cancer and diabetes, and intriguingly, suggests links to other pathways, which as yet, have not been implicated in the innate immune response.

CONCLUSIONS

In summary, curation of the InnateDB interactome provides a wealth of information to enable systems-level analysis of innate immunity.

Mapping plant interactomes using literature curated and predicted protein-protein interaction data sets

Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein-protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.