An automated assay for the clinical measurement of plasma renin activity by immuno-MALDI (iMALDI).

Plasma renin activity (PRA) is essential for the screening and diagnosis of primary aldosteronism (PA), a form of secondary hypertension, which affects approximately 100 million people worldwide. It is commonly determined by radioimmunoassay (RIA) and, more recently, by relatively low-throughput LC-MS/MS methods. In order to circumvent the negative aspects of RIAs (radioisotopes, cross-reactivity) and the low throughput of LC-MS based methods, we have developed a high-throughput immuno-MALDI (iMALDI)-based assay for PRA determination using an Agilent Bravo for automated liquid handling and a Bruker Microflex LRF instrument for MALDI analysis, with the goal of implementing the assay in clinical laboratories. The current assay allows PRA determination of 29 patient samples (192 immuno-captures), within ~6 to 7h, using a 3-hour Ang I generation period, at a 7.5-fold faster analysis time than LC-MS/MS. The assay is performed on 350µL of plasma, and has a linear range from 0.08 to 5.3ng/L/s in the reflector mode, and 0.04 to 5.3ng/L/s in the linear mode. The analytical precision is 2.0 to 9.7% CV in the reflector mode, and 1.5 to 14.3% CV in the linear mode. A method comparison to a clinically employed LC-MS/MS assay for PRA determination showed excellent correlation within the linear range, with an R(2) value of ≥0.98. This automated high throughput iMALDI platform has clinically suitable sensitivity, precision, linear range, and correlation with the standard method for PRA determination. Furthermore, the developed workflow based on the iMALDI technology can be used for the determination of other proteomic biomarkers. This article is part of a Special Issue entitled: Medical Proteomics.

Paleoproteomic study of the Iceman's brain tissue.

The Tyrolean Iceman, a Copper-age ice mummy, is one of the best-studied human individuals. While the genome of the Iceman has largely been decoded, tissue-specific proteomes have not yet been investigated. We studied the proteome of two distinct brain samples using gel-based and liquid chromatography-mass spectrometry-based proteomics technologies together with a multiple-databases and -search algorithms-driven data-analysis approach. Thereby, we identified a total of 502 different proteins. Of these, 41 proteins are known to be highly abundant in brain tissue and 9 are even specifically expressed in the brain. Furthermore, we found 10 proteins related to blood and coagulation. An enrichment analysis revealed a significant accumulation of proteins related to stress response and wound healing. Together with atomic force microscope scans, indicating clustered blood cells, our data reopens former discussions about a possible injury of the Iceman's head near the site where the tissue samples have been extracted.

Effects of smoking and smoking cessation on human serum metabolite profile: results from the KORA cohort study.

BACKGROUND: Metabolomics helps to identify links between environmental exposures and intermediate biomarkers of disturbed pathways. We previously reported variations in phosphatidylcholines in male smokers compared with non-smokers in a cross-sectional pilot study with a small sample size, but knowledge of the reversibility of smoking effects on metabolite profiles is limited. Here, we extend our metabolomics study with a large prospective study including female smokers and quitters. METHODS: Using targeted metabolomics approach, we quantified 140 metabolite concentrations for 1,241 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) human cohort at two time points: baseline survey conducted between 1999 and 2001 and follow-up after seven years. Metabolite profiles were compared among groups of current smokers, former smokers and never smokers, and were further assessed for their reversibility after smoking cessation. Changes in metabolite concentrations from baseline to the follow-up were investigated in a longitudinal analysis comparing current smokers, never smokers and smoking quitters, who were current smokers at baseline but former smokers by the time of follow-up. In addition, we constructed protein-metabolite networks with smoking-related genes and metabolites. RESULTS: We identified 21 smoking-related metabolites in the baseline investigation (18 in men and six in women, with three overlaps) enriched in amino acid and lipid pathways, which were significantly different between current smokers and never smokers. Moreover, 19 out of the 21 metabolites were found to be reversible in former smokers. In the follow-up study, 13 reversible metabolites in men were measured, of which 10 were confirmed to be reversible in male quitters. Protein-metabolite networks are proposed to explain the consistent reversibility of smoking effects on metabolites. CONCLUSIONS: We showed that smoking-related changes in human serum metabolites are reversible after smoking cessation, consistent with the known cardiovascular risk reduction. The metabolites identified may serve as potential biomarkers to evaluate the status of smoking cessation and characterize smoking-related diseases.