Reactome: a database of reactions, pathways and biological processes

Online tools for bioinformatics analyses in nutrition sciences

Recent advances in "omics" research have resulted in the creation of large datasets that were generated by consortiums and centers, small datasets that were generated by individual investigators, and bioinformatics tools for mining these datasets. It is important for nutrition laboratories to take full advantage of the analysis tools to interrogate datasets for information relevant to genomics, epigenomics, transcriptomics, proteomics, and metabolomics. This review provides guidance regarding bioinformatics resources that are currently available in the public domain, with the intent to provide a starting point for investigators who want to take advantage of the opportunities provided by the bioinformatics field.

A Systems Biology Approach to Deciphering the Etiology of Steatosis Employing Patient-Derived Dermal Fibroblasts and iPS Cells

Non-alcoholic fatty liver disease comprises a broad spectrum of disease states ranging from simple steatosis to non-alcoholic steatohepatitis. As a result of increases in the prevalences of obesity, insulin resistance, and hyperlipidemia, the number of people with hepatic steatosis continues to increase. Differences in susceptibility to steatohepatitis and its progression to cirrhosis have been attributed to a complex interplay of genetic and external factors all addressing the intracellular network. Increase in sugar or refined carbohydrate consumption results in an increase of insulin and insulin resistance that can lead to the accumulation of fat in the liver. Here we demonstrate how a multidisciplinary approach encompassing cellular reprogramming, transcriptomics, proteomics, metabolomics, modeling, network reconstruction, and data management can be employed to unveil the mechanisms underlying the progression of steatosis. Proteomics revealed reduced AKT/mTOR signaling in fibroblasts derived from steatosis patients and further establishes that the insulin-resistant phenotype is present not only in insulin-metabolizing central organs, e.g., the liver, but is also manifested in skin fibroblasts. Transcriptome data enabled the generation of a regulatory network based on the transcription factor SREBF1, linked to a metabolic network of glycerolipid, and fatty acid biosynthesis including the downstream transcriptional targets of SREBF1 which include LIPIN1 (LPIN) and low density lipoprotein receptor. Glutathione metabolism was among the pathways enriched in steatosis patients in comparison to healthy controls. By using a model of the glutathione pathway we predict a significant increase in the flux through glutathione synthesis as both gamma-glutamylcysteine synthetase and glutathione synthetase have an increased flux. We anticipate that a larger cohort of patients and matched controls will confirm our preliminary findings presented here.

Endosomal crosstalk: meeting points for signaling pathways

Endocytosis participates in downregulating incoming signals, but 'signaling endosomes' may also serve as physical platforms for crosstalk between signaling pathways. Here, we briefly review the role of endosomes in signaling crosstalk and suggest that endosome-associated scaffold proteins mediate this crosstalk. In addition, using a proteome-wide in silico approach - in which we analyze endosome-binding properties and the capacity of candidates to recruit signaling proteins from more than one distinct pathway - we extend the list of putative crosstalk-mediating endosomal scaffolds. Because endosomal crosstalk may be an important systems-level regulator of pathway communication, scaffold proteins that mediate this crosstalk could be potential targets for pharmacological intervention and synthetic engineering.

Dual targeting of the Akt/mTOR signaling pathway inhibits castration-resistant prostate cancer in a genetically engineered mouse model

Although the prognosis for clinically localized prostate cancer is now favorable, there are still no curative treatments for castration-resistant prostate cancer (CRPC) and, therefore, it remains fatal. In this study, we investigate a new therapeutic approach for treatment of CRPC, which involves dual targeting of a major signaling pathway that is frequently deregulated in the disease. We found that dual targeting of the Akt and mTOR signaling pathways with their respective inhibitors, MK-2206 and ridaforolimus (MK-8669), is highly effective for inhibiting CRPC in preclinical studies in vivo using a refined genetically engineered mouse model of the disease. The efficacy of the combination treatment contrasts with their limited efficacy as single agents, since delivery of MK-2206 or MK-8669 individually had a modest impact in vivo on the overall tumor phenotype. In human prostate cancer cell lines, although not in the mouse model, the synergistic actions of MK-2206 and ridaforolimus (MK-8669) are due in part to limiting the mTORC2 feedback activation of Akt. Moreover, the effects of these drugs are mediated by inhibition of cellular proliferation via the retinoblastoma (Rb) pathway. Our findings suggest that dual targeting of the Akt and mTOR signaling pathways using MK-2206 and ridaforolimus (MK-8669) may be effective for treatment of CRPC, particularly for patients with deregulated Rb pathway activity.

Proteomic analysis of lysine acetylation sites in rat tissues reveals organ specificity and subcellular patterns

Lysine acetylation is a major posttranslational modification involved in a broad array of physiological functions. Here, we provide an organ-wide map of lysine acetylation sites from 16 rat tissues analyzed by high-resolution tandem mass spectrometry. We quantify 15,474 modification sites on 4,541 proteins and provide the data set as a web-based database. We demonstrate that lysine acetylation displays site-specific sequence motifs that diverge between cellular compartments, with a significant fraction of nuclear sites conforming to the consensus motifs G-AcK and AcK-P. Our data set reveals that the subcellular acetylation distribution is tissue-type dependent and that acetylation targets tissue-specific pathways involved in fundamental physiological processes. We compare lysine acetylation patterns for rat as well as human skeletal muscle biopsies and demonstrate its general involvement in muscle contraction. Furthermore, we illustrate that acetylation of fructose-bisphosphate aldolase and glycerol-3-phosphate dehydrogenase serves as a cellular mechanism to switch off enzymatic activity.

