Information on adverse drug reactions-Proof of principle for a structured database that allows customization of drug information.

BACKGROUND: The drug information most commonly requested by patients is to learn more about potential adverse drug reactions (ADRs) of their drugs. Such information should be customizable to individual information needs. While approaches to automatically aggregate ADRs by text-mining processes and establishment of respective databases are well known, further efforts to map additional ADR information are sparse, yet crucial for customization. In a proof-of-principle (PoP) study, we developed a database format demonstrating that natural language processing can further structure ADR information in a way that facilitates customization. METHODS: We developed the database in a 3-step process: (1) initial ADR extraction, (2) mapping of additional ADR information, and (3) review process. ADRs of 10 frequently prescribed active ingredients were initially extracted from their Summary of Product Characteristics (SmPC) by text-mining processes and mapped to Medical Dictionary for Regulatory Activities (MedDRA) terms. To further structure ADR information, we mapped 7 additional ADR characteristics (i.e. frequency, organ class, seriousness, lay perceptibility, onset, duration, and management strategies) to individual ADRs. In a PoP study, the process steps were assessed and tested. Initial ADR extraction was assessed by measuring precision, recall, and F1-scores (i.e. harmonic mean of precision and recall). Mapping of additional ADR information was assessed considering pre-defined parameters (i.e. correctness, errors, and misses) regarding the mapped ADR characteristics. RESULTS: Overall the SmPCs listed 393 ADRs with an average of 39.3 ±â€¯18.1 ADRs per SmPC. For initial ADR extraction precision was 97.9% and recall was 93.2% leading to an F1-score of 95.5%. Regarding mapping of additional ADR information, the frequency information of 28.6 ±â€¯18.4 ADRs for each SmPC was correctly mapped (72.8%). Overall 77 ADRs (20.6%) of the correctly extracted ADRs did not have a concise frequency stated in the SmPC and were consequently mapped with 'frequency not known'. Mapping of remaining ADR characteristics did not result in noteworthy errors or misses. CONCLUSION: ADR information can be automatically extracted and mapped to corresponding MedDRA terms. Additionally, ADR information can be further structured considering additional ADR characteristics to facilitate customization to individual patient needs.

Improving drug safety with a systems pharmacology approach.

Systems pharmacology is used to mechanistically analyze drug-adverse drug reaction (ADRs) pairs and is a promising solution to the complex problem of understanding mechanisms of toxicity. In this research, we have explored the feasibility of retrospectively mapping population-level adverse events from the FDA Adverse Event Reporting System (FAERS) to chemical and biological databases to identify drug safety signals and the underlying molecular mechanisms. We used an analytic platform - Molecular Analysis of Side Effects (MASE™). For this purpose, we selected the adverse event of severe and potentially fatal cutaneous reactions (SCARs) that are associated with acetaminophen (APAP). SCARs encompass the continuum between Stevens-Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN). We found a statistically significant association between APAP and TEN, the most severe form of SCARs. We also explored the influence of APAP on other classes of drugs commonly associated with SCARs. We found that APAP significantly reduced the risk of SCARs commonly associated with carbamazepine (CBZ). We used molecular docking simulations to propose a mechanism for APAP's reduction in CBZ-induced SCARs which is competitive inhibition of the binding of CBZ to HLA-B*15:02. We conclude that systems pharmacology can complement established surveillance methodologies by providing a means to undertake an independent investigation and review of the mechanisms by which drugs cause adverse events.

Src activation by ß-adrenoreceptors is a key switch for tumour metastasis.

Noradrenaline can modulate multiple cellular functions important for cancer progression; however, how this single extracellular signal regulates such a broad array of cellular processes is unknown. Here we identify Src as a key regulator of phosphoproteomic signalling networks activated in response to beta-adrenergic signalling in cancer cells. These results also identify a new mechanism of Src phosphorylation that mediates beta-adrenergic/PKA regulation of downstream networks, thereby enhancing tumour cell migration, invasion and growth. In human ovarian cancer samples, high tumoural noradrenaline levels were correlated with high pSrc(Y419) levels. Moreover, among cancer patients, the use of beta blockers was significantly associated with reduced cancer-related mortality. Collectively, these data provide a pivotal molecular target for disrupting neural signalling in the tumour microenvironment.

