Mesenchyme-derived factors enhance preneoplastic growth by non-genotoxic carcinogens in rat liver.

Many frequently prescribed drugs are non-genotoxic carcinogens (NGC) in rodent liver. Their mode of action and health risks for humans remain to be elucidated. Here, we investigated the impact of two model NGC, the anti-epileptic drug phenobarbital (PB) and the contraceptive cyproterone acetate (CPA), on intrahepatic epithelial-mesenchymal crosstalk and on growth of first stages of hepatocarcinogenesis. Unaltered hepatocytes (HC) and preneoplastic HC (HCPREN) were isolated from rat liver for primary culture. DNA replication of HC and HCPREN was increased by in vitro treatment with 10 µM CPA, but not 1 mM PB. Next, mesenchymal cells (MC) obtained from liver of rats treated with either PB (50 mg/kg bw/day) or CPA (100 mg/kg bw/day), were cultured. Supernatants from both types of MC raised DNA synthesis of HC and HCPREN. This indicates that PB induces replication of HC and HCPREN only indirectly, via growth factors secreted by MC. CPA, however, acts on HC and HCPREN directly as well as indirectly via mesenchymal factors. Transcriptomics and bio-informatics revealed that PB and CPA induce extensive changes in the expression profile of MC affecting many growth factors and pathways. MC from PB-treated rats produced and secreted enhanced levels of HBEGF and GDF15, factors found to suppress apoptosis and/or induce DNA synthesis in cultured HC and HCPREN. MC from CPA-treated animals showed enhanced expression and secretion of HGF, which strongly raised DNA replication of HC and HCPREN. In conclusion, our findings reveal profound effects of two prototypical NGC on the hepatic mesenchyme. The resulting release of factors, which suppress apoptosis and/or enhance cell replication preferentially in cancer prestages, appears to be crucial for tumor promotion by NGC in the liver.

Coordinating Role of RXRα in Downregulating Hepatic Detoxification during Inflammation Revealed by Fuzzy-Logic Modeling.

During various inflammatory processes circulating cytokines including IL-6, IL-1ß, and TNFα elicit a broad and clinically relevant impairment of hepatic detoxification that is based on the simultaneous downregulation of many drug metabolizing enzymes and transporter genes. To address the question whether a common mechanism is involved we treated human primary hepatocytes with IL-6, the major mediator of the acute phase response in liver, and characterized acute phase and detoxification responses in quantitative gene expression and (phospho-)proteomics data sets. Selective inhibitors were used to disentangle the roles of JAK/STAT, MAPK, and PI3K signaling pathways. A prior knowledge-based fuzzy logic model comprising signal transduction and gene regulation was established and trained with perturbation-derived gene expression data from five hepatocyte donors. Our model suggests a greater role of MAPK/PI3K compared to JAK/STAT with the orphan nuclear receptor RXRα playing a central role in mediating transcriptional downregulation. Validation experiments revealed a striking similarity of RXRα gene silencing versus IL-6 induced negative gene regulation (rs = 0.79; P<0.0001). These results concur with RXRα functioning as obligatory heterodimerization partner for several nuclear receptors that regulate drug and lipid metabolism.

Dysregulated serum response factor triggers formation of hepatocellular carcinoma.

UNLABELLED: The ubiquitously expressed transcriptional regulator serum response factor (SRF) is controlled by both Ras/MAPK (mitogen-activated protein kinase) and Rho/actin signaling pathways, which are frequently activated in hepatocellular carcinoma (HCC). We generated SRF-VP16iHep mice, which conditionally express constitutively active SRF-VP16 in hepatocytes, thereby controlling subsets of both Ras/MAPK- and Rho/actin-stimulated target genes. All SRF-VP16iHep mice develop hyperproliferative liver nodules that progresses to lethal HCC. Some murine (m)HCCs acquire Ctnnb1 mutations equivalent to those in human (h)HCC. The resulting transcript signatures mirror those of a distinct subgroup of hHCCs, with shared activation of oncofetal genes including Igf2, correlating with CpG hypomethylation at the imprinted Igf2/H19 locus. CONCLUSION: SRF-VP16iHep mHCC reveal convergent Ras/MAPK and Rho/actin signaling as a highly oncogenic driver mechanism for hepatocarcinogenesis. This suggests simultaneous inhibition of Ras/MAPK and Rho/actin signaling as a treatment strategy in hHCC therapy.

