The receptor for advanced glycation end-products (RAGE) is a single-transmembrane, multiligand receptor of the immunoglobulin superfamily. RAGE up-regulation is implicated in numerous pathological states including vascular disease, diabetes, cancer, and neurodegeneration. The understanding of the regulation of RAGE is important in both disease pathogenesis and normal homeostasis. Here, we demonstrate the characterization and identification of human RAGE splice variants by analysis of RAGE cDNA from tissue and cells. We identified a vast range of splice forms that lead to changes in the protein coding region of RAGE, which we have classified according to the Human Gene Nomenclature Committee (HGNC). These resulted in protein changes in the ligand-binding domain of RAGE or the removal of the transmembrane domain and cytosolic tail. Analysis of splice variants for premature termination codons reveals approximately 50% of identified variants are targeted to the nonsense-mediated mRNA decay pathway. Expression analysis revealed the RAGE_v1 variant to be the primary secreted soluble isoform of RAGE. Taken together, identification of functional splice variants of RAGE underscores the biological diversity of the RAGE gene and will aid in the understanding of the gene in the normal and pathological state.

Summary Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest—namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial—ENTHUSE M1—in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39–4·62, p<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified...

The alternative splicing of pre-mRNAs is a critical mechanism in genomic complexity, disease, and development. Studies of the receptor for advanced glycation end-products (RAGE) indicate that this gene undergoes a variety of splice events in humans. However, no studies have extensively analyzed the tissue distribution in other species or compared evolutionary differences of RAGE isoforms. Because the majority of studies probing RAGE function have been performed in murine models, we therefore performed studies to identify and characterize the splice variants of the murine RAGE gene, and we compared these to human isoforms. Here, using mouse tissues, we identified numerous splice variants including changes in the extracellular domain or the removal of the transmembrane and cytoplasmic domains, which produce soluble splice isoforms. Comparison of splice variants between humans and mice revealed homologous regions in the RAGE gene that undergo splicing as well as key species-specific mechanisms of splicing. Further analysis of tissue splice variant distribution in mice revealed major differences between lung, kidney, heart, and brain. To probe the potential impact of disease-like pathological states, we studied diabetic mice and report that RAGE splice variation changed dramatically, resulting in an increase in production of soluble RAGE (sRAGE) splice variants, which were not associated with detectable levels of sRAGE in murine plasma. In conclusion, we have determined that the murine RAGE gene undergoes extensive splicing with distinct splice isoforms being uniquely distributed in different tissues. These differences in RAGE splicing in both physiological and pathogenic states further expand our understanding of the biological repertoire of this receptor in health and disease.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer’s Disease. The Alzheimer’s Disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer’s Disease based on high-dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for to prediction of cognitive performance.

Introduction. African American breast cancer survivors engage in less physical activity compared with their Caucasian counterparts. There is a need for exercise intervention research that focuses on improving the overall health and longterm survivorship of African American breast cancer survivors, especially because they often have worse outcomes than Caucasian survivors. Study objectives were to determine whether African American participants increase physical activity and explore whether exercise had a positive impact on fitness and health. Methods. African American breast cancer survivors, stage 0 to IIIA, within 2 years of completing primary cancer treatments were recruited for a 16-week home-based aerobic and resistance training exercise pilot study. Outcome measures assessed at baseline and postintervention included physical activity questionnaires and accelerometry, cardiopulmonary function (VO 2peak ) with gas exchange, muscle strength, Selective Functional Movement Assessment, and dual energy X-ray absorptiometry scans for body composition analysis. Assessments for fatigue and quality of life (QOL) were also completed at baseline and postintervention. Motivational interviewing was utilized to determine goals and explore exercise facilitators/barriers. Participants completed weekly exercise logs and received weekly phone calls. Wilcoxon signed rank tests were used to detect significant changes in physical activity and also changes in fitness/health parameters, fatigue, and QOL. Spearman correlation coefficients were used to examine relationships between physical activity and health measures. Results. A total of 17 women enrolled; 13 completed the intervention (76%). Mean age of the participants was 51 years. There was a significant increase in total minutes of weekly physical activity postintervention (M = 271 minutes, SD = 151; P = .001). Significant improvements were found in cardiopulmonary fitness as measured by VO 2peak with a mean increase of 2.03 mL/kg/min (P = .01). Several strength measures significantly increased and also functional movement (P = .005). Positive correlations existed between physical activity and several physical measures, with significant relationships between functional movement and some strength measures (eg, left arm extension: r s = 0.61, P = .002). Total QOL and fatigue scores improved, but neither was significant. Conclusions. The intervention led to increased physical activity. As a result of increased levels of physical activity, improvements on several fitness/health parameters occurred.