BioNetBuilder: automatic integration of biological networks

Databases for Protein-Protein Interactions

Protein-protein interaction networks have a crucial role in biological processes. Proteins perform multiple functions in forming physical and functional interactions in cellular systems. Information concerning an enormous number of protein interactions in a wide range of species has accumulated and has been integrated into various resources for molecular biology and systems biology. This chapter provides a review of the representative databases and the major computational methods used for protein-protein interactions.

IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, mapping and mining analysis

Background

Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Using an evolution-based concept, i.e. interolog which refers to interacting orthologous protein sets, pave the way toward an optimal benchmark.

Results

Here, we provide an R package, IMMAN, as a tool for reconstructing Interolog Protein Network (IPN) by integrating several Protein-protein Interaction Networks (PPINs). Users can unify different PPINs to mine conserved common networks among species. IMMAN is designed to retrieve IPNs with different degrees of conservation to engage prediction analysis of protein functions according to their networks.

Conclusions

IPN consists of evolutionarily conserved nodes and their related edges regarding low false positive rates, which can be considered as a gold standard network in the contexts of biological network analysis regarding to those PPINs which is derived from.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2659-y) contains supplementary material, which is available to authorized users.

The proteomic study of serially passaged human skin fibroblast cells uncovers down-regulation of the chromosome condensin complex proteins involved in replicative senescence

Dermal fibroblast is one of the major constitutive cells of skin and plays a central role in skin senescence. The replicative senescence of fibroblasts may cause skin aging, bad wound healing, skin diseases and even cancer. In this study, a label-free quantitative proteomic approach was employed to analyzing the serial passaged human skin fibroblast (CCD-1079Sk) cells, resulting in 3371 proteins identified. Of which, 280 proteins were significantly changed in early passage (6 passages, P6), middle passage (12 passages, P12) and late passage (21 passages, P21), with a time-dependent decrease or increase tendency. Bioinformatic analysis demonstrated that the chromosome condensin complex, including structural maintenance of chromosomes protein 2 (SMC2) and structural maintenance of chromosomes protein 4 (SMC4), were down-regulated in the serially passaged fibroblast cells. The qRT-PCR and Western Blot experiments confirmed that the expression of these two proteins were significantly down-regulated in a time-dependent manner in the subculture of human skin fibroblasts (HSFb cells). In summary, we used serially passaged human skin fibroblast cells coupled with quantitative proteomic approach to profile the protein expression pattern in the temporal progress of replicative senescence in HSFb cells and revealed that the down-regulation of the chromosome condensin complex subunits, such as SMC2 and SMC4, may play an important role in the fibroblast senescence.

Impact of the Type I Interferon Receptor on the Global Gene Expression Program During the Course of Dendritic Cell Maturation Induced by Polyinosinic Polycytidylic Acid

Dendritic cell (DC) maturation involves widespread changes in cellular function and gene expression. The regulatory role of IFNAR in the program of DC maturation remains incompletely defined. Thus, the time evolution impact of IFNAR on this process was evaluated. Changes in DC phenotype, function, and gene expression induced by poly I:C were measured in wild-type and IFNAR(-/-) DC at 9 time points over 24 h. Temporal gene expression profiles were filtered on consistency and response magnitude across replicates. The number of genes whose expression was altered by poly I:C treatment was greatly reduced in IFNAR(-/-) DC, including the majority of the downregulated gene expression program previously observed in wild-type (WT) DC. Furthermore, the number of genes upregulated was almost equal between WT and IFNAR(-/-) DC, yet the identities of those genes were distinct. Integrating these data with protein-protein interaction data revealed several novel subnetworks active during maturation, including nucleotide synthesis, metabolism, and repair. A subnetwork associated with redox activity was uniquely identified in IFNAR(-/-) DC. Overall, temporal gene expression and network analyses identified many genes regulated by the type I interferon response and revealed previously unidentified aspects of the DC maturation process.

Profiling the Proteome of Mycobacterium tuberculosis during Dormancy and Reactivation

Tuberculosis, caused by Mycobacterium tuberculosis, still remains a major global health problem. The main obstacle in eradicating this disease is the ability of this pathogen to remain dormant in macrophages, and then reactivate later under immuno-compromised conditions. The physiology of hypoxic nonreplicating M. tuberculosis is well-studied using many in vitro dormancy models. However, the physiological changes that take place during the shift from dormancy to aerobic growth (reactivation) have rarely been subjected to a detailed investigation. In this study, we developed an in vitro reactivation system by re-aerating the virulent laboratory strain of M. tuberculosis that was made dormant employing Wayne's dormancy model, and compared the proteome profiles of dormant and reactivated bacteria using label-free one-dimensional LC/MS/MS analysis. The proteome of dormant bacteria was analyzed at nonreplicating persistent stage 1 (NRP1) and stage 2 (NRP2), whereas that of reactivated bacteria was analyzed at 6 and 24 h post re-aeration. Proteome of normoxially grown bacteria served as the reference. In total, 1871 proteins comprising 47% of the M. tuberculosis proteome were identified, and many of them were observed to be expressed differentially or uniquely during dormancy and reactivation. The number of proteins detected at different stages of dormancy (764 at NRP1, 691 at NRP2) and reactivation (768 at R6 and 983 at R24) was very low compared with that of the control (1663). The number of unique proteins identified during normoxia, NRP1, NRP2, R6, and R24 were 597, 66, 56, 73, and 94, respectively. We analyzed various biological functions during these conditions. Fluctuation in the relative quantities of proteins involved in energy metabolism during dormancy and reactivation was the most significant observation we made in this study. Proteins that are up-regulated or uniquely expressed during reactivation from dormancy offer to be attractive targets for therapeutic intervention to prevent reactivation of latent tuberculosis.

