Dataset Information

Identification of new biomarker candidates for glucocorticoid induced insulin resistance using literature mining.

ABSTRACT:

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Background

Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic side-effects, e.g. insulin resistance, glucose intolerance and diabetes. To gain more insight into the mechanisms behind glucocorticoid induced insulin resistance, it is important to understand which genes play a role in the development of insulin resistance and which genes are affected by glucocorticoids.Medline abstracts contain many studies about insulin resistance and the molecular effects of glucocorticoids and thus are a good resource to study these effects.

Results

We developed CoPubGene a method to automatically identify gene-disease associations in Medline abstracts. We used this method to create a literature network of genes related to insulin resistance and to evaluate the importance of the genes in this network for glucocorticoid induced metabolic side effects and anti-inflammatory processes.With this approach we found several genes that already are considered markers of GC induced IR, such as phosphoenolpyruvate carboxykinase (PCK) and glucose-6-phosphatase, catalytic subunit (G6PC). In addition, we found genes involved in steroid synthesis that have not yet been recognized as mediators of GC induced IR.

Conclusions

With this approach we are able to construct a robust informative literature network of insulin resistance related genes that gave new insights to better understand the mechanisms behind GC induced IR. The method has been set up in a generic way so it can be applied to a wide variety of disease networks.

SUBMITTER: Fleuren WW

PROVIDER: S-EPMC3577498 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Publications

Identification of new biomarker candidates for glucocorticoid induced insulin resistance using literature mining.

Fleuren Wilco Wm WW Toonen Erik Jm EJ Verhoeven Stefan S Frijters Raoul R Hulsen Tim T Rullmann Ton T van Schaik René R de Vlieg Jacob J Alkema Wynand W

BioData mining 20130204 1

<h4>Unlabelled</h4><h4>Background</h4>Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic side-effects, e.g. insulin resistance, glucose intolerance and diabetes. To gain more insight into the mechanisms behind glucocorticoid induced insulin resistance, it is important to understand which genes play a role in the development of in ...[more]

PMID: 23379763

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Project description:BackgroundFor selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives.MethodsA biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases.ResultsThe current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms) in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods) is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http://www.scaiview.com/scaiview-academia.html.ConclusionsThe approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.

Project description:Polycystic ovarian syndrome (PCOS) is characterized by hyperandrogenemia, polycystic ovaries, and menstrual disturbance and a clear association with insulin resistance. This research evaluated whether induction of insulin resistance, using dexamethasone (DEX), in a monovular animal model, the cow, could produce an ovarian phenotype similar to PCOS. In all of these experiments, DEX induced insulin resistance in cows as shown by increased glucose, insulin, and HOMA-IR (homeostasis model assessment of insulin resistance). Experiment 1: DEX induced anovulation (zero of five DEX vs. four of four control cows ovulated) and decreased circulating estradiol (E2). Experiment 2: Gonadotropin-releasing hormone (GnRH) was administered to determine pituitary and follicular responses during insulin resistance. GnRH induced a luteinizing hormone (LH) surge and ovulation in both DEX (seven of seven) and control (seven of seven) cows. Experiment 3: E2 was administered to determine hypothalamic responsiveness after induction of an E2 surge in DEX (eight of eight) and control (eight of eight) cows. An LH surge was induced in control (eight of eight) but not DEX (zero of eight) cows. All control (eight of eight) but only two of eight DEX cows ovulated within 60 h of E2 administration. Experiment 4: Short-term DEX was initiated 24 h after induced luteal regression to determine if DEX could acutely block ovulation before peak insulin resistance was induced, similar to progesterone (P4). All control (five of five), no P4-treated (zero of six), and 50% of DEX-treated (three of six) cows ovulated by 96 h after luteal regression. All anovular cows had reduced circulating E2. These data are consistent with DEX creating a lesion in hypothalamic positive feedback to E2 without altering pituitary responsiveness to GnRH or ovulatory responsiveness of follicles to LH. It remains to be determined if the considerable insulin resistance and the reduced follicular E2 production induced by DEX had any physiological importance in the induction of anovulation.

Dataset Information

Identification of new biomarker candidates for glucocorticoid induced insulin resistance using literature mining.

Unlabelled

Background

Results

Conclusions

Publications

Identification of new biomarker candidates for glucocorticoid induced insulin resistance using literature mining.

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