Knowledge-based Pathway Research Articles

Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz.

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.

Long‐Term Electrically‐Induced Muscle Exercise Duration Modulates Distinct Gene Signaling Pathways in People with Spinal Cord Injury

Exercise as a form of medicine attenuates the development of chronic non‐communicable diseases (NCDs) in people with intact nervous systems as they age. However, there is a knowledge gap regarding the influence of electrically induced skeletal muscle exercise on NCDs in people with spinal cord injury (SCI). Gene signaling pathway analysis offers an opportunity to understand the long‐term effects of electrically induced exercise on people with chronic and complete paralysis from SCI; providing a basis for interventions to be studied in future clinical trials.PurposeTo determine if long term electrically induced skeletal muscle exercise regulates complex network signaling pathways associated with improved metabolic health in people with SCI.MethodsWe analyzed 17 paired muscle samples from 11 males with complete SCI who completed at least 3 months of a unilateral exercise training program using electrical stimulation. During their exercise training, participants underwent routine physiological assessments. At the end of their exercise training, muscle samples were obtained from the exercised and non‐exercised limb. Extracted RNA was hybridized to Affymetrix Human Exon microarrays. Probe intensities were normalized using a robust multi‐array average. Gene expression values were analyzed using an a priori list of gene sets defined by the Reactome Pathways Knowledgebase and genes related to metabolic and hypertrophy pathways in skeletal muscle using the gene set enrichment algorithm and a fold‐change signature. Muscle biopsy samples were grouped together as a single cohort, and subdivided into 2 groups based on the training duration and muscle physiologic properties (<6 and >6 months of training). All participants provided written consent approved by the University of Iowa Institutional Review Board in compliance with the Declaration of Helsinki.ResultsWe analyzed over 17,000 genes and 1,900 genesets curated from the Reactome Pathways Knowledgebase. When we subdivided the muscle samples into 2 phenotypes based on training duration, we found 19 pathways associated with metabolism and mitochondrial function to be significantly up regulated (FDR < 0.25) in the trained phenotype compared to the untrained phenotype. A subset of representative genes that were up regulated included the mitochondria protein carrier genes (MPC1 and MPC2), the citric acid cycle genes (SDHA, SDHB), the oxidative phosphorylation genes (NDUFA2 and NDUFB1), and mitochondrial biogenesis genes (PGC1α).ConclusionsOverall health is dependent, in part, on skeletal muscle size and metabolic capacity. 6 months of chronic electrically induced exercise was sufficient to reverse the expression of important metabolic gene pathways needed to recreate an oxidative phenotype in chronically paralyzed skeletal muscles. Future studies are underway to ascertain if electrically induced exercise attenuates the development of NCDs in people with SCI.Support or Funding InformationResearch Support: This study was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development: R01HD084645, R01HD082109This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Expression of miR‑542‑3p in osteosarcoma with miRNA microarray data, and its potential signaling pathways.

Osteosarcoma (OS) is the most common pediatric primary bone tumor, with high malignancy rates and a poor prognosis following metastasis. At present, the role of microRNA (miR)-542-3p in OS remains to be elucidated. The purpose of the present study was to investigate the expression level of miR-542-3p in OS, and its potential molecular mechanisms, via a bioinformatics analysis. First, the expression of miR-542-3p in OS based on the continuous variables of the Gene Expression Omnibus database and PubMed was studied. Subsequently, the potential target genes of miR-542-3p were predicted using gene expression profiles and bioinformatics software. On the basis of the Database for Annotation, Visualization and Integrated Discovery, version 6.8, a study of gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway knowledge base was conducted to explore the biological value of miR-542-3p in OS. Finally, the protein-protein interaction (PPI) network was completed using the STRING database. The expression of miR-542-3p in OS was revealed to be significantly higher compared with that in normal tissue. In total, 1,036 target genes of miR-542-3p were obtained. The results of the GO enrichment analysis revealed that the significant terms were ‘bone development’, ‘cell cycle arrest’ and ‘intracellular signal transduction’. The results of the KEGG analysis revealed the highlighted pathways that were targeted to miR-542-3p, including the sphingolipid signaling pathway (P=3.91×10−5), the phosphoinositide 3-kinase (PI3K)-AKT serine/threonine kinase (AKT) signaling pathway (P=3.17×10−5) and the insulin signaling pathway (P=1.04×10−5). The PPI network revealed eight hub genes: Ubiquitin-60S ribosomal protein L40, Ras-related C3 botulinum toxin substrate, mitogen-activated protein kinase 1, epidermal growth factor receptor, cystic fibrosis transmembrane conductance regulator, PI3K regulatory subunit 1, AKT1, and actin-related protein 2/3 complex subunit 1A, which may be the key target genes of miR-542-3p in OS. Taken together, these results have demonstrated that miR-542-3p was overexpressed in OS. The potential target genes and biological functions of miR-542-3p may provide novel insights into the differentially expressed genes that are involved in OS.