Genome-scale modeling for metabolic engineering

We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

Integrating proteomics profiling data sets: a network perspective

Understanding disease mechanisms often requires complex and accurate integration of cellular pathways and molecular networks. Systems biology offers the possibility to provide a comprehensive map of the cell's intricate wiring network, which can ultimately lead to decipher the disease phenotype. Here, we describe what biological pathways are, how they function in normal and abnormal cellular systems, limitations faced by databases for integrating data, and highlight how network models are emerging as a powerful integrative framework to understand and interpret the roles of proteins and peptides in diseases.

Proteomics of bronchial biopsies: galectin-3 as a predictive biomarker of airway remodelling modulation in omalizumab-treated severe asthma patients

Asthma is a chronic inflammatory disease. Reticular basement membrane (RBM) thickening is considered feature of airway remodelling (AR) particularly in severe asthma (SA). Omalizumab, mAb to IgE is effective in SA and can modulate AR. Herein we describe protein profiles of bronchial biopsies to detect biomarkers of anti-IgE effects on AR and to explain potential mechanisms/pathways. We defined the bronchial biopsy protein profiles, before and after treatment. Unsupervised clustering of baseline proteomes resulted in very good agreement with the morphometric analysis of AR. Protein profiles of omalizumab responders (ORs) were significantly different from those of non-omalizumab responders (NORs). The major differences between ORs and NORs lied to smooth muscle and extra cellular matrix proteins. Notably, an IgE-binding protein (galectin-3) was reliable, stable and predictive biomarker of AR modulation. Omalizumab down-regulated bronchial smooth muscle proteins in SA. These findings suggest that omalizumab may exert disease-modifying effects on remodelling components.

Broad phenotypic changes associated with gain of radiation resistance in head and neck squamous cell cancer

AIMS

The central issue of resistance to radiation remains a significant challenge in the treatment of cancer despite improvements in treatment modality and emergence of new therapies. To facilitate the identification of molecular factors that elicit protection against ionizing radiation, we developed a matched model of radiation resistance for head and neck squamous cell cancer (HNSCC) and characterized its properties using quantitative mass spectrometry and complementary assays.

RESULTS

Functional network analysis of proteomics data identified DNA replication and base excision repair, extracellular matrix-receptor interaction, cell cycle, focal adhesion, and regulation of actin cytoskeleton as significantly up- or downregulated networks in resistant (rSCC-61) HNSCC cells. Upregulated proteins in rSCC-61 included a number of cytokeratins, fatty acid synthase, and antioxidant proteins. In addition, the rSCC-61 cells displayed two unexpected features compared with parental radiation-sensitive SCC-61 cells: (i) rSCC-61 had increased sensitivity to Erlotinib, a small-molecule inhibitor of epidermal growth factor receptor; and (ii) there was evidence of mesenchymal-to-epithelial transition in rSCC-61, confirmed by the expression of protein markers and functional assays (e.g., Vimentin, migration).

INNOVATION

The matched model of radiation resistance presented here shows that multiple signaling and metabolic pathways converge to produce the rSCC-61 phenotype, and this points to the function of the antioxidant system as a major regulator of resistance to ionizing radiation in rSCC-61, a phenomenon further confirmed by analysis of HNSCC tumor samples.

CONCLUSION

The rSCC-61/SCC-61 model provides the opportunity for future investigations of the redox-regulated mechanisms of response to combined radiation and Erlotinib in a preclinical setting.

Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development

Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic-based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research.

Integration of gene expression data with network-based analysis to identify signaling and metabolic pathways regulated during the development of osteoarthritis

Osteoarthritis (OA) is characterized by remodeling and degradation of joint tissues. Microarray studies have led to a better understanding of the molecular changes that occur in tissues affected by conditions such as OA; however, such analyses are limited to the identification of a list of genes with altered transcript expression, usually at a single time point during disease progression. While these lists have identified many novel genes that are altered during the disease process, they are unable to identify perturbed relationships between genes and gene products. In this work, we have integrated a time course gene expression dataset with network analysis to gain a better systems level understanding of the early events that occur during the development of OA in a mouse model. The subnetworks that were enriched at one or more of the time points examined (2, 4, 8, and 16 weeks after induction of OA) contained genes from several pathways proposed to be important to the OA process, including the extracellular matrix-receptor interaction and the focal adhesion pathways and the Wnt, Hedgehog and TGF-β signaling pathways. The genes within the subnetworks were most active at the 2 and 4 week time points and included genes not previously studied in the OA process. A unique pathway, riboflavin metabolism, was active at the 4 week time point. These results suggest that the incorporation of network-type analyses along with time series microarray data will lead to advancements in our understanding of complex diseases such as OA at a systems level, and may provide novel insights into the pathways and processes involved in disease pathogenesis.