Tumor promotion and inhibition by phenobarbital in livers of conditional Apc-deficient mice.

Activation of Wnt/ß-catenin signaling is important for human and rodent hepatocarcinogenesis. In mice, the tumor promoter phenobarbital (PB) selects for hepatocellular tumors with activating ß-catenin mutations via constitutive androstane receptor activation. PB-dependent tumor promotion was studied in mice with genetic inactivation of Apc, a negative regulator of ß-catenin, to circumvent the problem of randomly induced mutations by chemical initiators and to allow monitoring of PB- and Wnt/ß-catenin-dependent tumorigenesis in the absence of unknown genomic alterations. Moreover, the study was designed to investigate PB-induced proliferation of liver cells with activated ß-catenin. PB treatment provided Apc-deficient hepatocytes with only a minor proliferative advantage, and additional connexin 32 deficiency did not affect the proliferative response. PB significantly promoted the outgrowth of Apc-deficient hepatocellular adenoma (HCA), but simultaneously inhibited the formation of Apc-deficient hepatocellular carcinoma (HCC). The probability of tumor promotion by PB was calculated to be much lower for hepatocytes with loss of Apc, as compared to mutational ß-catenin activation. Comprehensive transcriptomic and phosphoproteomic characterization of HCA and HCC revealed molecular details of the two tumor types. HCC were characterized by a loss of differentiated hepatocellular gene expression, enhanced proliferative signaling, and massive over-activation of Wnt/ß-catenin signaling. In conclusion, PB exerts a dual role in liver tumor formation by promoting the growth of HCA but inhibiting the growth of HCC. Data demonstrate that one and the same compound can produce opposite effects on hepatocarcinogenesis, depending on context, highlighting the necessity to develop a more differentiated view on the tumorigenicity of this model compound.

Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data.

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine-based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems.

Proinflammatory mesenchymal effects of the non-genotoxic hepatocarcinogen phenobarbital: a novel mechanism of antiapoptosis and tumor promotion.

Many environmental pollutants and drugs, including steroid hormones, hypolipidemics and antiepileptics, are non-genotoxic carcinogens (NGC) in rodent liver. The mechanism of action and the risk for human health are still insufficiently known. Here, we study the effects of phenobarbital (PB), a widely used model NGC, on hepatic epithelial-mesenchymal crosstalk and the impact on hepatic apoptosis. Mesenchymal cells (MC) and hepatocytes (HC) were isolated from control and PB-treated rat livers. PB induced extensive changes in gene expression in MC and much less in HC as shown by transcriptomics with oligoarrays. In MC only, transcript levels of numerous proinflammatory cytokines were elevated. Correspondingly, ELISA on the supernatant of MC from PB-treated rats revealed enhanced release of various cytokines. In cultured HC, this supernatant caused (i) nuclear translocation and activation of nuclear factor-κB (shown by immunoblots of nuclear extracts and reporter gene assays), (ii) elevated expression of proinflammatory genes and (iii) protection from the proapoptotic action of transforming growth factor beta 1 (TGFß1). PB treatment in vivo or in vitro elevated the production and release of tumor necrosis factor alpha from MC, which was identified as mainly responsible for the inhibition of apoptosis in HC. In conclusion, our findings reveal profound proinflammatory effects of PB on hepatic mesenchyme and mesenchymal-epithelial interactions. The resulting release of cytokines acts antiapoptotic in HC, an effect crucial for tumor promotion and carcinogenesis by NGC.

Loss of mitochondrial peptidase Clpp leads to infertility, hearing loss plus growth retardation via accumulation of CLPX, mtDNA and inflammatory factors.

The caseinolytic peptidase P (CLPP) is conserved from bacteria to humans. In the mitochondrial matrix, it multimerizes and forms a macromolecular proteasome-like cylinder together with the chaperone CLPX. In spite of a known relevance for the mitochondrial unfolded protein response, its substrates and tissue-specific roles are unclear in mammals. Recessive CLPP mutations were recently observed in the human Perrault variant of ovarian failure and sensorineural hearing loss. Here, a first characterization of CLPP null mice demonstrated complete female and male infertility and auditory deficits. Disrupted spermatogenesis already at the spermatid stage and ovarian follicular differentiation failure were evident. Reduced pre-/post-natal survival and marked ubiquitous growth retardation contrasted with only light impairment of movement and respiratory activities. Interestingly, the mice showed resistance to ulcerative dermatitis. Systematic expression studies detected up-regulation of other mitochondrial chaperones, accumulation of CLPX and mtDNA as well as inflammatory factors throughout tissues. T-lymphocytes in the spleen were activated. Thus, murine Clpp deletion represents a faithful Perrault model. The disease mechanism probably involves deficient clearance of mitochondrial components and inflammatory tissue destruction.