Analyzing the Structure of Pathways and Its Influence on the Interpretation of Biomedical Proteomics Data Sets.

Biochemical pathways are commonly used as a reference to conduct functional analysis on biomedical omics data sets, where experimental results are mapped to knowledgebases comprising known molecular interactions collected from the literature. Due to their central role, the content of the functional knowledgebases directly influences the outcome of pathway analyses. In this study, we investigate the structure of the current pathway knowledge, as exemplified by Reactome, discuss the consequences for biological interpretation, and outline possible improvements in the use of pathway knowledgebases. By providing a view of the underlying protein interaction network, we aim to help pathway analysis users manage their expectations and better identify possible artifacts in the results.

Approaching the development of alternative testing methods using information available in the adverse outcome pathway knowledgebase (AOP-KB)

Approaching the development of alternative testing methods using information available in the adverse outcome pathway knowledgebase (AOP-KB)

Creating a Structured AOP Knowledgebase via Ontology-Based Annotations.

The Adverse Outcome Pathway framework is increasingly used to integrate data generated based on traditional and emerging toxicity testing paradigms. As the number of AOP descriptions has increased, so has the need to define the AOP in computable terms. Herein, we present a comprehensive annotation of 172 AOPs housed in the AOP-Wiki as of December 4, 2016 using terms from existing biological ontologies. AOP Key Events (KEs) were assigned ontology terms using a concept called the Event Component, which consists of a Process, an Object, and an Action term, with each term originating from ontologies and other controlled vocabularies. Annotation of KEs with ontology classes from fourteen ontologies and controlled vocabularies resulted in a total of 685 KEs being annotated with a total of 809 Event Components. A set of seven conventions resulted, defining the annotation of KEs via Event Components. This expanded annotation of AOPs allows computational reasoners to aid in both AOP development and applications. In addition, the incorporation of explicit biological objects will reduce the time required for converting a qualitative AOP description into a conceptual model that can support computational modeling. As high throughput genomics becomes a more important part of the high throughput toxicity testing landscape, the new approaches described here for annotating key events will also promote the visualization and analysis of genomics data in an AOP context.

Cetuximab Prevents Methotrexate-Induced Cytotoxicity in Vitro through Epidermal Growth Factor Dependent Regulation of Renal Drug Transporters.

The combination of methotrexate with epidermal growth factor receptor (EGFR) recombinant antibody, cetuximab, is currently being investigated in treatment of head and neck carcinoma. As methotrexate is cleared by renal excretion, we studied the effect of cetuximab on renal methotrexate handling. We used human conditionally immortalized proximal tubule epithelial cells overexpressing either organic anion transporter 1 or 3 (ciPTEC-OAT1/ciPTEC-OAT3) to examine OAT1 and OAT3, and the efflux pumps breast cancer resistance protein (BCRP), multidrug resistance protein 4 (MRP4), and P-glycoprotein (P-gp) in methotrexate handling upon EGF or cetuximab treatment. Protein kinase microarrays and knowledge-based pathway analysis were used to predict EGFR-mediated transporter regulation. Cytotoxic effects of methotrexate were evaluated using the dimethylthiazol bromide (MTT) viability assay. Methotrexate inhibited OAT-mediated fluorescein uptake and decreased efflux of Hoechst33342 and glutathione-methylfluorescein (GS-MF), which suggested involvement of OAT1/3, BCRP, and MRP4 in transepithelial transport, respectively. Cetuximab reversed the EGF-increased expression of OAT1 and BCRP as well as their membrane expressions and transport activities, while MRP4 and P-gp were increased. Pathway analysis predicted cetuximab-induced modulation of PKC and PI3K pathways downstream EGFR/ERBB2/PLCg. Pharmacological inhibition of ERK decreased expression of OAT1 and BCRP, while P-gp and MRP4 were increased. AKT inhibition reduced all transporters. Exposure to methotrexate for 24 h led to a decreased viability, an effect that was reversed by cetuximab. In conclusion, cetuximab downregulates OAT1 and BCRP while upregulating P-gp and MRP4 through an EGFR-mediated regulation of PI3K-AKT and MAPKK-ERK pathways. Consequently, cetuximab attenuates methotrexate-induced cytotoxicity, which opens possibilities for further research into nephroprotective comedication therapies.

