High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.

Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles.

Compounds that are candidates for drug repurposing can be ranked by leveraging knowledge available in the biomedical literature and databases. This knowledge, spread across a variety of sources, can be integrated within a knowledge graph, which thereby comprehensively describes known relationships between biomedical concepts, such as drugs, diseases, genes, etc. Our work uses the semantic information between drug and disease concepts as features, which are extracted from an existing knowledge graph that integrates 200 different biological knowledge sources. RepoDB, a standard drug repurposing database which describes drug-disease combinations that were approved or that failed in clinical trials, is used to train a random forest classifier. The 10-times repeated 10-fold cross-validation performance of the classifier achieves a mean area under the receiver operating characteristic curve (AUC) of 92.2%. We apply the classifier to prioritize 21 preclinical drug repurposing candidates that have been suggested for Autosomal Dominant Polycystic Kidney Disease (ADPKD). Mozavaptan, a vasopressin V2 receptor antagonist is predicted to be the drug most likely to be approved after a clinical trial, and belongs to the same drug class as tolvaptan, the only treatment for ADPKD that is currently approved. We conclude that semantic properties of concepts in a knowledge graph can be exploited to prioritize drug repurposing candidates for testing in clinical trials.

Medication for nonalcoholic fatty liver disease (NAFLD) is an unmet need. Glucocorticoid (GC) stress hormones drive fat metabolism in the liver, but both full blockade and full stimulation of GC signaling aggravate NAFLD pathology. We investigated the efficacy of selective glucocorticoid receptor (GR) modulator CORT118335, which recapitulates only a subset of GC actions, in reducing liver lipid accumulation in mice. Male C57BL/6J mice received a low-fat diet or high-fat diet mixed with vehicle or CORT118335. Livers were analyzed histologically and for genome-wide mRNA expression. Functionally, hepatic long-chain fatty acid (LCFA) composition was determined by gas chromatography. We determined very-low-density lipoprotein (VLDL) production by treatment with a lipoprotein lipase inhibitor after which blood was collected to isolate radiolabeled VLDL particles and apoB proteins. CORT118335 strongly prevented and reversed hepatic lipid accumulation. Liver transcriptome analysis showed increased expression of GR target genes involved in VLDL production. Accordingly, CORT118335 led to increased lipidation of VLDL particles, mimicking physiological GC action. Independent pathway analysis revealed that CORT118335 lacked induction of GC-responsive genes involved in cholesterol synthesis and LCFA uptake, which was indeed reflected in unaltered hepatic LCFA uptake in vivo. Our data thus reveal that the robust hepatic lipid-lowering effect of CORT118335 is due to a unique combination of GR-dependent stimulation of lipid (VLDL) efflux from the liver, with a lack of stimulation of GR-dependent hepatic fatty acid uptake. Our findings firmly demonstrate the potential use of CORT118335 in the treatment of NAFLD and underscore the potential of selective GR modulation in metabolic disease.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is one of the most common causes of end-stage renal failure, caused by mutations in PKD1 or PKD2 genes. Tolvaptan, the only drug approved for ADPKD treatment, results in serious side-effects, warranting the need for novel drugs.

One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows.

The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals.

Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hospital-independent solution to indicate points for quality improvement.

We previously used a chemical genetics approach with the larval zebrafish to identify small molecule inhibitors of tissue regeneration. This led to the discovery that glucocorticoids (GC) block early stages of tissue regeneration by the inappropriate activation of the glucocorticoid receptor (GR). We performed a microarray analysis to identify the changes in gene expression associated with beclomethasone dipropionate (BDP) exposure during epimorphic fin regeneration. Oncofetal cripto-1 showed > eight-fold increased expression in BDP-treated regenerates. We hypothesized that the mis-expression of cripto-1 was essential for BDP to block regeneration. Expression of cripto-1 was not elevated in GR morphants in the presence of BDP indicating that cripto-1 induction was GR-dependent. Partial translational suppression of Cripto-1 in the presence of BDP restored tissue regeneration. Retinoic acid exposure prevented increased cripto-1 expression and permitted regeneration in the presence of BDP. We demonstrated that BDP exposure increased cripto-1 expression in mouse embryonic stem cells and that regulation of cripto-1 by GCs is conserved in mammals.