The Notch signalling system: recent insights into the complexity of a conserved pathway

Notch signalling links the fate of one cell to that of an immediate neighbour and consequently controls differentiation, proliferation and apoptotic events in multiple metazoan tissues. Perturbations in this pathway activity have been linked to several human genetic disorders and cancers. Recent genome-scale studies in Drosophila melanogaster have revealed an extraordinarily complex network of genes that can affect Notch activity. This highly interconnected network contrasts our traditional view of the Notch pathway as a simple linear sequence of events. Although we now have an unprecedented insight into the way in which such a fundamental signalling mechanism is controlled by the genome, we are faced with serious challenges in analysing the underlying molecular mechanisms of Notch signal control.

Are we ready for genome-scale modeling in plants?

As it is becoming easier and faster to generate various types of high-throughput data, one would expect that by now we should have a comprehensive systems-level understanding of biology, biochemistry, and physiology at least in major prokaryotic and eukaryotic model systems. Despite the wealth of available data, we only get a glimpse of what is going on at the molecular level from the global perspective. The major reason is the high level of cellular complexity and our limited ability to identify all (or at least important) components and their interactions in virtually infinite number of internal and external conditions. Metabolism can be modeled mathematically by the use of genome-scale models (GEMs). GEMs are in silico metabolic flux models derived from available genome annotation. These models predict the combination of flux values of a defined metabolic network given the influence of internal and external signals. GEMs have been successfully implemented to model bacterial metabolism for over a decade. However, it was not until 2009 when the first GEM for Arabidopsis thaliana cell-suspension cultures was generated. Genome-scale modeling ("GEMing") in plants brings new challenges primarily due to the missing components and complexity of plant cells represented by the existence of: (i) photosynthesis; (ii) compartmentation; (iii) variety of cell and tissue types; and (iv) diverse metabolic responses to environmental and developmental cues as well as pathogens, insects, and competing weeds. This review presents a critical discussion of the advantages of existing plant GEMs, while identifies key targets for future improvements. Plant GEMs tend to be accurate in predicting qualitative changes in selected aspects of central carbon metabolism, while secondary metabolism is largely neglected mainly due to the missing (unknown) genes and metabolites. As such, these models are suitable for exploring metabolism in plants grown in favorable conditions, but not in field-grown plants that have to cope with environmental changes in complex ecosystems. AraGEM is the first GEM describing a photosynthetic and photorespiring plant cell (Arabidopsis thaliana). We demonstrate the use of AraGEM given the current (limited) knowledge of plant metabolism and reveal the unexpected robustness of AraGEM by a series of in silico simulations. The major focus of these simulations is on the assessment of the: (i) network connectivity; (ii) influence of CO₂ and photon uptake rates on cellular growth rates and production of individual biomass components; and (iii) stability of plant central carbon metabolism with internal pH changes.

Comparison of methods for competitive tests of pathway analysis

It has been suggested that pathway analysis can complement single-SNP analysis in exploring genomewide association data. Pathway analysis incorporates the available biological knowledge of genes and SNPs and is expected to improve the chances of revealing the underlying genetic architecture of complex traits. Methods for pathway analysis can be classified as competitive (enrichment) or self-contained (association) according to the hypothesis tested. Although association tests are statistically more powerful than enrichment tests they can be difficult to calibrate because biases in analysis accumulate across multiple SNPs or genes. Furthermore, enrichment tests can be more scientifically relevant than association tests, as they detect pathways with relatively more evidence for association than the remaining genes. Here we show how some well known association tests can be simply adapted to test for enrichment, and compare their performance to some established enrichment tests. We propose versions of the Adaptive Rank Truncated Product (ARTP), Tail Strength Measure and Fisher's combination of p-values for testing the enrichment null hypothesis. We compare the behaviour of these proposed methods with the established Hypergeometric Test and Gene-Set Enrichment Analysis (GSEA). The results of the simulation study show that the modified version of the ARTP method has generally the best performance across the situations considered. The methods were also applied for finding enriched pathways for body mass index (BMI) and platelet function phenotypes. The pathway analysis of BMI identified the Vasoactive Intestinal Peptide pathway as significantly associated with BMI. This pathway has been previously reported as associated with BMI and the risk of obesity. The ARTP method was the method that identified the largest number of enriched pathways across all tested pathway databases and phenotypes. The simulation and data application results are in agreement with previous work on association tests and suggests that the ARTP should be preferred for both enrichment and association testing.

Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics

Progress in genomics has raised expectations in many fields, and particularly in personalized cancer research. The new technologies available make it possible to combine information about potential disease markers, altered function and accessible drug targets, which, coupled with pathological and medical information, will help produce more appropriate clinical decisions. The accessibility of such experimental techniques makes it all the more necessary to improve and adapt computational strategies to the new challenges. This review focuses on the critical issues associated with the standard pipeline, which includes: DNA sequencing analysis; analysis of mutations in coding regions; the study of genome rearrangements; extrapolating information on mutations to the functional and signaling level; and predicting the effects of therapies using mouse tumor models. We describe the possibilities, limitations and future challenges of current bioinformatics strategies for each of these issues. Furthermore, we emphasize the need for the collaboration between the bioinformaticians who implement the software and use the data resources, the computational biologists who develop the analytical methods, and the clinicians, the systems' end users and those ultimately responsible for taking medical decisions. Finally, the different steps in cancer genome analysis are illustrated through examples of applications in cancer genome analysis.

Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes

BACKGROUND

To understand the roles they play in complex diseases, genes need to be investigated in the networks they are involved in. Integration of gene expression and network data is a promising approach to prioritize disease-associated genes. Some methods have been developed in this field, but the problem is still far from being solved.

RESULTS

In this paper, we developed a method, Networked Gene Prioritizer (NGP), to prioritize cancer-associated genes. Applications on several breast cancer and lung cancer datasets demonstrated that NGP performs better than the existing methods. It provides stable top ranking genes between independent datasets. The top-ranked genes by NGP are enriched in the cancer-associated pathways. The top-ranked genes by NGP-PLK1, MCM2, MCM3, MCM7, MCM10 and SKP2 might coordinate to promote cell cycle related processes in cancer but not normal cells.

CONCLUSIONS

In this paper, we have developed a method named NGP, to prioritize cancer-associated genes. Our results demonstrated that NGP performs better than the existing methods.

Parasites, proteomes and systems: has Descartes' clock run out of time?

Systems biology aims to integrate multiple biological data types such as genomics, transcriptomics and proteomics across different levels of structure and scale; it represents an emerging paradigm in the scientific process which challenges the reductionism that has dominated biomedical research for hundreds of years. Systems biology will nevertheless only be successful if the technologies on which it is based are able to deliver the required type and quality of data. In this review we discuss how well positioned is proteomics to deliver the data necessary to support meaningful systems modelling in parasite biology. We summarise the current state of identification proteomics in parasites, but argue that a new generation of quantitative proteomics data is now needed to underpin effective systems modelling. We discuss the challenges faced to acquire more complete knowledge of protein post-translational modifications, protein turnover and protein-protein interactions in parasites. Finally we highlight the central role of proteome-informatics in ensuring that proteomics data is readily accessible to the user-community and can be translated and integrated with other relevant data types.

B-Raf activation cooperates with PTEN loss to drive c-Myc expression in advanced prostate cancer

Both the PI3K → Akt → mTOR and mitogen-activated protein kinase (MAPK) signaling pathways are often deregulated in prostate tumors with poor prognosis. Here we describe a new genetically engineered mouse model of prostate cancer in which PI3K-Akt-mTOR signaling is activated by inducible disruption of PTEN, and extracellular signal-regulated kinase 1/2 (ERK1/2) MAPK signaling is activated by inducible expression of a BRAF(V600E) oncogene. These tissue-specific compound mutant mice develop lethal prostate tumors that are inherently resistant to castration. These tumors bypass cellular senescence and disseminate to lymph nodes, bone marrow, and lungs where they form overt metastases in approximately 30% of the cases. Activation of PI3K → Akt → mTOR and MAPK signaling pathways in these prostate tumors cooperate to upregulate c-Myc. Accordingly, therapeutic treatments with rapamycin and PD0325901 to target these pathways, respectively, attenuate c-Myc levels and reduce tumor and metastatic burden. Together, our findings suggest a generalized therapeutic approach to target c-Myc activation in prostate cancer by combinatorial targeting of the PI3K → Akt → mTOR and ERK1/2 MAPK signaling pathways.

CIDeR: multifactorial interaction networks in human diseases

The pathobiology of common diseases is influenced by heterogeneous factors interacting in complex networks. CIDeR http://mips.helmholtz-muenchen.de/cider/ is a publicly available, manually curated, integrative database of metabolic and neurological disorders. The resource provides structured information on 18,813 experimentally validated interactions between molecules, bioprocesses and environmental factors extracted from the scientific literature. Systematic annotation and interactive graphical representation of disease networks make CIDeR a versatile knowledge base for biologists, analysis of large-scale data and systems biology approaches.

Combinatorial drug therapy for cancer in the post-genomic era

Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy.

Gene family matters: expanding the HGNC resource

The HUGO Gene Nomenclature Committee (HGNC) assigns approved gene symbols to human loci. There are currently over 33,000 approved gene symbols, the majority of which represent protein-coding genes, but we also name other locus types such as non-coding RNAs, pseudogenes and phenotypic loci. Where relevant, the HGNC organise these genes into gene families and groups. The HGNC website http://www.genenames.org/ is an online repository of HGNC-approved gene nomenclature and associated resources for human genes, and includes links to genomic, proteomic and phenotypic information. In addition to this, we also have dedicated gene family web pages and are currently expanding and generating more of these pages using data curated by the HGNC and from information derived from external resources that focus on particular gene families. Here, we review our current online resources with a particular focus on our gene family data, using it to highlight our new Gene Symbol Report and gene family data downloads.

Different gene sets contribute to different symptom dimensions of depression and anxiety

Although many genetic association studies have been carried out, it remains unclear which genes contribute to depression. This may be due to heterogeneity of the DSM-IV category of depression. Specific symptom-dimensions provide a more homogenous phenotype. Furthermore, as effects of individual genes are small, analysis of genetic data at the pathway-level provides more power to detect associations and yield valuable biological insight. In 1,398 individuals with a Major Depressive Disorder, the symptom dimensions of the tripartite model of anxiety and depression, General Distress, Anhedonic Depression, and Anxious Arousal, were measured with the Mood and Anxiety Symptoms Questionnaire (30-item Dutch adaptation; MASQ-D30). Association of these symptom dimensions with candidate gene sets and gene sets from two public pathway databases was tested using the Global test. One pathway was associated with General Distress, and concerned molecules expressed in the endoplasmatic reticulum lumen. Seven pathways were associated with Anhedonic Depression. Important themes were neurodevelopment, neurodegeneration, and cytoskeleton. Furthermore, three gene sets associated with Anxious Arousal regarded development, morphology, and genetic recombination. The individual pathways explained up to 1.7% of the variance. These data demonstrate mechanisms that influence the specific dimensions. Moreover, they show the value of using dimensional phenotypes on one hand and gene sets on the other hand.