CyTargetLinker: a cytoscape app to integrate regulatory interactions in network analysis

INTRODUCTION

The high complexity and dynamic nature of the regulation of gene expression, protein synthesis, and protein activity pose a challenge to fully understand the cellular machinery. By deciphering the role of important players, including transcription factors, microRNAs, or small molecules, a better understanding of key regulatory processes can be obtained. Various databases contain information on the interactions of regulators with their targets for different organisms, data recently being extended with the results of the ENCODE (Encyclopedia of DNA Elements) project. A systems biology approach integrating our understanding on different regulators is essential in interpreting the regulation of molecular biological processes.

IMPLEMENTATION

We developed CyTargetLinker (http://projects.bigcat.unimaas.nl/cytargetlinker), a Cytoscape app, for integrating regulatory interactions in network analysis. Recently we released CyTargetLinker as one of the first apps for Cytoscape 3. It provides a user-friendly and flexible interface to extend biological networks with regulatory interactions, such as microRNA-target, transcription factor-target and/or drug-target. Importantly, CyTargetLinker employs identifier mapping to combine various interaction data resources that use different types of identifiers.

RESULTS

Three case studies demonstrate the strength and broad applicability of CyTargetLinker, (i) extending a mouse molecular interaction network, containing genes linked to diabetes mellitus, with validated and predicted microRNAs, (ii) enriching a molecular interaction network, containing DNA repair genes, with ENCODE transcription factor and (iii) building a regulatory meta-network in which a biological process is extended with information on transcription factor, microRNA and drug regulation.

CONCLUSIONS

CyTargetLinker provides a simple and extensible framework for biologists and bioinformaticians to integrate different regulatory interactions into their network analysis approaches. Visualization options enable biological interpretation of complex regulatory networks in a graphical way. Importantly the incorporation of our tool into the Cytoscape framework allows the application of CyTargetLinker in combination with a wide variety of other apps for state-of-the-art network analysis.

'HypothesisFinder:' a strategy for the detection of speculative statements in scientific text

Speculative statements communicating experimental findings are frequently found in scientific articles, and their purpose is to provide an impetus for further investigations into the given topic. Automated recognition of speculative statements in scientific text has gained interest in recent years as systematic analysis of such statements could transform speculative thoughts into testable hypotheses. We describe here a pattern matching approach for the detection of speculative statements in scientific text that uses a dictionary of speculative patterns to classify sentences as hypothetical. To demonstrate the practical utility of our approach, we applied it to the domain of Alzheimer's disease and showed that our automated approach captures a wide spectrum of scientific speculations on Alzheimer's disease. Subsequent exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches, and can thus provide added value to ongoing research activities.

RECONN: A CYTOSCAPE PLUG-IN FOR EXPLORING AND VISUALIZING REACTOME

Interface and visualization tools usually provide static representations of biological pathways, which can be a severe limitation: fixed pathway boundaries are used without consensus about the elements that should be included in a particular pathway; one cannot generate new pathways or produce selective views of existing pathways. Also, the tools are not capable of integrating multiple levels that conceptually can be distinguished in biological systems.

We present ReConn, an interface and visualization tool for a flexible analysis of large data at multiple biological levels. ReConn (Reactome Connector) is an open source extension to Cytoscape which allows user friendly interaction with the Reactome database. ReConn can use both predefined Reactome pathways as well as generate new pathways. A pathway can be derived by starting from any given metabolite and existing pathways can be extended by adding related reactions. The tool can also retrieve alternative routes between elements of a biological network. Such an option is potentially applicable in the design and analysis of knockout experiments. ReConn displays information about multiple levels of the system in one view. With these dynamic features ReConn addresses all of the above mentioned limitations of the interface tools.

Processing of Mass Spectrometry Data in Clinical Applications

Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.

Comparative microbial modules resource: generation and visualization of multi-species biclusters

The increasing abundance of large-scale, high-throughput datasets for many closely related organisms provides opportunities for comparative analysis via the simultaneous biclustering of datasets from multiple species. These analyses require a reformulation of how to organize multi-species datasets and visualize comparative genomics data analyses results. Recently, we developed a method, multi-species cMonkey, which integrates heterogeneous high-throughput datatypes from multiple species to identify conserved regulatory modules. Here we present an integrated data visualization system, built upon the Gaggle, enabling exploration of our method's results (available at http://meatwad.bio.nyu.edu/cmmr.html). The system can also be used to explore other comparative genomics datasets and outputs from other data analysis procedures – results from other multiple-species clustering programs or from independent clustering of different single-species datasets. We provide an example use of our system for two bacteria, Escherichia coli and Salmonella Typhimurium. We illustrate the use of our system by exploring conserved biclusters involved in nitrogen metabolism, uncovering a putative function for yjjI, a currently uncharacterized gene that we predict to be involved in nitrogen assimilation.