Metabolic changes have been frequently associated with Huntington's disease (HD). At the same time peripheral blood represents a minimally invasive sampling avenue with little distress to Huntington's disease patients especially when brain or other tissue samples are difficult to collect.

We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents.

Spinocerebellar ataxia type 3 (SCA3) is a progressive neurodegenerative disorder caused by expansion of the polyglutamine repeat in the ataxin-3 protein. Expression of mutant ataxin-3 is known to result in transcriptional dysregulation, which can contribute to the cellular toxicity and neurodegeneration. Since the exact causative mechanisms underlying this process have not been fully elucidated, gene expression analyses in brains of transgenic SCA3 mouse models may provide useful insights.

It was investigated whether positive immunomagnetic selection with two novel DC-specific mAb allowed purification of functional myeloid dendritic cells (MDC) and plasmacytoid dendritic cells (PDC) from the human lymph nodes (LN). The results were compared with enrichment of DC by low-density Nycodenz gradient centrifugation followed by immunomagnetic depletion of residual B- and T-cells (Nycodenz method). MDC were selected from inguinal LN cell suspensions using CD1c mAb and PDC using anti-BDCA-4 mAb. Immunomagnetic selection with anti-CD1c mAb yielded highly pure MDC preparations (90 +/- 3% MDC; n = 7), provided that the B-cells were thoroughly depleted by using CD19 magnetic beads and Large Depletion (LD) columns prior to selection of MDC. The purified MDC comprised both mature and immature cells and were functional, secreting large amounts of cytokines upon stimulation and strongly stimulating allogeneic T-cell proliferation. Immunomagnetic selection with anti-BDCA-4 mAb enriched PDC 70-fold to a purity of 59 +/- 26% (n = 8). The contamination consisted mainly of BDCA-4(+) T- and NK-cells. The previously used Nycodenz method yielded mixtures of MDC and PDC, not allowing functional studies of MDC and PDC separately. In conclusion, positive immunomagnetic selection with CD1c mAb from human LN cell suspensions yields almost pure MDC preparations, which are, in contrast to those obtained by the Nycodenz method, not contaminated with PDC. Moreover, these MDC are functionally intact. Selection with anti-BDCA-4 mAb does enrich PDC from human LN, but the resulting preparations are contaminated with T- and NK-cells.

Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships.

Hereditary cerebral hemorrhage with amyloidosis-Dutch type (HCHWA-D) is an early onset hereditary form of cerebral amyloid angiopathy (CAA) caused by a point mutation resulting in an amino acid change (NP_000475.1:p.Glu693Gln) in the amyloid precursor protein (APP). Post-mortem frontal and occipital cortical brain tissue from nine patients and nine age-related controls was used for RNA sequencing to identify biological pathways affected in HCHWA-D. Although previous studies indicated that pathology is more severe in the occipital lobe in HCHWA-D compared to the frontal lobe, the current study showed similar changes in gene expression in frontal and occipital cortex and the two brain regions were pooled for further analysis. Significantly altered pathways were analyzed using gene set enrichment analysis (GSEA) on 2036 significantly differentially expressed genes. Main pathways over-represented by down-regulated genes were related to cellular aerobic respiration (including ATP synthesis and carbon metabolism) indicating a mitochondrial dysfunction. Principal up-regulated pathways were extracellular matrix (ECM)-receptor interaction and ECM proteoglycans in relation with an increase in the transforming growth factor beta (TGFβ) signaling pathway. Comparison with the publicly available dataset from pre-symptomatic APP-E693Q transgenic mice identified overlap for the ECM-receptor interaction pathway, indicating that ECM modification is an early disease specific pathomechanism.