Body Fluids Of Interest Research Articles

Affinity Proteomics Assays for Cardiovascular and Atherosclerotic Disease Biomarkers.

Systematic exploration of the dynamic human plasma proteome enables the discovery of novel protein biomarkers. Using state-of-the-art technologies holds the promise to facilitate a better diagnosis and risk prediction of diseases. Cardiovascular disease (CVD) pathophysiology is characterized for unbalancing of processes such as vascular inflammation, endothelial dysfunction, or lipid profiles among others. Such processes have a direct impact on the dynamic and complex composition of blood and hence the plasma proteome. Therefore, the study of the plasma proteome comprises an excellent exploratory source of biomarker research particularly for CVD. We describe the protocol for performing the discovery of protein biomarker candidates using the suspension bead array technology. The process does not require depletion steps to remove abundant proteins and consumes only a few microliters of sample from the body fluid of interest. The approach is scalable to measure many analytes as well as large numbers of samples. Moreover, we describe a bead-assisted antibody-labeling process that helps to develop quantitative assays for validation purposes and facilitate the translation of the identified candidates into clinical studies.

Conservation of the Amyloid Interactome Across Diverse Fibrillar Structures

Several human proteins cause disease by misfolding and aggregating into amyloid fibril deposits affecting the surrounding tissues. Multiple other proteins co-associate with the diseased deposits but little is known about how this association is influenced by the nature of the amyloid aggregate and the properties of the amyloid-forming protein. In this study, we investigated the co-aggregation of plasma and cerebrospinal proteins in the presence of pre-formed amyloid fibrils. We evaluated the fibril-associated proteome across multiple amyloid fibril types that differ in their amino acid sequences, ultrastructural morphologies, and recognition by amyloid-binding dyes. The fibril types included aggregates formed by Amyloid β, α-synuclein, and FAS4 that are associated with pathological disorders, and aggregates formed by the glucagon and C-36 peptides, currently not linked to any human disease. Our results highlighted a highly similar response to the amyloid fold within the body fluid of interest. Fibrils with diverse primary sequences and ultrastructural morphologies only differed slightly in the composition of the co-aggregated proteins but were clearly distinct from less fibrillar and amorphous aggregates. The type of body fluid greatly affected the resulting amyloid interactome, underlining the role of the in vivo environment. We conclude that protein fibrils lead to a specific response in protein co-aggregation and discuss the effects hereof in the context of amyloid deposition.

Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM Strategic Conference.

This paper, prepared by the EFLM Task and Finish Group on Allocation of laboratory tests to different models for performance specifications (TFG-DM), is dealing with criteria for allocating measurands to the different models for analytical performance specifications (APS) recognized in the 1st EFLM Strategic Conference Consensus Statement. Model 1, based on the effect of APS on clinical outcome, is the model of choice for measurands that have a central role in the decision-making of a specific disease or clinical situation and where cut-off/decision limits are established for either diagnosing, screening or monitoring. Total cholesterol, glucose, HbA1c, serum albumin and cardiac troponins represent practical examples. Model 2 is based on components of biological variation and should be applied to measurands that do not have a central role in a specific disease or clinical situation, but where the concentration of the measurand is in a steady state. This is best achieved for measurands under strict homeostatic control in order to preserve their concentrations in the body fluid of interest, but it can also be applied to other measurands that are in a steady state in biological fluids. In this case, it is expected that the "noise" produced by the measurement procedure will not significantly alter the signal provided by the concentration of the measurand. This model especially applies to electrolytes and minerals in blood plasma (sodium, potassium, chloride, bicarbonate, calcium, magnesium, inorganic phosphate) and to creatinine, cystatin C, uric acid and total protein in plasma. Model 3, based on state-of-the-art of the measurement, should be used for all the measurands that cannot be included in models 1 or 2.

Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

BackgroundThe purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School.ResultsWe found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential.ConclusionsValid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.