Plasma microRNA profiles for bladder cancer detection.

BACKGROUND: Bladder cancer (BC) is a burdensome disease with significant morbidity, mortality, and cost. The development of novel plasma-based biomarkers for BC diagnosis and surveillance could significantly improve clinical outcomes and decrease health expenditures. Plasma miRNAs are promising biomarkers that have yet to be rigorously investigated in BC. OBJECTIVE: To determine the feasibility and efficacy of detecting BC with plasma miRNA signatures. MATERIALS AND METHODS: Plasma miRNA was isolated from 20 patients with bladder cancer and 18 noncancerous controls. Samples were analyzed with a miRNA array containing duplicate probes for each miRNA in the Sanger database. Logistic regression modeling was used to optimize diagnostic miRNA signatures to distinguish between muscle invasive BC (MIBC), non-muscle-invasive BC (NMIBC) and noncancerous controls. RESULTS: Seventy-nine differentially expressed plasma miRNAs (local false discovery rate [FDR] <0.5) in patients with or without BC were identified. Some diagnostically relevant miRNAs, such as miR-200b, were up-regulated in MIBC patients, whereas others, such as miR-92 and miR-33, were inversely correlated with advanced clinical stage, supporting the notion that miRNAs released in the circulation have a variety of cellular origins. Logistic regression modeling was able to predict diagnosis with 89% accuracy for detecting the presence or absence of BC, 92% accuracy for distinguishing invasive BC from other cases, 100% accuracy for distinguishing MIBC from controls, and 79% accuracy for three-way classification between MIBC, NIMBC, and controls. CONCLUSIONS: This study provides preliminary data supporting the use of plasma miRNAs as a noninvasive means of BC detection. Future studies will be required to further specify the optimal plasma miRNA signature, and to apply these signatures to clinical scenarios, such as initial BC detection and BC surveillance.

A scatter-based prototype framework and multi-class extension of support vector machines.

We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. This enables us to implement computationally efficient solvers based on sequential minimal and chunking optimization. As a further contribution, the primal problem formulation is developed in terms of regularized risk minimization and the hinge loss, revealing the score function to be used in the actual classification of test patterns. We investigate Scatter SVM properties related to generalization ability, computational efficiency, sparsity and sensitivity maps, and report promising results.

mGene: accurate SVM-based gene finding with an application to nematode genomes.

We present a highly accurate gene-prediction system for eukaryotic genomes, called mGene. It combines in an unprecedented manner the flexibility of generalized hidden Markov models (gHMMs) with the predictive power of modern machine learning methods, such as Support Vector Machines (SVMs). Its excellent performance was proved in an objective competition based on the genome of the nematode Caenorhabditis elegans. Considering the average of sensitivity and specificity, the developmental version of mGene exhibited the best prediction performance on nucleotide, exon, and transcript level for ab initio and multiple-genome gene-prediction tasks. The fully developed version shows superior performance in 10 out of 12 evaluation criteria compared with the other participating gene finders, including Fgenesh++ and Augustus. An in-depth analysis of mGene's genome-wide predictions revealed that approximately 2200 predicted genes were not contained in the current genome annotation. Testing a subset of 57 of these genes by RT-PCR and sequencing, we confirmed expression for 24 (42%) of them. mGene missed 300 annotated genes, out of which 205 were unconfirmed. RT-PCR testing of 24 of these genes resulted in a success rate of merely 8%. These findings suggest that even the gene catalog of a well-studied organism such as C. elegans can be substantially improved by mGene's predictions. We also provide gene predictions for the four nematodes C. briggsae, C. brenneri, C. japonica, and C. remanei. Comparing the resulting proteomes among these organisms and to the known protein universe, we identified many species-specific gene inventions. In a quality assessment of several available annotations for these genomes, we find that mGene's predictions are most accurate.

mGene.web: a web service for accurate computational gene finding.