Advancing high-throughput experimental technologies are providing access to genome-wide measurements for multiple related species on multiple information levels (e.g. mRNA, protein, interactions, functional assays, etc.). We present a biclustering algorithm and an associated visualization system for generating and exploring regulatory modules derived from analysis of integrated multi-species genomics datasets. We use multi-species-cMonkey, an algorithm of our own construction that can integrate diverse systems-biology datatypes from multiple species to form biclusters, or condition-dependent regulatory modules, that are conserved across both the multiple species analyzed and biclusters that are specific to subsets of the processed species. Our resource is an integrated web and java based system that allows biologists to explore both conserved and species-specific biclusters in the context of the data, associated networks for both species, and existing annotations for both species. Our focus in this work is on the use of the integrated system with examples drawn from exploring modules associated with nitrogen metabolism in two Gram-negative bacteria, E. coli and S. Typhimurium.

Construction of gene regulatory networks using biclustering and Bayesian networks

Background

Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling.

Results

In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method.

Conclusions

Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.

RefNetBuilder: a platform for construction of integrated reference gene regulatory networks from expressed sequence tags

Background

Gene Regulatory Networks (GRNs) provide integrated views of gene interactions that control biological processes. Many public databases contain biological interactions extracted from experimentally validated literature reports, but most furnish only information for a few genetic model organisms. In order to provide a bioinformatic tool for researchers who work with non-model organisms, we developed RefNetBuilder, a new platform that allows construction of putative reference pathways or GRNs from expressed sequence tags (ESTs).

Results

RefNetBuilder was designed to have the flexibility to extract and archive pathway or GRN information from public databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG). It features sequence alignment tools such as BLAST to allow mapping ESTs to pathways and GRNs in model organisms. A scoring algorithm was incorporated to rank and select the best match for each query EST. We validated RefNetBuilder using DNA sequences of Caenorhabditis elegans, a model organism having manually curated KEGG pathways. Using the earthworm Eisenia fetida as an example, we demonstrated the functionalities and features of RefNetBuilder.

Conclusions

The RefNetBuilder provides a standalone application for building reference GRNs for non-model organisms on a number of operating system platforms with standard desktop computer hardware. As a new bioinformatic tool aimed for constructing putative GRNs for non-model organisms that have only ESTs available, RefNetBuilder is especially useful to explore pathway- or network-related information in these organisms.

The Proteome Folding Project: proteome-scale prediction of structure and function

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions.

Human protein reference database and human proteinpedia as discovery resources for molecular biotechnology

In the recent years, research in molecular biotechnology has transformed from being small scale studies targeted at a single or a small set of molecule(s) into a combination of high throughput discovery platforms and extensive validations. Such a discovery platform provided an unbiased approach which resulted in the identification of several novel genetic and protein biomarkers. High throughput nature of these investigations coupled with higher sensitivity and specificity of Next Generation technologies provided qualitatively and quantitatively richer biological data. These developments have also revolutionized biological research and speed of data generation. However, it is becoming difficult for individual investigators to directly benefit from this data because they are not easily accessible. Data resources became necessary to assimilate, store and disseminate information that could allow future discoveries. We have developed two resources--Human Protein Reference Database (HPRD) and Human Proteinpedia, which integrate knowledge relevant to human proteins. A number of protein features including protein-protein interactions, post-translational modifications, subcellular localization, and tissue expression, which have been studied using different strategies were incorporated in these databases. Human Proteinpedia also provides a portal for community participation to annotate and share proteomic data and uses HPRD as the scaffold for data processing. Proteomic investigators can even share unpublished data in Human Proteinpedia, which provides a meaningful platform for data sharing. As proteomic information reflects a direct view of cellular systems, proteomics is expected to complement other areas of biology such as genomics, transcriptomics, molecular biology, cloning, and classical genetics in understanding the relationships among multiple facets of biological systems.

New take on comparative immunology: relevance to immunotherapy

It is becoming increasingly recognized that experimental animal models, while useful to address monothematic biological questions, bear unpredictable relevance to human disease. Several reasons have been proposed. However, the uncontrollable nature of human genetics and the heterogeneity of disease that can only be replicated with difficulty experimentally play a leading role. Comparative immunology is a term that generally refers to the analysis of shared or diverging facets of immunology among species; these comparisons are carried out according to the principle that evolutionarily conserved themes outline biologic functions universally relevant for survival. We propose that a similar strategy could be applied to searching for themes shared by distinct immune pathologies within our own species. Identification of common patterns may outline pathways necessary for a particular determinism to occur, such as tissue-specific rejection or tolerance. This approach is founded on the unproven but sensible presumption that nature does not require an infinite plethora of redundant mechanisms to reach its purposes. Thus, immune pathologies must follow, at least in part, common means that determine their onset and maintenance. Commonalities among diseases can, in turn, be segregated from disease-specific patterns uncovering essential mechanisms that may represent universal targets for immunotherapy.