Identification of short terminal motifs enriched by antibodies using peptide mass fingerprinting.

MOTIVATION: Mass spectrometry-based protein profiling has become a key technology in biomedical research and biomarker discovery. Sample preparation strategies that reduce the complexity of tryptic digests by immunoaffinity substantially increase throughput and sensitivity in proteomic mass spectrometry. The scarce availability of peptide-specific capture antibodies limits these approaches. Recently antibodies directed against short terminal motifs were found to enrich subsets of peptides with identical terminal sequences. This approach holds the promise of a significant gain in efficiency. TXP (Triple X Proteomics) and context-independent motif specific/global proteome survey binders are variants of this concept. Principally the binding motifs of such antibodies have to be elucidated after generating these antibodies. This entails a substantial effort in the lab, as it requires synthetic peptide libraries and numerous mass spectrometry experiments. RESULTS: We present an algorithm for predicting the antibody-binding motif in a mass spectrum obtained from a tryptic digest of a common cell line after immunoprecipitation. The epitope prediction, based on peptide mass fingerprinting, reveals the most enriched terminal epitopes. The tool provides a P-value for each potential epitope, estimated by sampling random spectra from a peptide database. The second algorithm combines the predicted sequences to more complex binding motifs. A comparison with library screenings shows that the predictions made by the novel methods are reliable and reproducible indicators of the binding properties of an antibody.

Ha-ras and ß-catenin oncoproteins orchestrate metabolic programs in mouse liver tumors.

The process of hepatocarcinogenesis in the diethylnitrosamine (DEN) initiation/phenobarbital (PB) promotion mouse model involves the selective clonal outgrowth of cells harboring oncogene mutations in Ctnnb1, while spontaneous or DEN-only-induced tumors are often Ha-ras- or B-raf-mutated. The molecular mechanisms and pathways underlying these different tumor sub-types are not well characterized. Their identification may help identify markers for xenobiotic promoted versus spontaneously occurring liver tumors. Here, we have characterized mouse liver tumors harboring either Ctnnb1 or Ha-ras mutations via integrated molecular profiling at the transcriptional, translational and post-translational levels. In addition, metabolites of the intermediary metabolism were quantified by high resolution (1)H magic angle nuclear magnetic resonance. We have identified tumor genotype-specific differences in mRNA and miRNA expression, protein levels, post-translational modifications, and metabolite levels that facilitate the molecular and biochemical stratification of tumor phenotypes. Bioinformatic integration of these data at the pathway level led to novel insights into tumor genotype-specific aberrant cell signaling and in particular to a better understanding of alterations in pathways of the cell intermediary metabolism, which are driven by the constitutive activation of the ß-Catenin and Ha-ras oncoproteins in tumors of the two genotypes.

InCroMAP: integrated analysis of cross-platform microarray and pathway data.

SUMMARY: Microarrays are commonly used to detect changes in gene expression between different biological samples. For this purpose, many analysis tools have been developed that offer visualization, statistical analysis and more sophisticated analysis methods. Most of these tools are designed specifically for messenger RNA microarrays. However, today, more and more different microarray platforms are available. Changes in DNA methylation, microRNA expression or even protein phosphorylation states can be detected with specialized arrays. For these microarray technologies, the number of available tools is small compared with mRNA analysis tools. Especially, a joint analysis of different microarray platforms that have been used on the same set of biological samples is hardly supported by most microarray analysis tools. Here, we present InCroMAP, a tool for the analysis and visualization of high-level microarray data from individual or multiple different platforms. Currently, InCroMAP supports mRNA, microRNA, DNA methylation and protein modification datasets. Several methods are offered that allow for an integrated analysis of data from those platforms. The available features of InCroMAP range from visualization of DNA methylation data over annotation of microRNA targets and integrated gene set enrichment analysis to a joint visualization of data from all platforms in the context of metabolic or signalling pathways. AVAILABILITY: InCroMAP is freely available as Java™ application at www.cogsys.cs.uni-tuebingen.de/software/InCroMAP, including a comprehensive user's guide and example files.

TDP-43 regulates global translational yield by splicing of exon junction complex component SKAR.