We describe mGene.web, a web service for the genome-wide prediction of protein coding genes from eukaryotic DNA sequences. It offers pre-trained models for the recognition of gene structures including untranslated regions in an increasing number of organisms. With mGene.web, users have the additional possibility to train the system with their own data for other organisms on the push of a button, a functionality that will greatly accelerate the annotation of newly sequenced genomes. The system is built in a highly modular way, such that individual components of the framework, like the promoter prediction tool or the splice site predictor, can be used autonomously. The underlying gene finding system mGene is based on discriminative machine learning techniques and its high accuracy has been demonstrated in an international competition on nematode genomes. mGene.web is available at http://www.mgene.org/web, it is free of charge and can be used for eukaryotic genomes of small to moderate size (several hundred Mbp).

POIMs: positional oligomer importance matrices--understanding support vector machine-based signal detectors.

MOTIVATION: At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences. Frequently the most accurate classifiers are obtained by training support vector machines (SVMs) with complex sequence kernels. However, a cumbersome shortcoming of SVMs is that their learned decision rules are very hard to understand for humans and cannot easily be related to biological facts. RESULTS: To make SVM-based sequence classifiers more accessible and profitable, we introduce the concept of positional oligomer importance matrices (POIMs) and propose an efficient algorithm for their computation. In contrast to the raw SVM feature weighting, POIMs take the underlying correlation structure of k-mer features induced by overlaps of related k-mers into account. POIMs can be seen as a powerful generalization of sequence logos: they allow to capture and visualize sequence patterns that are relevant for the investigated biological phenomena. AVAILABILITY: All source code, datasets, tables and figures are available at http://www.fml.tuebingen.mpg.de/raetsch/projects/POIM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phenotyping of chondrocytes in vivo and in vitro using cDNA array technology.

The cDNA array technology is a powerful tool to analyze a high number of genes in parallel. We investigated whether large-scale gene expression analysis allows clustering and identification of cellular phenotypes of chondrocytes in different in vivo and in vitro conditions. In 100% of cases, clustering analysis distinguished between in vivo and in vitro samples, suggesting fundamental differences in chondrocytes in situ and in vitro regardless of the culture conditions or disease status. It also allowed us to differentiate between healthy and osteoarthritic cartilage. The clustering also revealed the relative importance of the investigated culturing conditions (stimulation agent, stimulation time, bead/monolayer). We augmented the cluster analysis with a statistical search for genes showing differential expression. The identified genes provided hints to the molecular basis of the differences between the sample classes. Our approach shows the power of modern bioinformatic algorithms for understanding and classifying chondrocytic phenotypes in vivo and in vitro. Although it does not generate new experimental data per se, it provides valuable information regarding the biology of chondrocytes and may provide tools for diagnosing and staging the osteoarthritic disease process.

Large-scale gene expression profiling reveals major pathogenetic pathways of cartilage degeneration in osteoarthritis.

OBJECTIVE: Despite many research efforts in recent decades, the major pathogenetic mechanisms of osteoarthritis (OA), including gene alterations occurring during OA cartilage degeneration, are poorly understood, and there is no disease-modifying treatment approach. The present study was therefore initiated in order to identify differentially expressed disease-related genes and potential therapeutic targets. METHODS: This investigation consisted of a large gene expression profiling study performed based on 78 normal and disease samples, using a custom-made complementary DNA array covering >4,000 genes. RESULTS: Many differentially expressed genes were identified, including the expected up-regulation of anabolic and catabolic matrix genes. In particular, the down-regulation of important oxidative defense genes, i.e., the genes for superoxide dismutases 2 and 3 and glutathione peroxidase 3, was prominent. This indicates that continuous oxidative stress to the cells and the matrix is one major underlying pathogenetic mechanism in OA. Also, genes that are involved in the phenotypic stability of cells, a feature that is greatly reduced in OA cartilage, appeared to be suppressed. CONCLUSION: Our findings provide a reference data set on gene alterations in OA cartilage and, importantly, indicate major mechanisms underlying central cell biologic alterations that occur during the OA disease process. These results identify molecular targets that can be further investigated in the search for therapeutic interventions.