Systems biology: leading the revolution in ecotoxicology

The rapid development of new technologies such as transcriptomics, proteomics, and metabolomics (Omics) are changing the way ecotoxicology is practiced. The data deluge has begun with genomes of over 65 different aquatic species that are currently being sequenced, and many times that number with at least some level of transcriptome sequencing. Integrating these top-down methodologies is an essential task in the field of systems biology. Systems biology is a biology-based interdisciplinary field that focuses on complex interactions in biological systems, with the intent to model and discover emergent properties of the system. Recent studies demonstrate that Omics technologies provide valuable insight into ecotoxicity, both in laboratory exposures with model organisms and with animals exposed in the field. However, these approaches require a context of the whole animal and population to be relevant. Powerful approaches using reverse engineering to determine interacting networks of genes, proteins, or biochemical reactions are uncovering unique responses to toxicants. Modeling efforts in aquatic animals are evolving to interrelate the interacting networks of a system and the flow of information linking these elements. Just as is happening in medicine, systems biology approaches that allow the integration of many different scales of interaction and information are already driving a revolution in understanding the impacts of pollutants on aquatic systems.

Integrating Omics data for signaling pathways, interactome reconstruction, and functional analysis

Omics data and computational approaches are today providing a key to disentangle the complex architecture of living systems. The integration and analysis of data of different nature allows to extract meaningful representations of signaling pathways and protein interactions networks, helpful in achieving an increased understanding of such intricate biochemical processes. We here describe a general workflow and relative hurdles in integrating online Omics data and analyzing reconstructed representations by using the available computational platforms.

Reverse engineering adverse outcome pathways

The toxicological effects of many stressors are mediated through unknown, or incompletely characterized, mechanisms of action. The application of reverse engineering complex interaction networks from high dimensional omics data (gene, protein, metabolic, signaling) can be used to overcome these limitations. This approach was used to characterize adverse outcome pathways (AOPs) for chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis in fathead minnows (FHM, Pimephales promelas). Gene expression changes in FHM ovaries in response to seven different chemicals, over different times, doses, and in vivo versus in vitro conditions, were captured in a large data set of 868 arrays. Potential AOPs of the antiandrogen flutamide were examined using two mutual information-based methods to infer gene regulatory networks and potential AOPs. Representative networks from these studies were used to predict network paths from stressor to adverse outcome as candidate AOPs. The relationship of individual chemicals to an adverse outcome can be determined by following perturbations through the network in response to chemical treatment, thus leading to the nodes associated with the adverse outcome. Identification of candidate pathways allows for formation of testable hypotheses about key biological processes, biomarkers, or alternative endpoints that can be used to monitor an AOP. Finally, the unique challenges facing the application of this approach in ecotoxicology were identified and a road map for the utilization of these tools presented.

Multi-species integrative biclustering

We describe an algorithm, multi-species cMonkey, for the simultaneous biclustering of heterogeneous multiple-species data collections and apply the algorithm to a group of bacteria containing Bacillus subtilis, Bacillus anthracis, and Listeria monocytogenes. The algorithm reveals evolutionary insights into the surprisingly high degree of conservation of regulatory modules across these three species and allows data and insights from well-studied organisms to complement the analysis of related but less well studied organisms.

Synthetic lethal screen of an EGFR-centered network to improve targeted therapies

Intrinsic and acquired cellular resistance factors limit the efficacy of most targeted cancer therapeutics. Synthetic lethal screens in lower eukaryotes suggest that networks of genes closely linked to therapeutic targets would be enriched for determinants of drug resistance. We developed a protein network centered on the epidermal growth factor receptor (EGFR), which is a validated cancer therapeutic target, and used small interfering RNA screening to comparatively probe this network for proteins that regulate the effectiveness of both EGFR-targeted agents and nonspecific cytotoxic agents. We identified subnetworks of proteins influencing resistance, with putative resistance determinants enriched among proteins that interacted with proteins at the core of the network. We found that clinically relevant drugs targeting proteins connected in the EGFR network, such as protein kinase C or Aurora kinase A, or the transcriptional regulator signal transducer and activator of transcription 3 (STAT3), synergized with EGFR antagonists to reduce cell viability and tumor size, suggesting the potential for a direct path to clinical exploitation. Such a focused approach can potentially improve the coherent design of combination cancer therapies.

Flexible network reconstruction from relational databases with Cytoscape and CytoSQL

Background

Molecular interaction networks can be efficiently studied using network visualization software such as Cytoscape. The relevant nodes, edges and their attributes can be imported in Cytoscape in various file formats, or directly from external databases through specialized third party plugins. However, molecular data are often stored in relational databases with their own specific structure, for which dedicated plugins do not exist. Therefore, a more generic solution is presented.

Results

A new Cytoscape plugin 'CytoSQL' is developed to connect Cytoscape to any relational database. It allows to launch SQL ('Structured Query Language') queries from within Cytoscape, with the option to inject node or edge features of an existing network as SQL arguments, and to convert the retrieved data to Cytoscape network components. Supported by a set of case studies we demonstrate the flexibility and the power of the CytoSQL plugin in converting specific data subsets into meaningful network representations.

Conclusions

CytoSQL offers a unified approach to let Cytoscape interact with relational databases. Thanks to the power of the SQL syntax, this tool can rapidly generate and enrich networks according to very complex criteria. The plugin is available at http://www.ptools.ua.ac.be/CytoSQL.