TDP-43 is linked to neurodegenerative diseases including frontotemporal dementia and amyotrophic lateral sclerosis. Mostly localized in the nucleus, TDP-43 acts in conjunction with other ribonucleoproteins as a splicing co-factor. Several RNA targets of TDP-43 have been identified so far, but its role(s) in pathogenesis remains unclear. Using Affymetrix exon arrays, we have screened for the first time for splicing events upon TDP-43 knockdown. We found alternative splicing of the ribosomal S6 kinase 1 (S6K1) Aly/REF-like target (SKAR) upon TDP-43 knockdown in non-neuronal and neuronal cell lines. Alternative SKAR splicing depended on the first RNA recognition motif (RRM1) of TDP-43 and on 5'-GA-3' and 5'-UG-3' repeats within the SKAR pre-mRNA. SKAR is a component of the exon junction complex, which recruits S6K1, thereby facilitating the pioneer round of translation and promoting cell growth. Indeed, we found that expression of the alternatively spliced SKAR enhanced S6K1-dependent signaling pathways and the translational yield of a splice-dependent reporter. Consistent with this, TDP-43 knockdown also increased translational yield and significantly increased cell size. This indicates a novel mechanism of deregulated translational control upon TDP-43 deficiency, which might contribute to pathogenesis of the protein aggregation diseases frontotemporal dementia and amyotrophic lateral sclerosis.

ToxDBScan: Large-scale similarity screening of toxicological databases for drug candidates.

We present a new tool for hepatocarcinogenicity evaluation of drug candidates in rodents. ToxDBScan is a web tool offering quick and easy similarity screening of new drug candidates against two large-scale public databases, which contain expression profiles for substances with known carcinogenic profiles: TG-GATEs and DrugMatrix. ToxDBScan uses a set similarity score that computes the putative similarity based on similar expression of genes to identify chemicals with similar genotoxic and hepatocarcinogenic potential. We propose using a discretized representation of expression profiles, which use only information on up- or down-regulation of genes as relevant features. Therefore, only the deregulated genes are required as input. ToxDBScan provides an extensive report on similar compounds, which includes additional information on compounds, differential genes and pathway enrichments. We evaluated ToxDBScan with expression data from 15 chemicals with known hepatocarcinogenic potential and observed a sensitivity of 88 Based on the identified chemicals, we achieved perfect classification of the independent test set. ToxDBScan is publicly available from the ZBIT Bioinformatics Toolbox.

Pathway-based visualization of cross-platform microarray datasets.

MOTIVATION: Traditionally, microarrays were almost exclusively used for the genome-wide analysis of differential gene expression. But nowadays, their scope of application has been extended to various genomic features, such as microRNAs (miRNAs), proteins and DNA methylation (DNAm). Most available methods for the visualization of these datasets are focused on individual platforms and are not capable of integratively visualizing multiple microarray datasets from cross-platform studies. Above all, there is a demand for methods that can visualize genomic features that are not directly linked to protein-coding genes, such as regulatory RNAs (e.g. miRNAs) and epigenetic alterations (e.g. DNAm), in a pathway-centred manner. RESULTS: We present a novel pathway-based visualization method that is especially suitable for the visualization of high-throughput datasets from multiple different microarray platforms that were used for the analysis of diverse genomic features in the same set of biological samples. The proposed methodology includes concepts for linking DNAm and miRNA expression datasets to canonical signalling and metabolic pathways. We further point out strategies for displaying data from multiple proteins and protein modifications corresponding to the same gene. Ultimately, we show how data from four distinct platform types (messenger RNA, miRNA, protein and DNAm arrays) can be integratively visualized in the context of canonical pathways. AVAILABILITY: The described method is implemented as part of the InCroMAP application that is freely available at www.cogsys.cs.uni-tuebingen.de/software/InCroMAP. CONTACT: clemens.wrzodek@uni-tuebingen.de or andreas.zell@uni-tuebingen.de.

Qualitative translation of relations from BioPAX to SBML qual.