ARTS: accurate recognition of transcription starts in human.

UNLABELLED: We develop new methods for finding transcription start sites (TSS) of RNA Polymerase II binding genes in genomic DNA sequences. Employing Support Vector Machines with advanced sequence kernels, we achieve drastically higher prediction accuracies than state-of-the-art methods. MOTIVATION: One of the most important features of genomic DNA are the protein-coding genes. While it is of great value to identify those genes and the encoded proteins, it is also crucial to understand how their transcription is regulated. To this end one has to identify the corresponding promoters and the contained transcription factor binding sites. TSS finders can be used to locate potential promoters. They may also be used in combination with other signal and content detectors to resolve entire gene structures. RESULTS: We have developed a novel kernel based method - called ARTS - that accurately recognizes transcription start sites in human. The application of otherwise too computationally expensive Support Vector Machines was made possible due to the use of efficient training and evaluation techniques using suffix tries. In a carefully designed experimental study, we compare our TSS finder to state-of-the-art methods from the literature: McPromoter, Eponine and FirstEF. For given false positive rates within a reasonable range, we consistently achieve considerably higher true positive rates. For instance, ARTS finds about 35% true positives at a false positive rate of 1/1000, where the other methods find about a half (18%). AVAILABILITY: Datasets, model selection results, whole genome predictions, and additional experimental results are available at http://www.fml.tuebingen.mpg.de/raetsch/projects/arts.

Gene expression profiling of serum- and interleukin-1 beta-stimulated primary human adult articular chondrocytes--a molecular analysis based on chondrocytes isolated from one donor.

In order to understand the cellular disease mechanisms of osteoarthritic cartilage degeneration it is of primary importance to understand both the anabolic and the catabolic processes going on in parallel in the diseased tissue. In this study, we have applied cDNA-array technology (Clontech) to study gene expression patterns of primary human normal adult articular chondrocytes isolated from one donor cultured under anabolic (serum) and catabolic (IL-1beta) conditions. Significant differences between the different in vitro cultures tested were detected. Overall, serum and IL-1beta significantly altered gene expression levels of 102 and 79 genes, respectively. IL-1beta stimulated the matrix metalloproteinases-1, -3, and -13 as well as members of its intracellular signaling cascade, whereas serum increased the expression of many cartilage matrix genes. Comparative gene expression analysis with previously published in vivo data (normal and osteoarthritic cartilage) showed significant differences of all in vitro stimulations compared to the changes detected in osteoarthritic cartilage in vivo. This investigation allowed us to characterize gene expression profiles of two classical anabolic and catabolic stimuli of human adult articular chondrocytes in vitro. No in vitro model appeared to be adequate to study overall gene expression alterations in osteoarthritic cartilage. Serum stimulated in vitro cultures largely reflected the results that were only consistent with the anabolic activation seen in osteoarthritic chondrocytes. In contrast, IL-1beta did not appear to be a good model for mimicking catabolic gene alterations in degenerating chondrocytes.

Phenotypic characterization of human chondrocyte cell line C-20/A4: a comparison between monolayer and alginate suspension culture.