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties.

Interest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g., the European Union's Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine structural similarity with shared mechanisms of action. Substances with similar chemical structure and toxicological profile form candidate categories suitable for read-across. We combined two databases on repeated dose toxicity, RepDose database, and ELINCS database to form a common database for the identification of categories. The resulting database contained physicochemical, structural, and toxicological data, which were refined and curated for cluster analyses. We applied the Predictive Clustering Tree (PCT) approach for clustering chemicals based on structural and on toxicological information to detect groups of chemicals with similar toxic profiles and pathways/mechanisms of toxicity. As many of the experimental toxicity values were not available, this data was imputed by predicting them with a multi-label classification method, prior to clustering. The clustering results were evaluated by assessing chemical and toxicological similarities with the aim of identifying clusters with a concordance between structural information and toxicity profiles/mechanisms. From these chosen clusters, seven were selected for a quantitative read-across, based on a small ratio of NOAEL of the members with the highest and the lowest NOAEL in the cluster (< 5). We discuss the limitations of the approach. Based on this analysis we propose improvements for a follow-up approach, such as incorporation of metabolic information and more detailed mechanistic information. The software enables the user to allocate a substance in a cluster and to use this information for a possible read- across. The clustering tool is provided as a free web service, accessible at http://mlc-reach.informatik.uni-mainz.de.

Metabolome Analyses Uncovered a Novel Inhibitory Effect of Acyclic Retinoid on Aberrant Lipogenesis in a Mouse Diethylnitrosamine-Induced Hepatic Tumorigenesis Model.

Acyclic retinoid (ACR) is a promising drug under clinical trials for preventing recurrence of hepatocellular carcinoma. The objective of this study was to gain insights into molecular basis of the antitumorigenic action of ACR from a metabolic point of view. To achieve this, comprehensive cationic and lipophilic liver metabolic profiling was performed in mouse diethylnitrosamine (DEN)-induced hepatic tumorigenesis model using both capillary electrophoresis time-of-flight mass spectrometry and liquid chromatography time-of-flight mass spectrometry. ACR significantly counteracted against acceleration of lipogenesis but not glucose metabolism in DEN-treated mice liver, suggesting an important role of lipid metabolic reprogramming in the initiation step of hepatic tumorigenesis. Knowledge-based pathway analysis suggested that inhibition of linoleic acid metabolites such as arachidonic acid, a proinflammatory precursor, played a crucial role in the prevention by ACR of DEN-induced chronic inflammation-mediated tumorigenesis of the liver. As a molecular mechanism of the ACR's effect to prevent the aberrant lipogenesis, microarray analysis identified that a key transcription regulator of both embryogenesis and tumorigenesis, COUP transcription factor 2, also known as NR2F2, was associated with the metabolic effect of ACR in human hepatocellular carcinoma cells. Our study provided potential therapeutic targets for the chemoprevention of hepatocellular carcinoma as well as new insights into the mechanisms underlying prevention of hepatic tumorigenesis.

Adverse Outcome Pathways Knowledge Base (AOP-KB)

Adverse Outcome Pathways Knowledge Base (AOP-KB)

A standardized randomized 6-month aerobic exercise-training down-regulated pro-inflammatory genes, but up-regulated anti-inflammatory, neuron survival and axon growth-related genes

There is considerable support for the view that aerobic exercise may confer cognitive benefits to mild cognitively impaired elderly persons. However, the biological mechanisms mediating these effects are not entirely clear. As a preliminary step towards informing this gap in knowledge, we enrolled older adults confirmed to have mild cognitive impairment (MCI) in a 6-month exercise program. Male and female subjects were randomized into a 6-month program of either aerobic or stretch (control) exercise. Data collected from the first 10 completers, aerobic exercise (n=5) or stretch (control) exercise (n=5), were used to determine intervention-induced changes in the global gene expression profiles of the aerobic and stretch groups. Using microarray, we identified genes with altered expression (relative to baseline values) in response to the 6-month exercise intervention. Genes whose expression were altered by at least two-fold, and met the p-value cutoff of 0.01 were inputted into the Ingenuity Pathway Knowledge Base Library to generate gene-interaction networks. After a 6-month aerobic exercise-training, genes promoting inflammation became down-regulated, whereas genes having anti-inflammatory properties and those modulating immune function or promoting neuron survival and axon growth, became up-regulated (all fold change≥±2.0, p<0.01). These changes were not observed in the stretch group. Importantly, the differences in the expression profiles correlated with significant improvement in maximal oxygen uptake (VO2max) in the aerobic program as opposed to the stretch group. We conclude that three distinct cellular pathways may collectively influence the training effects of aerobic exercise in MCI subjects. We plan to confirm these effects using rt-PCR and correlate such changes with the cognitive phenotype.

