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

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.

Assessing the metabolic effects of prednisolone in healthy volunteers using urine metabolic profiling.

BACKGROUND: Glucocorticoids, such as prednisolone, are widely used anti-inflammatory drugs, but therapy is hampered by a broad range of metabolic side effects including skeletal muscle wasting and insulin resistance. Therefore, development of improved synthetic glucocorticoids that display similar efficacy as prednisolone but reduced side effects is an active research area. For efficient development of such new drugs, in vivo biomarkers, which can predict glucocorticoid metabolic side effects in an early stage, are needed. In this study, we aim to provide the first description of the metabolic perturbations induced by acute and therapeutic treatments with prednisolone in humans using urine metabolomics, and to derive potential biomarkers for prednisolone-induced metabolic effects. METHODS: A randomized, double blind, placebo-controlled trial consisting of two protocols was conducted in healthy men. In protocol 1, volunteers received placebo (n = 11) or prednisolone (7.5 mg (n = 11), 15 mg (n = 13) or 30 mg (n = 12)) orally once daily for 15 days. In protocol 2, volunteers (n = 6) received placebo at day 0 and 75 mg prednisolone at day 1. We collected 24 h urine and serum samples at baseline (day 0), after a single dose (day 1) and after prolonged treatment (day 15) and obtained mass-spectrometry-based urine and serum metabolic profiles. RESULTS: At day 1, high-dose prednisolone treatment increased levels of 13 and 10 proteinogenic amino acids in urine and serum respectively, as well as levels of 3-methylhistidine, providing evidence for an early manifestation of glucocorticoid-induced muscle wasting. Prednisolone treatment also strongly increased urinary carnitine derivatives at day 1 but not at day 15, which might reflect adaptive mechanisms under prolonged treatment. Finally, urinary levels of proteinogenic amino acids at day 1 and of N-methylnicotinamide at day 15 significantly correlated with the homeostatic model assessment of insulin resistance and might represent biomarkers for prednisolone-induced insulin resistance. CONCLUSION: This study provides evidence that urinary metabolomics represents a noninvasive way of monitoring the effect of glucocorticoids on muscle protein catabolism after a single dose and can derive new biomarkers of glucocorticoid-induced insulin resistance. It might, therefore, help the development of improved synthetic glucocorticoids. TRIAL REGISTRATION: ClinicalTrials.gov NCT00971724.

Identification of CXCL13 as a marker for rheumatoid arthritis outcome using an in silico model of the rheumatic joint.

OBJECTIVE: Rheumatoid arthritis (RA) is characterized by inflammation and joint destruction, with the degree of damage varying greatly among patients. Prediction of disease severity using known clinical and serologic risk factors is inaccurate. This study was undertaken to identify new serologic markers for RA severity using an in silico model of the rheumatic joint. METHODS: An in silico model of a prototypical rheumatic joint was used to predict candidate markers associated with erosiveness. The following 4 markers were chosen for validation: tartrate-resistant acid phosphatase 5b (TRAP-5b), N-telopeptide of type I collagen (NTX), angiopoietin 2 (Ang-2), and CXCL13. Serum from 74 RA patients was used to study whether radiologic joint destruction (total erosion score and total Sharp/van der Heijde score [SHS]) after 4 years of disease was associated with serum levels at the time of diagnosis. Serum marker levels were determined using enzyme-linked immunosorbent assays. For confirmation, baseline serum levels were analyzed for an association with progression of joint damage over 7 years of followup in a cohort of 155 patients with early RA. RESULTS: Comparison of high and low quartiles of erosion score and SHS at 4 years showed a difference in baseline serum CXCL13 level (P = 0.011 and P = 0.018, respectively). In the confirmation cohort, elevated baseline CXCL13 levels were associated with increased rates of joint destruction during 7 years of followup (P < 0.001 unadjusted and P ≤ 0.004 with adjustment for C-reactive protein level). Analyzing anti-CCP-2-positive and anti-CCP-2­negative RA separately yielded a significant result only in the anti-CCP-2-negative group (P ≤ 0.001). CONCLUSION: Our findings indicate that CXCL13 is a novel serologic marker predictive of RA severity.This marker was identified with the help of an in silicomodel of the RA joint.

Target validation in silico: does the virtual patient cure the pharma pipeline?

Genomics has multiplied the number of targets for new therapeutic interventions, but this has not yet lead to a marked increase of pharma pipeline outputs. The complexity of protein function in higher order biological systems is often underestimated. Translation from in vitro and in vivo results to the human setting frequently fails due to unforeseen toxicity and efficacy issues. Biosimulation addresses these issues by capturing the complex dynamics of interacting molecules and cells in mechanistic, predictive models. A central concept is that of the virtual patient, an encapsulation of a specific pathophysiological behaviour in a biosimulation model. The authors describe how virtual patients are being used in target identification, target validation and clinical development, and discuss challenges for the acceptance of biosimulation methods.

CoPub Mapper: mining MEDLINE based on search term co-publication.

BACKGROUND: High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to identify biological similarities between genes, publications from MEDLINE were identified in which pairs of gene names and combinations of gene name with specific keywords were co-mentioned. RESULTS: MEDLINE search strings for 15,621 known genes and 3,731 keywords were generated and validated. PubMed IDs were retrieved from MEDLINE and relative probability of co-occurrences of all gene-gene and gene-keyword pairs determined. To assess gene clustering according to literature co-publication, 150 genes consisting of 8 sets with known connections (same pathway, same protein complex, or same cellular localization, etc.) were run through the program. Receiver operator characteristics (ROC) analyses showed that most gene sets were clustered much better than expected by random chance. To test grouping of genes from real microarray data, 221 differentially expressed genes from a microarray experiment were analyzed with CoPub Mapper, which resulted in several relevant clusters of genes with biological process and disease keywords. In addition, all genes versus keywords were hierarchical clustered to reveal a complete grouping of published genes based on co-occurrence. CONCLUSION: The CoPub Mapper program allows for quick and versatile querying of co-published genes and keywords and can be successfully used to cluster predefined groups of genes and microarray data.