DNA microarray analysis was used to investigate the molecular phenotype of one of the first human chondrocyte cell lines, C-20/A4, derived from juvenile costal chondrocytes by immortalization with origin-defective simian virus 40 large T antigen. Clontech Human Cancer Arrays 1.2 and quantitative PCR were used to examine gene expression profiles of C-20/A4 cells cultured in the presence of serum in monolayer and alginate beads. In monolayer cultures, genes involved in cell proliferation were strongly upregulated compared to those expressed by human adult articular chondrocytes in primary culture. Of the cell cycle-regulated genes, only two, the CDK regulatory subunit and histone H4, were downregulated after culture in alginate beads, consistent with the ability of these cells to proliferate in suspension culture. In contrast, the expression of several genes that are involved in pericellular matrix formation, including MMP-14, COL6A1, fibronectin, biglycan and decorin, was upregulated when the C-20/A4 cells were transferred to suspension culture in alginate. Also, nexin-1, vimentin, and IGFBP-3, which are known to be expressed by primary chondrocytes, were differentially expressed in our study. Consistent with the proliferative phenotype of this cell line, few genes involved in matrix synthesis and turnover were highly expressed in the presence of serum. These results indicate that immortalized chondrocyte cell lines, rather than substituting for primary chondrocytes, may serve as models for extending findings on chondrocyte function not achievable by the use of primary chondrocytes.

Analysis of differential gene expression in healthy and osteoarthritic cartilage and isolated chondrocytes by microarray analysis.

The regulation of chondrocytes in osteoarthritic cartilage and the expression of specific gene products by these cells during early-onset and late-stage osteoarthritis are not well characterized. With the introduction of cDNA array technology, the measurement of thousands of different genes in one small tissue sample can be carried out. Interpretation of gene expression analyses in articular cartilage is aided by the fact that this tissue contains only one cell type in both normal and diseased conditions. However, care has to be taken not to over- and misinterpret results, and some major challenges must be overcome in order to utilize the potential of this technology properly in the field of osteoarthritis.

Correlated stage- and subfield-associated hippocampal gene expression patterns in experimental and human temporal lobe epilepsy.

Epileptic activity evokes profound alterations of hippocampal organization and function. Genomic responses may reflect immediate consequences of excitatory stimulation as well as sustained molecular processes related to neuronal plasticity and structural remodeling. Using oligonucleotide microarrays with 8799 sequences, we determined subregional gene expression profiles in rats subjected to pilocarpine-induced epilepsy (U34A arrays, Affymetrix, Santa Clara, CA, USA; P < 0.05, twofold change, n = 3 per stage). Patterns of gene expression corresponded to distinct stages of epilepsy development. The highest number of differentially expressed genes (dentate gyrus, approx. 400 genes and CA1, approx. 700 genes) was observed 3 days after status epilepticus. The majority of up-regulated genes was associated with mechanisms of cellular stress and injury - 14 days after status epilepticus, numerous transcription factors and genes linked to cytoskeletal and synaptic reorganization were differentially expressed and, in the stage of chronic spontaneous seizures, distinct changes were observed in the transcription of genes involved in various neurotransmission pathways and between animals with low vs. high seizure frequency. A number of genes (n = 18) differentially expressed during the chronic epileptic stage showed corresponding expression patterns in hippocampal subfields of patients with pharmacoresistant temporal lobe epilepsy (n = 5 temporal lobe epilepsy patients; U133A microarrays, Affymetrix; covering 22284 human sequences). These data provide novel insights into the molecular mechanisms of epileptogenesis and seizure-associated cellular and structural remodeling of the hippocampus.

Microarrays: how many do you need?

We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. We show that current knowledge allows for the construction of models that look realistic with respect to searches for individual differentially expressed genes and derive prototypical parameters from real data sets. Such models allow investigation of the dependence of the required number of samples on the relevant parameters: the biological variability of the samples within each class, the fold changes in expression that are desired to be detected, the detection sensitivity of the microarrays, and the acceptable error rates of the results. We supply experimentalists with general conclusions as well as a freely accessible Java applet at www.scai.fhg.de/special/bio/howmanyarrays/ for fine tuning simulations to their particular settings.

Gene expression in chondrocytes assessed with use of microarrays.

