Magic™ Anti-APOA2 polyclonal antibody

We have previously reported that radix Angelica sinensis (RAS) suppressed body weight and altered the expression of the fat mass and obesity associated (FTO) gene in mice with high fat diet (HFD)-induced obesity. In the present study we performed RNA sequencing-mediated transcriptome analysis to elucidate the molecular mechanisms underlying the anti-obesogenic effects of RAS in mice. The results revealed that 36 differentially expressed genes (DEGs) were identified in adipose tissues from the RAS supplementation group (DH) and control group (HC). These 36 DEGs were clustered into 297 functional gene ontology (GO) categories, among which several GO annotations and signaling pathways were associated with lipid homeostasis. Six out of the 36 DEGs were identified to be involved in lipid metabolism, with the APOA2 gene a potential anti-obesogenic influence. The expression pattern revealed by RNA-Seq was identical to the results of quantitative real-time PCR (qPCR). Therefore, RAS supplementation in HFD-induced obese mice was associated with an anti-obesogenic global transcriptomic response. This study provides insight into potential applications of RAS in obesity therapy. (C) 2016 Elsevier B.V. All rights reserved.

Background: In the present study, we explored potential protein biomarkers useful to predict the therapeutic response of knee osteoarthritis (KOA) patients treated with pharmaceutical grade Chondroitin sulfate/Glucosamine hydrochloride (CS+GH; Droglican, Bioiberica), in order to optimize therapeutic outcomes. Methods: A shotgun proteomic analysis by iTRAQ labelling and liquid chromatography-mass spectrometry (LC-MS/MS) was performed using sera from 40 patients enrolled in the Multicentre Osteoarthritis interVEntion trial with Sysadoa (MOVES). The panel of proteins potentially useful to predict KOA patient's response was clinically validated in the whole MOVES cohort at baseline (n = 506) using commercially available enzyme-linked immunosorbent assays kits. Logistic regression models and receiver-operating-characteristics (ROC) curves were used to analyze the contribution of these proteins to our prediction models of symptomatic drug response in KOA. Results: In the discovery phase of the study, a panel of six putative predictive biomarkers of response to CS+GH (APOA2, APOA4, APOH, ITIH1, C4BPa and ORM2) were identified by shotgun proteomics. Data are available via ProteomeXchange with identifier PXD012444. In the verification phase, the panel was verified in a larger set of KOA patients (n = 262). Finally, ITIH1 and ORM2 were qualified by a blind test in the whole MOVES cohort at baseline. The combination of these biomarkers with clinical variables predict the patients' response to CS+GH with a specificity of 79.5% and a sensitivity of 77.1%. Conclusions: Combining clinical and analytical parameters, we identified one biomarker that could accurately predict KOA patients' response to CS+GH treatment. Its use would allow an increase in response rates and safety for the patients suffering KOA.