Construction of a polycystic ovarian syndrome (PCOS) pathway based on the interactions of PCOS-related proteins retrieved from bibliomic data

Polycystic ovary syndrome (PCOS) is a complex but frequently occurring endocrine abnormality. PCOS has become one of the leading causes of oligo-ovulatory infertility among premenopausal women. The definition of PCOS remains unclear because of the heterogeneity of this abnormality, but it is associated with insulin resistance, hyperandrogenism, obesity and dyslipidaemia. The main purpose of this study was to identify possible candidate genes involved in PCOS. Several genomic approaches, including linkage analysis and microarray analysis, have been used to look for candidate PCOS genes. To obtain a clearer view of the mechanism of PCOS, we have compiled data from microarray analyses. An extensive literature search identified seven published microarray analyses that utilized PCOS samples. These were published between the year of 2003 and 2007 and included analyses of ovary tissues as well as whole ovaries and theca cells. Although somewhat different methods were used, all the studies employed cDNA microarrays to compare the gene expression patterns of PCOS patients with those of healthy controls. These analyses identified more than a thousand genes whose expression was altered in PCOS patients. Most of the genes were found to be involved in gene and protein expression, cell signaling and metabolism. We have classified all of the 1081 identified genes as coding for either known or unknown proteins. Cytoscape 2.6.1 was used to build a network of protein and then to analyze it. This protein network consists of 504 protein nodes and 1408 interactions among those proteins. One hypothetical protein in the PCOS network was postulated to be involved in the cell cycle. BiNGO was used to identify the three main ontologies in the protein network: molecular functions, biological processes and cellular components. This gene ontology analysis identified a number of ontologies and genes likely to be involved in the complex mechanism of PCOS. These include the insulin receptor signaling pathway, steroid biosynthesis, and the regulation of gonadotropin secretion among others.

Genoscape: a Cytoscape plug-in to automate the retrieval and integration of gene expression data and molecular networks

Summary: Genoscape is an open-source Cytoscape plug-in that visually integrates gene expression data sets from GenoScript, a transcriptomic database, and KEGG pathways into Cytoscape networks. The generated visualisation highlights gene expression changes and their statistical significance. The plug-in also allows one to browse GenoScript or import transcriptomic data from other sources through tab-separated text files. Genoscape has been successfully used by researchers to investigate the results of gene expression profiling experiments.

Availability: Genoscape is an open-source software freely available from the Genoscape webpage (http://www.pasteur.fr/recherche/unites/Gim/genoscape/). Installation instructions and tutorial can also be found at this URL.

Contact:Mathieu.clement-ziza@biotec.tu-dresden.de; sandrine.rousseau@pasteur.fr

Supplementary information:Supplementary data are available at Bioinformatics online.

Layered functional network analysis of gene expression in human heart failure

Background

Although dilated cardiomyopathy (DCM) is a leading cause of heart failure (HF), the mechanism underlying DCM is not well understood. Previously, it has been demonstrated that an integrative analysis of gene expression and protein-protein interaction (PPI) networks can provide insights into the molecular mechanisms of various diseases. In this study we develop a systems approach by linking public available gene expression data on ischemic dilated cardiomyopathy (ICM), a main pathological form of DCM, with data from a layered PPI network. We propose that the use of a layered PPI network, as opposed to a traditional PPI network, provides unique insights into the mechanism of DCM.

Methods

Four Cytoscape plugins including BionetBuilder, NetworkAnalyzer, Cerebral and GenePro were used to establish the layered PPI network, which was based upon validated subcellular protein localization data retrieved from the HRPD and Entrez Gene databases. The DAVID function annotation clustering tool was used for gene ontology (GO) analysis.

Results

The assembled layered PPI network was divided into four layers: extracellular, plasma membrane, cytoplasm and nucleus. The characteristics of the gene expression pattern of the four layers were compared. In the extracellular and plasma membrane layers, there were more proteins encoded by down-regulated genes than by up-regulated genes, but in the other two layers, the opposite trend was found. GO analysis established that proteins encoded by up-regulated genes, reflecting significantly over-represented biological processes, were mainly located in the nucleus and cytoplasm layers, while proteins encoded by down-regulated genes were mainly located in the extracellular and plasma membrane layers. The PPI network analysis revealed that the Janus family tyrosine kinase-signal transducer and activator of transcription (Jak-STAT) signaling pathway might play an important role in the development of ICM and could be exploited as a therapeutic target of ICM. In addition, glycogen synthase kinase 3 beta (GSK3B) may also be a potential candidate target, but more evidence is required.

Conclusion

This study illustrated that by incorporating subcellular localization information into a PPI network based analysis, one can derive greater insights into the mechanisms underlying ICM.

BioNetBuilder2.0: bringing systems biology to chicken and other model organisms

Background

Systems Biology research tools, such as Cytoscape, have greatly extended the reach of genomic research. By providing platforms to integrate data with molecular interaction networks, researchers can more rapidly begin interpretation of large data sets collected for a system of interest. BioNetBuilder is an open-source client-server Cytoscape plugin that automatically integrates molecular interactions from all major public interaction databases and serves them directly to the user's Cytoscape environment. Until recently however, chicken and other eukaryotic model systems had little interaction data available.

Results

Version 2.0 of BioNetBuilder includes a redesigned synonyms resolution engine that enables transfer and integration of interactions across species; this engine translates between alternate gene names as well as between orthologs in multiple species. Additionally, BioNetBuilder is now implemented to be part of the Gaggle, thereby allowing seamless communication of interaction data to any software implementing the widely used Gaggle software. Using BioNetBuilder, we constructed a chicken interactome possessing 72,000 interactions among 8,140 genes directly in the Cytoscape environment. In this paper, we present a tutorial on how to do so and analysis of a specific use case.

