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The emergence of new biological disease markers from mass spectrometric studies of serum proteomes has been quite limited. There are challenges regarding the analytical and statistical procedures, preanalytical variability, and study designs. In this serological study of ovarian cancer, we apply classification methods in a strictly designed study with standardized sample collection procedures. A total of 265 sera from women admitted with symptoms of a pelvic mass were used for model building. We developed a rigorous approach for building classification models suitable for the highly multivariate data and illustrate how to evaluate and ensure data quality and optimize data preprocessing and data reduction. We document time dependent changes in peak profiles up to 15 months after sampling even when storing samples at -20[degrees]C. The developed classification model was validated using completely independent samples, and a cross validation procedure which we call cross model validation was applied to get realistic performance values. The best models were able to classify with 79% specificity and 56% sensitivity, i.e., an analytical accuracy of 68%. However, the existing serum marker (CA-125) alone gave a better analytical accuracy (81%) in the same sample set. Also, the combination of mass spectrometric data and levels of CA-125 data did not improve the predictive performance of models. In conclusion, proteomic approaches to biomarker discovery are not necessarily yielding straightforward diagnostic leads but lay the foundation for more work.

Biomarkers in the cerebrospinal fluid (CSF) may be important for the diagnosis of chronic degenerative disorders in the central nervous system including dementia. Existing CSF biomarkers for dementia, however, are relatively nonspecific. More specific markers may be found by targeting investigations based on knowledge of the molecular pathology of the disease in question. In Alzheimer's disease, hyperphosphorylation of the tau protein is a characteristic feature and thus a comprehensive characterization of the phosphoproteome of the CSF may be pursued to obtain a complete picture of phosphorylation aberrations in health and disease. Toward that goal we here describe a method for a comprehensive isolation and identification of phosphorylated tryptic peptides derived from CSF proteins using a simple sample preparation step and titanium dioxide-affinity chromatography followed by MALDI-TOF or LC-MS/MS linear iontrap-FT mass spectrometry. Whereas not all previously reported phosphoproteins were found in normal CSF, we detected 56 putative novel phosphorylation sites in 38 proteins in addition to known sites. The approach seems to be a promising foundation for the discovery of new biomarkers embedded in the CSF phosphoproteome.

Capillary electrophoretic separation profiles of cleaved variants of [[beta].sub.2]-microglobulin ([[beta].sub.2]m) reflect the conformational equilibria existing in solutions of these proteins. The characterization of these equilibria is of interest since [[beta].sub.2]m is responsible for amyloid formation in dialysisrelated amyloidosis and thus is able to attain alternative conformations that lead to irreversible aggregation and precipitation. In this study, we quantitate the increased conformational instability of cleaved [[beta].sub.2]m by extracting rate constants and activation energies by simulating the experimental data using a unified theory for dynamic chromatography and dynamic electrophoresis. The results are correlated with the outcome of independent experiments based on mass spectrometric measurement of H/D exchange. This study illustrates that dynamic capillary electrophoresis is suitable for the investigation of the interconversion of protein conformations of amyloidogenic molecules and is not only restricted to ideal model compounds.

Proteomic investigations of sera are potentially of value for diagnosis, prognosis, choice of therapy, and disease activity assessment by virtue of discovering new biomarkers and biomarker patterns. Much debate focuses on the biological relevance and the need for identification of such biomarkers while less effort has been invested in devising standard procedures for sample preparation and storage in relation to model building based on complex sets of mass spectrometric (MS) data. Thus, development of standardized methods for collection and storage of patient samples together with standards for transportation and handling of samples are needed. This requires knowledge about how sample processing affects MS-based proteome analyses and thereby how nonbiological biased classification errors are avoided. In this study, we characterize the effects of sample handling, including clotting conditions, storage temperature, storage time, and freeze/thaw cycles, on MS-based proteomics of human serum by using principal components analysis, support vector machine learning, and clustering methods based on genetic algorithms as class modeling and prediction methods. Using spiking to artificially create differentiable sample groups, this integrated approach yields data that--even when working with sample groups that differ more than may be expected in biological studies--clearly demonstrate the need for comparable sampling conditions for samples used for modeling and for the samples that are going into the test set group. Also, the study emphasizes the difference between class prediction and class comparison studies as well as the advantages and disadvantages of different modeling methods.

An analysis of the structural variant of beta2-microglobulin (beta2m) that is linked with cancer and inflammation and also present in the circulation of dialysis patients is conducted. The results highlight the instability and amyloidogenicity under near physiological conditions of a slightly modified beta2m variant generated by limited proteolysis and also describes stages of amyloid formation from early conformational variants to overt fibrillation.