Identification of aberrant pathways and network activities from high-throughput data

Many complex diseases such as cancer are associated with changes in biological pathways and molecular networks rather than being caused by single gene alterations. A major challenge in the diagnosis and treatment of such diseases is to identify characteristic aberrancies in the biological pathways and molecular network activities and elucidate their relationship to the disease. This review presents recent progress in using high-throughput biological assays to decipher aberrant pathways and network activities. In particular, this review provides specific examples in which high-throughput data have been applied to identify relationships between diseases and aberrant pathways and network activities. The achievements in this field have been remarkable, but many challenges have yet to be addressed.

Genomics and bioinformatics of Parkinson's disease

Within the last two decades, genomics and bioinformatics have profoundly impacted our understanding of the molecular mechanisms of Parkinson's disease (PD). From the description of the first PD gene in 1997 until today, we have witnessed the emergence of new technologies that have revolutionized our concepts to identify genetic mechanisms implicated in human health and disease. Driven by the publication of the human genome sequence and followed by the description of detailed maps for common genetic variability, novel applications to rapidly scrutinize the entire genome in a systematic, cost-effective manner have become a reality. As a consequence, about 30 genetic loci have been unequivocally linked to the pathogenesis of PD highlighting essential molecular pathways underlying this common disorder. Herein we discuss how neurogenomics and bioinformatics are applied to dissect the nature of this complex disease with the overall aim of developing rational therapeutic interventions.

Involvement of microRNA families in cancer

Collecting representative sets of cancer microRNAs (miRs) from the literature we show that their corresponding families are enriched in sets of highly interacting miR families. Targeting cancer genes on a statistically significant level, such cancer miR families strongly intervene with signaling pathways that harbor numerous cancer genes. Clustering miR family-specific profiles of pathway intervention, we found that different miR families share similar interaction patterns. Resembling corresponding patterns of cancer miRs families, such interaction patterns may indicate a miR family’s potential role in cancer. As we find that the number of targeted cancer genes is a naïve proxy for a cancer miR family, we design a simple method to predict candidate miR families based on gene-specific interaction profiles. Assessing the impact of miR families to distinguish between (non-)cancer genes, we predict a set of 84 potential candidate families, including 75% of initially collected cancer miR families. Further confirming their relevance, predicted cancer miR families are significantly indicated in increasing, non-random numbers of tumor types.

DEFOG: discrete enrichment of functionally organized genes

High-throughput biological experiments commonly result in a list of genes or proteins of interest. In order to understand the observed changes of the genes and to generate new hypotheses, one needs to understand the functions and roles of the genes and how those functions relate to the experimental conditions. Typically, statistical tests are performed in order to detect enriched Gene Ontology categories or pathways, i.e. the categories are observed in the genes of interest more often than is expected by chance. Depending on the number of genes and the complexity and quantity of functions in which they are involved, such an analysis can easily result in hundreds of enriched terms. To this end we developed DEFOG, a web-based application that facilitates the functional analysis of gene sets by hierarchically organizing the genes into functionally related modules. Our computational pipeline utilizes three powerful tools to achieve this goal: (1) GeneMANIA creates a functional consensus network of the genes of interest based on gene-list-specific data fusion of hundreds of genomic networks from publicly available sources; (2) Transitivity Clustering organizes those genes into a clear hierarchy of functionally related groups, and (3) Ontologizer performs a Gene Ontology enrichment analysis on the resulting gene clusters. DEFOG integrates this computational pipeline within an easy-to-use web interface, thus allowing for a novel visual analysis of gene sets that aids in the discovery of potentially important biological mechanisms and facilitates the creation of new hypotheses. DEFOG is available at http://www.mooneygroup.org/defog.

Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer

Although 2[18F]fluoro-2-deoxy-d-glucose (FDG) uptake during positron emission tomography (PET) predicts post-surgical outcome in patients with non-small cell lung cancer (NSCLC), the biologic basis for this observation is not fully understood. Here, we analyzed 25 tumors from patients with NSCLCs to identify tumor PET-FDG uptake features associated with gene expression signatures and survival. Fourteen quantitative PET imaging features describing FDG uptake were correlated with gene expression for single genes and coexpressed gene clusters (metagenes). For each FDG uptake feature, an associated metagene signature was derived, and a prognostic model was identified in an external cohort and then tested in a validation cohort of patients with NSCLC. Four of eight single genes associated with FDG uptake (LY6E, RNF149, MCM6, and FAP) were also associated with survival. The most prognostic metagene signature was associated with a multivariate FDG uptake feature [maximum standard uptake value (SUV(max)), SUV(variance), and SUV(PCA2)], each highly associated with survival in the external [HR, 5.87; confidence interval (CI), 2.49-13.8] and validation (HR, 6.12; CI, 1.08-34.8) cohorts, respectively. Cell-cycle, proliferation, death, and self-recognition pathways were altered in this radiogenomic profile. Together, our findings suggest that leveraging tumor genomics with an expanded collection of PET-FDG imaging features may enhance our understanding of FDG uptake as an imaging biomarker beyond its association with glycolysis.