Intrauterine growth restriction modifies gene expression profiling in cord blood

Small-for-gestational-age (SGA) newborns are at an increased risk for perinatal morbidity and mortality and development of metabolic syndromes such as cardiovascular disease and type 2 diabetes mellitus (T2DM) in adulthood. The mechanism underlying this increased risk remains unclear. In this study, genetic modifications of cord blood were investigated to characterize fetal change in SGA newborns. Gene expression in cord blood cells was compared between 10 SGA newborns and 10 appropriate-for-gestational-age (AGA) newborns using microarray analysis. Pathway analysis was conducted using the Ingenuity Pathways Knowledge Base. To confirm the microarray analysis results, quantitative real-time polymerase chain reaction (RT-PCR) was performed for upregulated genes in SGA newborns. In total, 775 upregulated and 936 downregulated probes were identified in SGA newborns and compared with those in AGA newborns. Of these probes, 1149 were annotated. Most of these genes have been implicated in the development of cardiovascular disease and T2DM. There was good agreement between the RT-PCR and microarray analyses results. Expression of certain genes was modified in SGA newborns in the fetal period. These genes have been associated with metabolic syndrome. To clarify the association between modified gene expression in cord blood and individual vulnerability to metabolic syndrome in adulthood, these SGA newborns will be have long-term follow up for examination of genetic and postnatal environmental factors. Gene expression of cord blood can be a useful and non-invasive method of investigation of genetic alterations in the fetal period.

CSML2SBML: a novel tool for converting quantitative biological pathway models from CSML into SBML.

CSML and SBML are XML-based model definition standards which are developed with the aim of creating exchange formats for modeling, visualizing and simulating biological pathways. In this article we report a release of a format convertor for quantitative pathway models, namely CSML2SBML. It translates models encoded by CSML into SBML without loss of structural and kinetic information. The simulation and parameter estimation of the resulting SBML model can be carried out with compliant tool CellDesigner for further analysis. The convertor is based on the standards CSML version 3.0 and SBML Level 2 Version 4. In our experiments, 11 out of 15 pathway models in CSML model repository and 228 models in Macrophage Pathway Knowledgebase (MACPAK) are successfully converted to SBML models. The consistency of the resulting model is validated by libSBML Consistency Check of CellDesigner. Furthermore, the converted SBML model assigned with the kinetic parameters translated from CSML model can reproduce the same dynamics with CellDesigner as CSML one running on Cell Illustrator. CSML2SBML, along with its instructions and examples for use are available at http://csml2sbml.csml.org.

The Reactome pathway knowledgebase

Reactome (http://www.reactome.org) is a manually curated open-source open-data resource of human pathways and reactions. The current version 46 describes 7088 human proteins (34% of the predicted human proteome), participating in 6744 reactions based on data extracted from 15 107 research publications with PubMed links. The Reactome Web site and analysis tool set have been completely redesigned to increase speed, flexibility and user friendliness. The data model has been extended to support annotation of disease processes due to infectious agents and to mutation.

Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.

Comparison of Spheroids Formed by Rat Glioma Stem Cells and Neural Stem Cells Reveals Differences in Glucose Metabolism and Promising Therapeutic Applications

Cancer stem cells (CSCs) are thought to be partially responsible for cancer resistance to current therapies and tumor recurrence. Dichloroacetate (DCA), a compound capable of shifting metabolism from glycolysis to glucose oxidation, via an inhibition of pyruvate dehydrogenase kinase was used. We show that DCA is able to shift the pyruvate metabolism in rat glioma CSCs but has no effect in rat neural stem cells. DCA forces CSCs into oxidative phosphorylation but does not trigger the production of reactive oxygen species and consecutive anti-cancer apoptosis. However, DCA, associated with etoposide or irradiation, induced a Bax-dependent apoptosis in CSCs in vitro and decreased their proliferation in vivo. The former phenomenon is related to DCA-induced Foxo3 and p53 expression, resulting in the overexpression of BH3-only proteins (Bad, Noxa, and Puma), which in turn facilitates Bax-dependent apoptosis. Our results demonstrate that a small drug available for clinical studies potentiates the induction of apoptosis in glioma CSCs.