MOTIVATION: The biological pathway exchange language (BioPAX) and the systems biology markup language (SBML) belong to the most popular modeling and data exchange languages in systems biology. The focus of SBML is quantitative modeling and dynamic simulation of models, whereas the BioPAX specification concentrates mainly on visualization and qualitative analysis of pathway maps. BioPAX describes reactions and relations. In contrast, SBML core exclusively describes quantitative processes such as reactions. With the SBML qualitative models extension (qual), it has recently also become possible to describe relations in SBML. Before the development of SBML qual, relations could not be properly translated into SBML. Until now, there exists no BioPAX to SBML converter that is fully capable of translating both reactions and relations. RESULTS: The entire nature pathway interaction database has been converted from BioPAX (Level 2 and Level 3) into SBML (Level 3 Version 1) including both reactions and relations by using the new qual extension package. Additionally, we present the new webtool BioPAX2SBML for further BioPAX to SBML conversions. Compared with previous conversion tools, BioPAX2SBML is more comprehensive, more robust and more exact. AVAILABILITY: BioPAX2SBML is freely available at http://webservices.cs.uni-tuebingen.de/ and the complete collection of the PID models is available at http://www.cogsys.cs.uni-tuebingen.de/downloads/Qualitative-Models/.

Parkinson's disease: dopaminergic nerve cell model is consistent with experimental finding of increased extracellular transport of α-synuclein.

BACKGROUND: Parkinson's disease is an age-related disease whose pathogenesis is not completely known. Animal models exist for investigating the disease but not all results can be easily transferred to humans. Therefore, mathematical or probabilistic models for the human disease are to be constructed in silico in order to predict specific processes within a cell, such as the dopamine metabolism and transport processes in a neuron. RESULTS: We present a Systems Biology Markup Language (SBML) model of a whole dopaminergic nerve cell consisting of 139 reactions and 111 metabolites which includes, among others, the dopamine metabolism and transport, oxidative stress, aggregation of α-synuclein (αSYN), lysosomal and proteasomal degradation, and mitophagy. The predictive power of the model was investigated using flux balance analysis for the identification of steady model states. To this end, we performed six experiments: (i) investigation of the normal cell behavior, (ii) increase of O2, (iii) increase of ATP, (iv) influence of neurotoxins, (v) increase of αSYN in the cell, and (vi) increase of dopamine synthesis. The SBML model is available in the BioModels database with identifier MODEL1302200000. CONCLUSION: It is possible to simulate the normal behavior of an in vivo nerve cell with the developed model. We show that the model is sensitive for neurotoxins and oxidative stress. Further, an increased level of αSYN induces apoptosis and an increased flux of αSYN to the extracellular space was observed.

Preanalytical aspects and sample quality assessment in metabolomics studies of human blood.

BACKGROUND: Metabolomics is a powerful tool that is increasingly used in clinical research. Although excellent sample quality is essential, it can easily be compromised by undetected preanalytical errors. We set out to identify critical preanalytical steps and biomarkers that reflect preanalytical inaccuracies. METHODS: We systematically investigated the effects of preanalytical variables (blood collection tubes, hemolysis, temperature and time before further processing, and number of freeze-thaw cycles) on metabolomics studies of clinical blood and plasma samples using a nontargeted LC-MS approach. RESULTS: Serum and heparinate blood collection tubes led to chemical noise in the mass spectra. Distinct, significant changes of 64 features in the EDTA-plasma metabolome were detected when blood was exposed to room temperature for 2, 4, 8, and 24 h. The resulting pattern was characterized by increases in hypoxanthine and sphingosine 1-phosphate (800% and 380%, respectively, at 2 h). In contrast, the plasma metabolome was stable for up to 4 h when EDTA blood samples were immediately placed in iced water. Hemolysis also caused numerous changes in the metabolic profile. Unexpectedly, up to 4 freeze-thaw cycles only slightly changed the EDTA-plasma metabolome, but increased the individual variability. CONCLUSIONS: Nontargeted metabolomics investigations led to the following recommendations for the preanalytical phase: test the blood collection tubes, avoid hemolysis, place whole blood immediately in ice water, use EDTA plasma, and preferably use nonrefrozen biobank samples. To exclude outliers due to preanalytical errors, inspect the biomarker signal intensities reflecting systematic as well as accidental and preanalytical inaccuracies before processing the bioinformatics data.

Targeting peptide termini, a novel immunoaffinity approach to reduce complexity in mass spectrometric protein identification.