CellBase, a comprehensive collection of RESTful web services for retrieving relevant biological information from heterogeneous sources

During the past years, the advances in high-throughput technologies have produced an unprecedented growth in the number and size of repositories and databases storing relevant biological data. Today, there is more biological information than ever but, unfortunately, the current status of many of these repositories is far from being optimal. Some of the most common problems are that the information is spread out in many small databases; frequently there are different standards among repositories and some databases are no longer supported or they contain too specific and unconnected information. In addition, data size is increasingly becoming an obstacle when accessing or storing biological data. All these issues make very difficult to extract and integrate information from different sources, to analyze experiments or to access and query this information in a programmatic way. CellBase provides a solution to the growing necessity of integration by easing the access to biological data. CellBase implements a set of RESTful web services that query a centralized database containing the most relevant biological data sources. The database is hosted in our servers and is regularly updated. CellBase documentation can be found at http://docs.bioinfo.cipf.es/projects/cellbase.

Inferring the regulatory network behind a gene expression experiment

Transcription factors (TFs) and miRNAs are the most important dynamic regulators in the control of gene expression in multicellular organisms. These regulatory elements play crucial roles in development, cell cycling and cell signaling, and they have also been associated with many diseases. The Regulatory Network Analysis Tool (RENATO) web server makes the exploration of regulatory networks easy, enabling a better understanding of functional modularity and network integrity under specific perturbations. RENATO is suitable for the analysis of the result of expression profiling experiments. The program analyses lists of genes and search for the regulators compatible with its activation or deactivation. Tests of single enrichment or gene set enrichment allow the selection of the subset of TFs or miRNAs significantly involved in the regulation of the query genes. RENATO also offers an interactive advanced graphical interface that allows exploring the regulatory network found.RENATO is available at: http://renato.bioinfo.cipf.es/.

Network analysis has diverse roles in drug discovery

Computational biologists use network analysis to uncover relationships between various data types of interest for drug discovery. For example, signalling and metabolic pathways are commonly used to understand disease states and drug mechanisms. However, several other flavours of network analysis techniques are also applicable in a drug discovery context. Recent advances include networks that encompass relationships between diseases, molecular mechanisms and gene targets. Even social networks that mirror interactions within the scientific community are helping to foster collaborations and novel research. We review how these different types of network analysis approaches facilitate drug discovery and their associated challenges.

Characterization of breast cancer interstitial fluids by TmT labeling, LTQ-Orbitrap Velos mass spectrometry, and pathway analysis

Cancer is currently considered as the end point of numerous genomic and epigenomic mutations and as the result of the interaction of transformed cells within the stromal microenvironment. The present work focuses on breast cancer, one of the most common malignancies affecting the female population in industrialized countries. In this study, we perform a proteomic analysis of bioptic samples from human breast cancer, namely, interstitial fluids and primary cells, normal vs disease tissues, using tandem mass tags (TmT) quantitative mass spectrometry combined with the MudPIT technique. To the best of our knowledge, this work, with over 1700 proteins identified, represents the most comprehensive characterization of the breast cancer interstitial fluid proteome to date. Network analysis was used to identify functionally active networks in the breast cancer associated samples. From the list of differentially expressed genes, we have retrieved the associated functional interaction networks. Many different signaling pathways were found activated, strongly linked to invasion, metastasis development, proliferation, and with a significant cross-talking rate. This pilot study presents evidence that the proposed quantitative proteomic approach can be applied to discriminate between normal and tumoral samples and for the discovery of yet unknown carcinogenesis mechanisms and therapeutic strategies.

Update on EPA's ToxCast program: providing high throughput decision support tools for chemical risk management

The field of toxicology is on the cusp of a major transformation in how the safety and hazard of chemicals are evaluated for potential effects on human health and the environment. Brought on by the recognition of the limitations of the current paradigm in terms of cost, time, and throughput, combined with the ever increasing power of modern biological tools to probe mechanisms of chemical-biological interactions at finer and finer resolutions, 21st century toxicology is rapidly taking shape. A key element of the new approach is a focus on the molecular and cellular pathways that are the targets of chemical interactions. By understanding toxicity in this manner, we begin to learn how chemicals cause toxicity, as opposed to merely what diseases or health effects they might cause. This deeper understanding leads to increasing confidence in identifying which populations might be at risk, significant susceptibility factors, and key influences on the shape of the dose-response curve. The U. S. Environmental Protection Agency (EPA) initiated the ToxCast, or "toxicity forecaster", program 5 years ago to gain understanding of the strengths and limitations of the new approach by starting to test relatively large numbers (hundreds) of chemicals against an equally large number of biological assays. Using computational approaches, the EPA is building decision support tools based on ToxCast in vitro screening results to help prioritize chemicals for further investigation, as well as developing predictive models for a number of health outcomes. This perspective provides a summary of the initial, proof of concept, Phase I of ToxCast that has laid the groundwork for the next phases and future directions of the program.

Personal omics profiling reveals dynamic molecular and medical phenotypes

Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.

Evolutionary conservation and network structure characterize genes of phenotypic relevance for mitosis in human

The impact of gene silencing on cellular phenotypes is difficult to establish due to the complexity of interactions in the associated biological processes and pathways. A recent genome-wide RNA knock-down study both identified and phenotypically characterized a set of important genes for the cell cycle in HeLa cells. Here, we combine a molecular interaction network analysis, based on physical and functional protein interactions, in conjunction with evolutionary information, to elucidate the common biological and topological properties of these key genes. Our results show that these genes tend to be conserved with their corresponding protein interactions across several species and are key constituents of the evolutionary conserved molecular interaction network. Moreover, a group of bistable network motifs is found to be conserved within this network, which are likely to influence the network stability and therefore the robustness of cellular functioning. They form a cluster, which displays functional homogeneity and is significantly enriched in genes phenotypically relevant for mitosis. Additional results reveal a relationship between specific cellular processes and the phenotypic outcomes induced by gene silencing. This study introduces new ideas regarding the relationship between genotype and phenotype in the context of the cell cycle. We show that the analysis of molecular interaction networks can result in the identification of genes relevant to cellular processes, which is a promising avenue for future research.