Disease-specific alterations in proteins and peptides such as the appearance of new isoforms, changed relative concentrations of known isoforms, or changed catabolism characterize the group of protein precipitation disorders collectively known as amyloidoses. The goal of this study was to develop an approach for isolating and characterizing the pool of isoforms of a polypeptide of interest from biological fluids for use in development of diagnostic markers and elucidation of pathogenesis. For this purpose, we employed an on-line immunoaffinity-liquid chromatography-mass spectrometry (IA-LC-MS) modular approach using antibodies binding populations of protein isoforms. In this system, crude biological samples, e.g., serum, may be injected and subjected to fast hands-off analysis. The setup consists of an optional preclear column for removal of unspecific binding components, an immunoaffinity column, a short cartridgelike reversed-phase column, and an electrospray time-of-flight mass spectrometer. We have tested the system for the automated analysis of three amyloid-related polypeptides, serum amyloid P component, amyloid [beta]-peptide, and [[beta].sub.2]-microglobulin, and we show the feasibility of detection of altered isoforms or determination of relative abundance of isoforms of the proteins from serum or cerebrospinal fluid samples. For each new protein investigated, the only change needed in the system is a new antibody or antibody mixture and the selection of a reversed-phase cartridge of appropriate hydrophobicity.

The study of drug-protein interactions by capillary electrophoresis frontal analysis requires establishment of a sufficient mobility difference between the mobility of the ligand and protein. The potential utility of dextran as a run buffer additive to manipulate the electrophoretic mobilities of low molecular weight ligands and protein in capillary electrophoresis frontal analysis binding studies was assessed. It was demonstrated that dextran was effective in improving the separation between the ligands warfarin and fiurbiprofen and human serum albumin. Separation of ligand and protein increased with the concentration of added dextran (0-7.5% (w/w)), while molecular weight of the additive (70 000-2 000 000) only had a minor effect. The effect of dextran addition on viscosity and electrophoretic and electroosmotic mobilites was systematically studied. Optimal frontal analysis settings were a compromise between achieving satisfactory separation and acceptable analysis times without loss of plateau peak conditions. No effect of dextran upon the drug-human serum albumin interactions could be detected for the model ligands. Introduction of dextran into the electrophoresis buffer expands the applicability of capillary electrophoresis frontal analysis in drug research to binding interactions between proteins and low molecular weight ligands possessing similar electrophoretic mobilities.

Background: MicroRNAs are small noncoding RNAs involved in the post-transcriptional regulation of protein synthesis. Extracellular microRNAs are accessible in a stable form in biofluids.Objectives: The aim was to identify individual microRNAs and/or subsets of microRNAs in CSF with biomarker potential and thus identify specific putative pathophysiological pathways.Methods: In a two-step exploratory study design of PD, MSA, PSP, and controls, we initially profiled CSF microRNAs in a pilot cohort (n = 40) by screening for 372 microRNAs. Subsequently, we attempted to validate findings in an independent study cohort in CSF (n = 118) and ethylenediaminetetraacetic acid plasma (n = 114). This study cohort encompassed 46 microRNAs, of which 26 were singled out from the pilot cohort, and an additional 20 microRNAs were added based on previous publications. The most accurate diagnostic microRNA classifiers were identified in a multivariable logistic regression model adjusted for age and sex.Results: A set of three microRNAs in CSF discriminated PD and MSA from controls with good diagnostic accuracy by receiver operating characteristics curve evaluation. The microRNAs were for PD versus controls: miR-7-5p, miR-331-5p, and miR-145-5p (area under the curve = 0.88) and MSA versus controls: miR-7-5p, miR-34c-3p, and miR-let-7b-5p (area under the curve = 0.87). The classifier that best distinguished MSA and PD consisted of two microRNAs: miR-9-3p and miR-106b-5p (area under the curve = 0.73). A single microRNA, miR-106b-5p, provided the best discrimination between PD and PSP (area under the curve = 0.85) in the CSF.Conclusions: Levels of specific trios of CSF-microRNAs discriminate well between α-synucleinopathies (PD and MSA) and controls. The results need to be validated in larger, independent cohorts. © 2018 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

We aim to examine if circulating micro-RNA and cytokine levels associate with dementia diagnosis and cognitive scores. To test our hypothesis, we use plasma donated from 48 monozygotic twin pairs in 1997 and 46 micro-RNAs and 10 cytokines were quantified using microfluidic RT-qPCR and multiplex solid-phase immunoassays, respectively. Micro-RNA and cytokine profiling were examined for associations with dementia diagnoses in a longitudinal registry study or with cognitive scores at baseline. Thirty-six micro-RNAs and all cytokines were detected consistently. Micro-RNA profiles associate with diagnoses and cognitive scores at statistically significant levels while cytokine only showed trends pointing at chronic inflammation in twins having or developing dementia. The most notable findings were decreased miR-106a and miR-210, and increased miR-106b expression in twins with a dementia diagnosis. This pioneering evaluation of micro-RNA and cytokine and dementia diagnosis suggests micro-RNA targets in vasculogenesis, lipoprotein transport, and amyloid precursor protein genes. [ABSTRACT FROM AUTHOR]