Mass spectrometry and peptide-centric approaches are powerful techniques for the identification of differentially expressed proteins. Despite enormous improvements in MS technologies, sample preparation and efficient fractionation of target analytes are still major bottlenecks in MS-based protein analysis. The complexity of tryptically digested whole proteomes needs to be considerably reduced before low abundance proteins can be effectively analyzed using MS/MS. Sample preparation strategies that use peptide-specific antibodies are able to reduce the complexity of tryptic digests and lead to a substantial increase in throughput and sensitivity; however, the number of peptide-specific capture reagents is low, and consequently immunoaffinity-based approaches are only capable of detecting small sets of protein-derived peptides. In this proof-of-principle study, special anti-peptide antibodies were used to enrich peptides from a complex mixture. These antibodies recognize short amino acid sequences that are found directly at the termini of the peptides. The recognized epitopes consist of three or four amino acids only and include the terminally charged group of the peptide. Because of its limited length, antibodies recognizing the epitope will enrich not only one peptide but a whole class of peptides that share this terminal epitope. In this study, ß-catenin-derived peptides were used to demonstrate that it is possible (i) to effectively generate antibodies that recognize short C-terminal peptide epitopes and (ii) to enrich and identify peptide classes from a complex mixture using these antibodies in an immunoaffinity MS approach. The expected ß-catenin peptides and a set of 38 epitope-containing peptides were identified from trypsin-digested cell lysates. This might be a first step in the development of proteomics applications that are based on the use of peptide class-specific antibodies.

Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities.

The success of genome-wide association studies (GWAS) in deciphering the genetic architecture of complex diseases has fueled the expectations whether the individual risk can also be quantified based on the genetic architecture. So far, disease risk prediction based on top-validated single-nucleotide polymorphisms (SNPs) showed little predictive value. Here, we applied a support vector machine (SVM) to Parkinson disease (PD) and type 1 diabetes (T1D), to show that apart from magnitude of effect size of risk variants, heritability of the disease also plays an important role in disease risk prediction. Furthermore, we performed a simulation study to show the role of uncommon (frequency 1-5%) as well as rare variants (frequency <1%) in disease etiology of complex diseases. Using a cross-validation model, we were able to achieve predictions with an area under the receiver operating characteristic curve (AUC) of ~0.88 for T1D, highlighting the strong heritable component (∼90%). This is in contrast to PD, where we were unable to achieve a satisfactory prediction (AUC ~0.56; heritability ~38%). Our simulations showed that simultaneous inclusion of uncommon and rare variants in GWAS would eventually lead to feasible disease risk prediction for complex diseases such as PD. The used software is available at http://www.ra.cs.uni-tuebingen.de/software/MACLEAPS/.

KEGGtranslator: visualizing and converting the KEGG PATHWAY database to various formats.

SUMMARY: The KEGG PATHWAY database provides a widely used service for metabolic and nonmetabolic pathways. It contains manually drawn pathway maps with information about the genes, reactions and relations contained therein. To store these pathways, KEGG uses KGML, a proprietary XML-format. Parsers and translators are needed to process the pathway maps for usage in other applications and algorithms. We have developed KEGGtranslator, an easy-to-use stand-alone application that can visualize and convert KGML formatted XML-files into multiple output formats. Unlike other translators, KEGGtranslator supports a plethora of output formats, is able to augment the information in translated documents (e.g. MIRIAM annotations) beyond the scope of the KGML document, and amends missing components to fragmentary reactions within the pathway to allow simulations on those. AVAILABILITY: KEGGtranslator is freely available as a Java(™) Web Start application and for download at http://www.cogsys.cs.uni-tuebingen.de/software/KEGGtranslator/. KGML files can be downloaded from within the application. CONTACT: clemens.wrzodek@uni-tuebingen.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

JSBML: a flexible Java library for working with SBML.

SUMMARY: The specifications of the Systems Biology Markup Language (SBML) define standards for storing and exchanging computer models of biological processes in text files. In order to perform model simulations, graphical visualizations and other software manipulations, an in-memory representation of SBML is required. We developed JSBML for this purpose. In contrast to prior implementations of SBML APIs, JSBML has been designed from the ground up for the Java programming language, and can therefore be used on all platforms supported by a Java Runtime Environment. This offers important benefits for Java users, including the ability to distribute software as Java Web Start applications. JSBML supports all SBML Levels and Versions through Level 3 Version 1, and we have strived to maintain the highest possible degree of compatibility with the popular library libSBML. JSBML also supports modules that can facilitate the development of plugins for end user applications, as well as ease migration from a libSBML-based backend. AVAILABILITY: Source code, binaries and documentation for JSBML can be freely obtained under the terms of the LGPL 2.1 from the website http://sbml.org/Software/JSBML.