Computational tools for metabolic engineering

A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area.

Linking proteins to signaling pathways for experiment design and evaluation

Biomedical experimental work often focuses on altering the functions of selected proteins. These changes can hit signaling pathways, and can therefore unexpectedly and non-specifically affect cellular processes. We propose PathwayLinker, an online tool that can provide a first estimate of the possible signaling effects of such changes, e.g., drug or microRNA treatments. PathwayLinker minimizes the users' efforts by integrating protein-protein interaction and signaling pathway data from several sources with statistical significance tests and clear visualization. We demonstrate through three case studies that the developed tool can point out unexpected signaling bias in normal laboratory experiments and identify likely novel signaling proteins among the interactors of known drug targets. In our first case study we show that knockdown of the Caenorhabditis elegans gene cdc-25.1 (meant to avoid progeny) may globally affect the signaling system and unexpectedly bias experiments. In the second case study we evaluate the loss-of-function phenotypes of a less known C. elegans gene to predict its function. In the third case study we analyze GJA1, an anti-cancer drug target protein in human, and predict for this protein novel signaling pathway memberships, which may be sources of side effects. Compared to similar services, a major advantage of PathwayLinker is that it drastically reduces the necessary amount of manual literature searches and can be used without a computational background. PathwayLinker is available at http://PathwayLinker.org. Detailed documentation and source code are available at the website.

A framework for mapping, visualisation and automatic model creation of signal-transduction networks

An intuitive formalism for reconstructing cellular networks from empirical data is presented, and used to build a comprehensive yeast MAP kinase network. The accompanying rxncon software tool can convert networks to a range of standard graphical formats and mathematical models.

Network mapping at the granularity of empirical data that largely avoids combinatorial complexity

Automatic visualisation and model generation with the rxncon open source software tool

Visualisation in a range of formats, including all three SBGN formats, as well as contingency matrix or regulatory graph

Comprehensive and completely references map of the yeast MAP kinase network in the rxncon format

Intracellular signalling systems are highly complex. This complexity makes handling, analysis and visualisation of available knowledge a major challenge in current signalling research. Here, we present a novel framework for mapping signal-transduction networks that avoids the combinatorial explosion by breaking down the network in reaction and contingency information. It provides two new visualisation methods and automatic export to mathematical models. We use this framework to compile the presently most comprehensive map of the yeast MAP kinase network. Our method improves previous strategies by combining (I) more concise mapping adapted to empirical data, (II) individual referencing for each piece of information, (III) visualisation without simplifications or added uncertainty, (IV) automatic visualisation in multiple formats, (V) automatic export to mathematical models and (VI) compatibility with established formats. The framework is supported by an open source software tool that facilitates integration of the three levels of network analysis: definition, visualisation and mathematical modelling. The framework is species independent and we expect that it will have wider impact in signalling research on any system.

Computational approaches for analyzing information flow in biological networks

The advancements in "omics" (proteomics, genomics, lipidomics, and metabolomics) technologies have yielded large inventories of genes, transcripts, proteins, and metabolites. The challenge is to find out how these entities work together to regulate the processes by which cells respond to external and internal signals. Mathematical and computational modeling of signaling networks has a key role in this task, and network analysis provides insights into biological systems and has applications for medicine. Here, we review experimental and theoretical progress and future challenges toward this goal. We focus on how networks are reconstructed from data, how these networks are structured to control the flow of biological information, and how the design features of the networks specify biological decisions.

GeneSetDB: A comprehensive meta-database, statistical and visualisation framework for gene set analysis

Most “omics” experiments require comprehensive interpretation of the biological meaning of gene lists. To address this requirement, a number of gene set analysis (GSA) tools have been developed. Although the biological value of GSA is strictly limited by the breadth of the gene sets used, very few methods exist for simultaneously analysing multiple publically available gene set databases. Therefore, we constructed GeneSetDB (http://genesetdb.auckland.ac.nz/haeremai.html), a comprehensive meta-database, which integrates 26 public databases containing diverse biological information with a particular focus on human disease and pharmacology. GeneSetDB enables users to search for gene sets containing a gene identifier or keyword, generate their own gene sets, or statistically test for enrichment of an uploaded gene list across all gene sets, and visualise gene set enrichment and overlap using a clustered heat map.

Context-specific protein network miner--an online system for exploring context-specific protein interaction networks from the literature

BACKGROUND

Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis.

RESULTS

We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality.

CONCLUSIONS

CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/.

OREMPdb: a semantic dictionary of computational pathway models

Background

The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems.

Methods

Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets.

Results

OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared.

Gene expression profiling of peripheral blood mononuclear cells in the setting of peripheral arterial disease

BACKGROUND

Peripheral arterial disease (PAD) is a relatively common manifestation of systemic atherosclerosis that leads to progressive narrowing of the lumen of leg arteries. Circulating monocytes are in contact with the arterial wall and can serve as reporters of vascular pathology in the setting of PAD. We performed gene expression analysis of peripheral blood mononuclear cells (PBMC) in patients with PAD and controls without PAD to identify differentially regulated genes.