BACKGROUND: Despite considerable limitations such as low sensitivity and insensitivity to alternative splicing, posttranscriptional regulation, and posttranslational modification, cDNA array technology provides a powerful tool with which to obtain an overview of gene expression patterns, hardly achievable with other techniques. This has been shown to be true for the analysis of known genes as well as the discovery of new genes of interest. METHODS: Samples of normal and late-stage osteoarthritic cartilage of human knee joints were analyzed with use of the Human Cancer 1.2 cDNA-array and TaqMan analysis. RESULTS: In spite of a large variability of expression levels among different patients, significant expression patterns for many known genes of interest such as cartilage matrix proteins (e.g., collagen types II, VI, and XI; aggrecan; decorin; biglycan) and matrix-degrading proteases were detected. Of the latter, MMP-3 appeared to be strongly expressed in normal and early degenerative cartilage and downregulated in the late disease stages. This indicates that, in the late stages of cartilage degeneration, other degradation pathways might be more important, for example, those involving enzymes such as MMP-2 and MMP-13, both of which were upregulated in late-stage disease. CONCLUSION: Most results have to be considered to be preliminary to a certain degree, as technical tools and interpretation approaches are still emerging and need more validation. Clearly, there is a major challenge to distill information and knowledge out of the obtained mass of data. However, these data will be one basis of a new world of biological understanding. These new insights will be network-based and no longer molecule-centered. Today, molecules have a biochemical and physiological context; tomorrow, biological networks will have molecules as constituents.

Functional genomics of osteoarthritis.

Functional genomics is a challenging new way to address a complex disease like osteoarthritis on a molecular level. Despite osteoarthritis being ultimately a biochemical problem, mainly characterized by an imbalanced cartilage matrix turnover, a deeper understanding of molecular events within the tissue cells (i.e., the chondrocytes) will provide not only a better understanding of pathogenetic mechanisms but also new diagnostic markers and cellular targets for therapeutic intervention. This innovative technology represents a challenging approach complementing (not replacing) classical research in previously described and new disease-relevant genes: large-scale functional genomics will open up new areas of so far unrecognized molecular networks. This will include as yet unidentified players in the anabolic-catabolic balance of matrix turnover of articular cartilage as well as disease-relevant intracellular signaling cascades so far hardly investigated in articular chondrocytes. However, care must be taken not to over or misinterpret results and some major challenges must be overcome in order to properly utilize the potential of this technology in the field of osteoarthritis.

Co-clustering of biological networks and gene expression data.

MOTIVATION: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. RESULTS: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.

Confidence measures for protein fold recognition.

MOTIVATION: We present an extensive evaluation of different methods and criteria to detect remote homologs of a given protein sequence. We investigate two associated problems: first, to develop a sensitive searching method to identify possible candidates and, second, to assign a confidence to the putative candidates in order to select the best one. For searching methods where the score distributions are known, p-values are used as confidence measure with great success. For the cases where such theoretical backing is absent, we propose empirical approximations to p-values for searching procedures. RESULTS: As a baseline, we review the performances of different methods for detecting remote protein folds (sequence alignment and threading, with and without sequence profiles, global and local). The analysis is performed on a large representative set of protein structures. For fold recognition, we find that methods using sequence profiles generally perform better than methods using plain sequences, and that threading methods perform better than sequence alignment methods. In order to assess the quality of the predictions made, we establish and compare several confidence measures, including raw scores, z-scores, raw score gaps, z-score gaps, and different methods of p-value estimation. We work our way from the theoretically well backed local scores towards more explorative global and threading scores. The methods for assessing the statistical significance of predictions are compared using specificity--sensitivity plots. For local alignment techniques we find that p-value methods work best, albeit computationally cheaper methods such as those based on score gaps achieve similar performance. For global methods where no theory is available methods based on score gaps work best. By using the score gap functions as the measure of confidence we improve the more powerful fold recognition methods for which p-values are unavailable. AVAILABILITY: The benchmark set is available upon request.