The KUPKB: a novel Web application to access multiomics data on kidney disease

The information gathered from the large number of omics experiments in renal biology is underexplored, as it is scattered over many publications or held in supplemental data. To address this, we have developed an open-source Kidney and Urinary Pathway Knowledge Base (KUPKB) that facilitates simple exploration of these omics data. The KUPKB currently comprises 220 data sets (miRNA, mRNA, proteins, and metabolites) extracted from existing publications or databases. Researchers can explore the integrated data using the iKUP browser, and a simple template is provided to submit new omics data sets to the knowledge base. As an example of iKUP's use, we show how we identified, in silico, calreticulin as a protein induced in human interstitial fibrosis and tubular atrophy (IFTA) in chronic kidney transplant rejection; a link that would have been difficult to establish using existing Web-based tools. Using immunohistochemistry, we validated in vivo this in silico result in human and rat biopsies of IFTA, thus identifying calreticulin as a potential new player in chronic kidney transplant rejection. The KUPKB provides a simple tool that enables users to quickly survey a wide range of omics data sets and has been shown to facilitate rapid hypothesis generation in the context of renal pathophysiology.

BRCA1-mediated signaling pathways in ovarian carcinogenesis

The link between loss or defect in functional BRCA1 and predisposition for development of ovarian and breast cancer is well established. Germ-line mutations in BRCA1 are responsible for both hereditary breast and ovarian cancer, which is around 5-10% for all breast and 10-15% of all ovarian cancer cases. However, majority of cases of ovarian cancer are sporadic in nature. The inactivation of cellular BRCA1 due to mutations or loss of heterozygosity is one of the most commonly observed events in such cases. Complement-resistant retroviral BRCA1 vector, MFG-BRCA1, is the only approved gene therapy for ovarian cancer patients by the Federal and Drug Administration. Given the limited available information, there is a need to evaluate the effects of BRCA1 on the global gene expression pattern for better understanding the etiology of the disease. Here, we use Ingenuity Pathway Knowledge Base to examine the differential pattern of global gene expression due to stable expression of BRCA1 in the ovarian cancer cell line, SKOV3. The functional analysis detected at least five major pathways that were significantly (p < 0.05) altered. These include: cell to cell signaling and interaction, cellular function and maintenance, cellular growth and proliferation, cell cycle and DNA replication, and recombination repair. In addition, we were able to detect several biologically relevant genes that are central for various signaling networks involved in cellular homeostasis; TGF-β1, TP53, c-MYC, NF-κB and TNF-α. This report provides a comprehensive rationale for tumor suppressor function(s) of BRCA1 in ovarian carcinogenesis.

Genome-wide analysis distinguishes hyperglycemia regulated epigenetic signatures of primary vascular cells

Emerging evidence suggests that poor glycemic control mediates post-translational modifications to the H3 histone tail. We are only beginning to understand the dynamic role of some of the diverse epigenetic changes mediated by hyperglycemia at single loci, yet elevated glucose levels are thought to regulate genome-wide changes, and this still remains poorly understood. In this article we describe genome-wide histone H3K9/K14 hyperacetylation and DNA methylation maps conferred by hyperglycemia in primary human vascular cells. Chromatin immunoprecipitation (ChIP) as well as CpG methylation (CpG) assays, followed by massive parallel sequencing (ChIP-seq and CpG-seq) identified unique hyperacetylation and CpG methylation signatures with proximal and distal patterns of regionalization associative with gene expression. Ingenuity knowledge-based pathway and gene ontology analyses indicate that hyperglycemia significantly affects human vascular chromatin with the transcriptional up-regulation of genes involved in metabolic and cardiovascular disease. We have generated the first installment of a reference collection of hyperglycemia-induced chromatin modifications using robust and reproducible platforms that allow parallel sequencing-by-synthesis of immunopurified content. We uncover that hyperglycemia-mediated induction of genes and pathways associated with endothelial dysfunction occur through modulation of acetylated H3K9/K14 inversely correlated with methyl-CpG content.

Developing a kidney and urinary pathway knowledge base.

BackgroundChronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration.ResultsWe present a Semantic Web approach to developing a knowledge base that integrates data from high-throughput experiments on kidney and urine. A specialised KUP ontology is used to tie the various layers together, whilst background knowledge from external databases is incorporated by conversion into RDF. Using SPARQL as a query mechanism, we are able to query for proteins expressed in urine and place these back into the context of genes expressed in regions of the kidney.ConclusionsThe KUPKB gives KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. The Semantic Web technologies we use, together with the background knowledge from the domain’s ontologies, allows both rapid conversion and integration of this knowledge base. The KUPKB is still relatively small, but questions remain about scalability, maintenance and availability of the knowledge itself.AvailabilityThe KUPKB may be accessed via http://www.e-lico.eu/kupkb.