METHODS

PAD was defined as an ankle brachial index (ABI) ≤0.9 (n = 19) while age and gender matched controls had an ABI > 1.0 (n = 18). Microarray analysis was performed using Affymetrix HG-U133 plus 2.0 gene chips and analyzed using GeneSpring GX 11.0. Gene expression data was normalized using Robust Multichip Analysis (RMA) normalization method, differential expression was defined as a fold change ≥1.5, followed by unpaired Mann-Whitney test (P < 0.05) and correction for multiple testing by Benjamini and Hochberg False Discovery Rate. Meta-analysis of differentially expressed genes was performed using an integrated bioinformatics pipeline with tools for enrichment analysis using Gene Ontology (GO) terms, pathway analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG), molecular event enrichment using Reactome annotations and network analysis using Ingenuity Pathway Analysis suite. Extensive biocuration was also performed to understand the functional context of genes.

RESULTS

We identified 87 genes differentially expressed in the setting of PAD; 40 genes were upregulated and 47 genes were downregulated. We employed an integrated bioinformatics pipeline coupled with literature curation to characterize the functional coherence of differentially regulated genes.

CONCLUSION

Notably, upregulated genes mediate immune response, inflammation, apoptosis, stress response, phosphorylation, hemostasis, platelet activation and platelet aggregation. Downregulated genes included several genes from the zinc finger family that are involved in transcriptional regulation. These results provide insights into molecular mechanisms relevant to the pathophysiology of PAD.

Extending signaling pathways with protein-interaction networks. Application to apoptosis

Cells exploit signaling pathways during responses to environmental changes, and these processes are often modulated during disease. Particularly, relevant human pathologies such as cancer or viral infections require downregulating apoptosis signaling pathways to progress. As a result, the identification of proteins responsible for these changes is essential for the diagnostics and development of therapeutics. Transferring functional annotation within protein interaction networks has proven useful to identify such proteins, although this is not a trivial task. Here, we used different scoring methods to transfer annotation from 53 well-studied members of the human apoptosis pathways (as known by 2005) to their protein interactors. All scoring methods produced significant predictions (compared to a random negative model), but its number was too large to be useful. Thus, we made a final prediction using specific combinations of scoring methods and compared it to the proteins related to apoptosis signaling pathways during the last 5 years. We propose 273 candidate proteins that may be relevant in apoptosis signaling pathways. Although some of them have known functions consistent with their proposed apoptotsis involvement, the majority have not been annotated yet, leaving room for further experimental studies. We provide our predictions at http://sbi.imim.es/web/Apoptosis.php.

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.

Large-scale mapping of human protein interactome using structural complexes

Although the identification of protein interactions by high-throughput (HTP) methods progresses at a fast pace, 'interactome' data sets still suffer from high rates of false positives and low coverage. To map the human protein interactome, we describe a new framework that uses experimental evidence on structural complexes, the atomic details of binding interfaces and evolutionary conservation. The structurally inferred interaction network is highly modular and more functionally coherent compared with experimental interaction networks derived from multiple literature citations. Moreover, structurally inferred and high-confidence HTP networks complement each other well, allowing us to construct a merged network to generate testable hypotheses and provide valuable experimental leads.

Infection Strategies of Bacterial and Viral Pathogens through Pathogen-Human Protein-Protein Interactions

Since ancient times, even in today’s modern world, infectious diseases cause lots of people to die. Infectious organisms, pathogens, cause diseases by physical interactions with human proteins. A thorough analysis of these interspecies interactions is required to provide insights about infection strategies of pathogens. Here we analyzed the most comprehensive available pathogen–human protein interaction data including 23,435 interactions, targeting 5,210 human proteins. The data were obtained from the newly developed pathogen–host interaction search tool, PHISTO. This is the first comprehensive attempt to get a comparison between bacterial and viral infections. We investigated human proteins that are targeted by bacteria and viruses to provide an overview of common and special infection strategies used by these pathogen types. We observed that in the human protein interaction network the proteins targeted by pathogens have higher connectivity and betweenness centrality values than those proteins not interacting with pathogens. The preference of interacting with hub and bottleneck proteins is found to be a common infection strategy of all types of pathogens to manipulate essential mechanisms in human. Compared to bacteria, viruses tend to interact with human proteins of much higher connectivity and centrality values in the human network. Gene Ontology enrichment analysis of the human proteins targeted by pathogens indicates crucial clues about the infection mechanisms of bacteria and viruses. As the main infection strategy, bacteria interact with human proteins that function in immune response to disrupt human defense mechanisms. Indispensable viral strategy, on the other hand, is the manipulation of human cellular processes in order to use that transcriptional machinery for their own genetic material transcription. A novel observation about pathogen–human systems is that the human proteins targeted by both pathogens are enriched in the regulation of metabolic processes.

Systems medicine and metabolic modelling

Several complex diseases are caused by the malfunction of human metabolism, and deciphering the underlying molecular mechanisms can elucidate their aetiology. Systems biology is an integrative approach combining experimental and computational biology to identify and describe the molecular mechanisms of complex biological systems. Systems medicine has the potential to elucidate the onset and progression of complex metabolic diseases through the use of computational approaches. Advances in biotechnology have resulted in the provision of high-throughput data, which provide information about different metabolic processes. The systems medicine approach can utilize such data to reconstruct genome-scale metabolic models that can be used to study the function of specific enzymes and pathways in the context of the complete metabolic network. In this review, we outline the importance of genome-scale models in systems medicine and discuss how they may contribute towards the development of personalized medicine.

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks

Background

Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph.