Conclusion

BioNetBuilder 2.0 provides numerous user-friendly systems biology tools that were otherwise inaccessible to researchers in chicken genomics, as well as other model systems. We provide a detailed tutorial spanning all required steps in the analysis. BioNetBuilder 2.0, the tools for maintaining its data bases, standard operating procedures for creating local copies of its back-end data bases, as well as all of the Gaggle and Cytoscape codes required, are open-source and freely available at http://err.bio.nyu.edu/cytoscape/bionetbuilder/.

DASMI: exchanging, annotating and assessing molecular interaction data

MOTIVATION

Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet.

RESULTS

We introduce the DASMI system for the dynamic exchange, annotation and assessment of molecular interaction data. DASMI is based on the widely used Distributed Annotation System (DAS) and consists of a data exchange specification, web servers for providing the interaction data and clients for data integration and visualization. The decentralized architecture of DASMI affords the online retrieval of the most recent data from distributed sources and databases. DASMI can also be extended easily by adding new data sources and clients. We describe all DASMI components and demonstrate their use for protein and domain interactions.

AVAILABILITY

The DASMI tools are available at http://www.dasmi.de/ and http://ipfam.sanger.ac.uk/graph. The DAS registry and the DAS 1.53E specification is found at http://www.dasregistry.org/.

myGRN: a database and visualisation system for the storage and analysis of developmental genetic regulatory networks

Background

Biological processes are regulated by complex interactions between transcription factors and signalling molecules, collectively described as Genetic Regulatory Networks (GRNs). The characterisation of these networks to reveal regulatory mechanisms is a long-term goal of many laboratories. However compiling, visualising and interacting with such networks is non-trivial. Current tools and databases typically focus on GRNs within simple, single celled organisms. However, data is available within the literature describing regulatory interactions in multi-cellular organisms, although not in any systematic form. This is particularly true within the field of developmental biology, where regulatory interactions should also be tagged with information about the time and anatomical location of development in which they occur.

Description

We have developed myGRN (), a web application for storing and interrogating interaction data, with an emphasis on developmental processes. Users can submit interaction and gene expression data, either curated from published sources or derived from their own unpublished data. All interactions associated with publications are publicly visible, and unpublished interactions can only be shared between collaborating labs prior to publication. Users can group interactions into discrete networks based on specific biological processes. Various filters allow dynamic production of network diagrams based on a range of information including tissue location, developmental stage or basic topology. Individual networks can be viewed using myGRV, a tool focused on displaying developmental networks, or exported in a range of formats compatible with third party tools. Networks can also be analysed for the presence of common network motifs. We demonstrate the capabilities of myGRN using a network of zebrafish interactions integrated with expression data from the zebrafish database, ZFIN.

Conclusion

Here we are launching myGRN as a community-based repository for interaction networks, with a specific focus on developmental networks. We plan to extend its functionality, as well as use it to study networks involved in embryonic development in the future.

Tomato transcriptional responses to a foliar and a vascular fungal pathogen are distinct

Plant activation of host defense against pathogenic microbes requires significant host transcriptional reprogramming. In this study, we compared transcriptional changes in tomato during compatible and incompatible interactions with the foliar fungal pathogen Cladosporium fulvum and the vascular fungal pathogen Verticillium dahliae. Although both pathogens colonize different host tissues, they display distinct commonalities in their infection strategy; both pathogens penetrate natural openings and grow strictly extracellular. Furthermore, resistance against both pathogens is conveyed by the same class of resistance proteins, the receptor-like proteins. For each individual pathogen, the expression profile of the compatible and incompatible interaction largely overlaps. However, when comparing between the two pathogens, the C. fulvum-induced transcriptional changes show little overlap with those induced by V. dahliae. Moreover, within the subset of genes that are regulated by both pathogens, many genes show inverse regulation. With pathway reconstruction, networks of tomato genes implicated in photorespiration, hypoxia, and glycoxylate metabolism were identified that are repressed upon infection with C. fulvum and induced by V. dahliae. Similarly, auxin signaling is differentially affected by the two pathogens. Thus, differentially regulated pathways were identified with novel strategies that allowed the use of state-of-the-art tools, even though tomato is not a genetic model organism.

Network integration and graph analysis in mammalian molecular systems biology

Abstraction of intracellular biomolecular interactions into networks is useful for data integration and graph analysis. Network analysis tools facilitate predictions of novel functions for proteins, prediction of functional interactions and identification of intracellular modules. These efforts are linked with drug and phenotype data to accelerate drug-target and biomarker discovery. This review highlights the currently available varieties of mammalian biomolecular networks, and surveys methods and tools to construct, compare, integrate, visualise and analyse such networks.

Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology

Although high-throughput technologies used in biology have resulted in the accumulation of vast amounts of data in the literature, it is becoming difficult for individual investigators to directly benefit from this data because they are not easily accessible. Databases have assumed a crucial role in assimilating and storing information that could enable future discoveries. To this end, our group has developed two resources - Human Protein Reference Database (HPRD) and Human Proteinpedia - that provide integrated information pertaining to human proteins. These databases contain information on a number of features of proteins that have been discovered using various experimental methods. Human Proteinpedia was developed as a portal for community participation to annotate and share proteomic data using HPRD as the scaffold. It allows proteomic investigators to even share unpublished data and provides an effective medium for data sharing. As proteomic information reflects a direct view of cellular systems, proteomics is expected to complement other areas of biology such as genomics, transcriptomics, classical genetics, and chemical genetics in understanding the relationships among genome, gene functions, and living systems.

Human Protein Reference Database--2009 update

Human Protein Reference Database (HPRD--http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system--Human Proteinpedia (http://www.humanproteinpedia.org/)--through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15,000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome.

Genetic and systems level analysis of Drosophila sticky/citron kinase and dFmr1 mutants reveals common regulation of genetic networks

Background

In Drosophila, the genes sticky and dFmr1 have both been shown to regulate cytoskeletal dynamics and chromatin structure. These genes also genetically interact with Argonaute family microRNA regulators. Furthermore, in mammalian systems, both genes have been implicated in neuronal development. Given these genetic and functional similarities, we tested Drosophila sticky and dFmr1 for a genetic interaction and measured whole genome expression in both mutants to assess similarities in gene regulation.

Results

We found that sticky mutations can dominantly suppress a dFmr1 gain-of-function phenotype in the developing eye, while phenotypes produced by RNAi knock-down of sticky were enhanced by dFmr1 RNAi and a dFmr1 loss-of-function mutation. We also identified a large number of transcripts that were misexpressed in both mutants suggesting that sticky and dFmr1 gene products similarly regulate gene expression. By integrating gene expression data with a protein-protein interaction network, we found that mutations in sticky and dFmr1 resulted in misexpression of common gene networks, and consequently predicted additional specific phenotypes previously not known to be associated with either gene. Further phenotypic analyses validated these predictions.

Conclusion

These findings establish a functional link between two previously unrelated genes. Microarray analysis indicates that sticky and dFmr1 are both required for regulation of many developmental genes in a variety of cell types. The diversity of transcripts regulated by these two genes suggests a clear cause of the pleiotropy that sticky and dFmr1 mutants display and provides many novel, testable hypotheses about the functions of these genes. As both of these genes are implicated in the development and function of the mammalian brain, these results have relevance to human health as well as to understanding more general biological processes.

Integrating and annotating the interactome using the MiMI plugin for cytoscape

The MiMI molecular interaction repository integrates data from multiple sources, resolves interactions to standard gene names and symbols, links to annotation data from GO, MeSH and PubMed and normalizes the descriptions of interaction type. Here, we describe a Cytoscape plugin that retrieves interaction and annotation data from MiMI and links out to multiple data sources and tools. Community annotation of the interactome is supported.

AVAILABILITY

MiMI plugin v3.0.1 can be installed from within Cytoscape 2.6 using the Cytoscape plugin manager in 'Network and Attribute I/0' category. The plugin is also preloaded when Cytoscape is launched using Java WebStart at http://mimi.ncibi.org by querying a gene and clicking 'View in MiMI Plugin for Cytoscape' link.

Functional modules integrating essential cellular functions are predictive of the response of leukaemia cells to DNA damage

MOTIVATION

Childhood B-precursor lymphoblastic leukaemia (ALL) is the most common paediatric malignancy. Despite the fact that 80% of ALL patients respond to anti-cancer drugs, the patho-physiology of this disease is still not fully understood. mRNA expression-profiling studies that have been performed have not yet provided novel insights into the mechanisms behind cellular response to DNA damage. More powerful data analysis techniques may be required for identifying novel functional pathways involved in the cellular responses to DNA damage.

RESULTS

In order to explore the possibility that unforeseen biological processes may be involved in the response to DNA damage, we have developed and applied a novel procedure for the identification of functional modules in ALL cells. We have discovered that the overall activity of functional modules integrating protein degradation and mRNA processing is predictive of response to DNA damage.

AVAILABILITY

Supplementary material including R code, additional results, experimental datasets, as well as a detailed description of the methodology are available at http://www.bip.bham.ac.uk/vivo/fumo.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bottom up: a modular view of immunology

In this issue of Immunity, Chaussabel et al. (2008) apply an inductive approach to pathway discovery identifying modular units that govern human immune biology.

Integration of biological networks and gene expression data using Cytoscape

Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.

Challenges in plant cellular pathway reconstruction based on gene expression profiling

Microarrays are used to profile transcriptional activity, providing global cell biology insight. Particularly for plants, interpretation of transcriptional profiles is challenging because many genes have unknown functions. Furthermore, many plant gene sequences do not have clear homologs in other model organisms. Fortunately, over the past five years, various tools that assist plant scientists have been developed. Here, we evaluate the currently available in silico tools for reconstruction of cellular (metabolic, biochemical and signal transduction) pathways based on plant gene expression datasets. Furthermore, we show how expression-profile comparison at the level of these various cellular pathways contributes to the postulation of novel hypotheses which, after experimental verification, can provide further insight into decisive elements that have roles in cellular processes.