Results

We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. In order to easily retrieve and efficiently analyze these interactions, we developed In-teractomeBrowser, a graph-based knowledge browser that comes as a plug-in for Transcriptome-Browser. The first objective of InteractomeBrowser is to provide a user-friendly tool to get new insight into any gene list by providing a context-specific display of putative regulatory and physical interactions. To achieve this, InteractomeBrowser relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology to map gene products onto relevant cell compartments. This layout is particularly powerful for visual integration of heterogeneous biological information and is a productive avenue in generating new hypotheses. The second objective of InteractomeBrowser is to fill the gap between interaction databases and dynamic modeling. It is thus compatible with the network analysis software Cytoscape and with the Gene Interaction Network simulation software (GINsim). We provide examples underlying the benefits of this visualization tool for large gene set analysis related to thymocyte differentiation.

Conclusions

The InteractomeBrowser plugin is a powerful tool to get quick access to a knowledge database that includes both predicted and validated molecular interactions. InteractomeBrowser is available through the TranscriptomeBrowser framework and can be found at: http://tagc.univ-mrs.fr/tbrowser/. Our database is updated on a regular basis.

Reactome pathway analysis to enrich biological discovery in proteomics data sets

Reactome (http://www.reactome.org) is an open-source, expert-authored, peer-reviewed, manually curated database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Pathway Browser is a Systems Biology Graphical Notation-like visualization system that supports manual navigation of pathways by zooming, scrolling and event highlighting, and that exploits PSI Common Query Interface web services to overlay pathways with molecular interaction data from the Reactome Functional Interaction Network and interaction databases such as IntAct, ChEMBL and BioGRID. Pathway and expression analysis tools employ web services to provide ID mapping, pathway assignment and over-representation analysis of user-supplied data sets. By applying Ensembl Compara to curated human proteins and reactions, Reactome generates pathway inferences for 20 other species. The Species Comparison tool provides a summary of results for each of these species as a table showing numbers of orthologous proteins found by pathway from which users can navigate to inferred details for specific proteins and reactions. Reactome's diverse pathway knowledge and suite of data analysis tools provide a platform for data mining, modeling and analysis of large-scale proteomics data sets. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 8).

Translational bioinformatics in psychoneuroimmunology: methods and applications

Translational bioinformatics plays an indispensable role in transforming psychoneuroimmunology (PNI) into personalized medicine. It provides a powerful method to bridge the gaps between various knowledge domains in PNI and systems biology. Translational bioinformatics methods at various systems levels can facilitate pattern recognition, and expedite and validate the discovery of systemic biomarkers to allow their incorporation into clinical trials and outcome assessments. Analysis of the correlations between genotypes and phenotypes including the behavioral-based profiles will contribute to the transition from the disease-based medicine to human-centered medicine. Translational bioinformatics would also enable the establishment of predictive models for patient responses to diseases, vaccines, and drugs. In PNI research, the development of systems biology models such as those of the neurons would play a critical role. Methods based on data integration, data mining, and knowledge representation are essential elements in building health information systems such as electronic health records and computerized decision support systems. Data integration of genes, pathophysiology, and behaviors are needed for a broad range of PNI studies. Knowledge discovery approaches such as network-based systems biology methods are valuable in studying the cross-talks among pathways in various brain regions involved in disorders such as Alzheimer's disease.

Cross species integration of functional genomics experiments

There is an increasing recognition of the value in integrating behavioral genomics data across species. The fragmentation of public resources, interoperability, and available representations present challenges due to the array of identifiers used to represent each genome feature. Once data are organized into a coherent collection, they can be integrated using a variety of methods to analyze convergent evidence for the roles of genes in behaviors. GeneWeaver.org is a web-based software system that employs many of these techniques and has been used in the study of complex behavior and addiction. These techniques will be increasingly necessary to understand global patterns emerging from experiments in behavioral genomics.

Pathways to cognitive deficits in Down syndrome

Major efforts in Down syndrome (DS) research have been directed at the identification and functional characterization of genes encoded by human chromosome 21 (HSA21). In parallel with this, tissue samples and cell lines derived from individuals with DS have been examined for abnormalities in gene expression and cellular morphology, and mouse models of DS have been characterized for abnormalities at the molecular, cellular, electrophysiological, and behavioral level. One goal of such investigations has been the identification of effective targets for pharmacotherapies that can prevent or correct the abnormalities and, by extension to human clinical trials, prevent or lessen aspects of the cognitive deficits seen in people with DS. Because it is caused by an extra copy of an entire chromosome, DS has been considered by some as too complicated a genetic perturbation to be amenable to postnatal pharmacological interventions. However, recent data from experiments with one mouse model, the Ts65Dn, have clearly demonstrated that several pharmacological interventions can indeed rescue DS-relevant learning and memory deficits. Extension of mouse data to successful human clinical trials will be aided by understanding the molecular basis of successful drug treatments, that is, how increased expression of HSA21 genes perturbs molecular mechanisms that are targeted and rescued by specific drugs. Here, we review information on HSA21 genes, their expression and their likely contributions to the DS phenotype. We then describe results of a bioinformatics effort that integrates information on genes known to cause intellectual disability when mutated, the pathways in which these genes function, and how these pathways are impacted by HSA21 encoded proteins. This pathway approach to the molecular basis of ID in DS aids in understanding why some drug therapies have been successful in the Ts65Dn and in predicting whether these same drugs are likely to be successful in treating ID in DS. These data can be used to design new experiments and interpret information for prediction of additional targets for effective drug treatments.