Albumin and multiple sclerosis

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Leakage of the blood–brain barrier (BBB) is a common pathological feature in multiple sclerosis (MS). Following a breach of the BBB, albumin, the most abundant protein in plasma, gains access to CNS tissue where it is exposed to an inflammatory milieu and tissue damage, e.g., demyelination. Once in the CNS, albumin can participate in protective mechanisms. For example, due to its high concentration and molecular properties, albumin becomes a target for oxidation and nitration reactions. Furthermore, albumin binds metals and heme thereby limiting their ability to produce reactive oxygen and reactive nitrogen species. Albumin also has the potential to worsen disease. Similar to pathogenic processes that occur during epilepsy, extravasated albumin could induce the expression of proinflammatory cytokines and affect the ability of astrocytes to maintain potassium homeostasis thereby possibly making neurons more vulnerable to glutamate exicitotoxicity, which is thought to be a pathogenic mechanism in MS. The albumin quotient, albumin in cerebrospinal fluid (CSF)/albumin in serum, is used as a measure of blood-CSF barrier dysfunction in MS, but it may be inaccurate since albumin levels in the CSF can be influenced by multiple factors including: 1) albumin becomes proteolytically cleaved during disease, 2) extravasated albumin is taken up by macrophages, microglia, and astrocytes, and 3) the location of BBB damage affects the entry of extravasated albumin into ventricular CSF. A discussion of the roles that albumin performs during MS is put forth

The Effect of Preanalytical Factors on Stability of the Proteome and Selected Metabolites in Cerebrospinal Fluid (CSF)

To standardize the use of cerebrospinal fluid (CSF) for biomarker research, a set of stability studies have been performed on porcine samples to investigate the influence of common sample handling procedures on proteins, peptides, metabolites and free amino acids. This study focuses at the effect on proteins and peptides, analyzed by applying label-free quantitation using microfluidics nanoscale liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (chipLC-MS) as well as matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FT-ICR-MS) and Orbitrap LC-MS/MS to trypsin-digested CSF samples, The factors assessed were a 30 or 120 min time delay at room temperature before storage at -80 degrees C after the collection of CSF in order to mimic potential delays in the clinic (delayed storage), storage at 4 degrees C after trypsin digestion to mimic the time that samples remain in the cooled autosampler of the analyzer, and repeated freeze-thaw cycles to mimic storage and handling procedures in the laboratory. The delayed storage factor was also analyzed by gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) for changes of metabolites and free amino acids, respectively. Our results show that repeated freeze/thawing introduced changes in transthyretin peptide levels. The trypsin digested samples left at 4 degrees C in the autosampler showed a time-dependent decrease of peak areas for peptides from prostaglandin D-synthase and serotransferrin. Delayed storage of CSF led to changes in prostaglandin D-synthase derived peptides as well as to increased levels of certain amino acids and metabolites. The changes of metabolites, amino acids and proteins in the delayed storage study appear to be related to remaining white blood cells. Our recommendations are to centrifuge CSF samples immediately after collection to remove white blood cells, aliquot, and then snap-freeze the supernatant in liquid nitrogen for storage at -80 degrees C. Preferably samples should not be left in the autosampler for more than 24 h and freeze/thaw cycles should be avoided if at all possible

The impact of delayed storage on the measured proteome and metabolome of human cerebrospinal fluid

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Detection of enterovirus in the islet cells of patients with type 1 diabetes: what do we learn from immunohistochemistry? Reply to Hansson SF, Korsgren S, Pontén F et al [letter].

CommentLetterAuthor version of comment article submitted to Diabetologia. The final publication is available at Springer via http://dx.doi.org/10.1007/s00125-014-3167-2Comment on Detection of enterovirus in the islet cells of patients with type 1 diabetes: what do we learn from immunohistochemistry? [Diabetologia. 2014] Evaluation of the fidelity of immunolabelling obtained with clone 5D8/1, a monoclonal antibody directed against the enteroviral capsid protein, VP1, in human pancreas. [Diabetologia. 2014

Evaluation of the fidelity of immunolabelling obtained with clone 5D8/1, a monoclonal antibody directed against the enteroviral capsid protein, VP1, in human pancreas

Journal ArticleCopyright © Springer-Verlag Berlin Heidelberg 2013Aims/hypothesis: Enteroviral infection has been implicated in the development of islet autoimmunity in type 1 diabetes and enteroviral antigen expression has been detected by immunohistochemistry in the pancreatic beta cells of patients with recent-onset type 1 diabetes. However, the immunohistochemical evidence relies heavily on the use of a monoclonal antibody, clone 5D8/1, raised against an enteroviral capsid protein, VP1. Recent data suggest that the clone 5D8/1 may also recognise non-viral antigens; in particular, a component of the mitochondrial ATP synthase (ATP5B) and an isoform of creatine kinase (CKB). Therefore, we evaluated the fidelity of immunolabelling by clone 5D8/1 in the islets of patients with type 1 diabetes. Methods: Enteroviral VP1, CKB and ATP5B expression were analysed by western blotting, RT-PCR and immunocytochemistry in a range of cultured cell lines, isolated human islets and human tissue. Results: Clone 5D8/1 labelled CKB, but not ATP5B, on western blots performed under denaturing conditions. In cultured human cell lines, isolated human islets and pancreas sections from patients with type 1 diabetes, the immunolabelling of ATP5B, CKB and VP1 by 5D8/1 was readily distinguishable. Moreover, in a human tissue microarray displaying more than 80 different cells and tissues, only two (stomach and colon; both of which are potential sites of enterovirus infection) were immunopositive when stained with clone 5D8/1. Conclusions/interpretation: When used under carefully optimised conditions, the immunolabelling pattern detected in sections of human pancreas with clone 5D8/1 did not reflect cross-reactivity with either ATP5B or CKB. Rather, 5D8/1 is likely to be representative of enteroviral antigen expression. © 2013 Springer-Verlag Berlin Heidelberg.European Union’s Seventh Framework Programme PEVNET (FP7/2007-2013)Diabetes Research and Wellness Foundation non-clinical research fellowshipKarolinska InstitutetStrategic Research Programme in Diabetes at the Karolinska InstitutetSwedish Research CouncilJDR

Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals

Pre- and Post-analytical Factors in Biomarker Discovery

The translation of promising biomarkers, which were identified in biomarker discovery experiments, to clinical assays is one of the key challenges in present-day proteomics research. Many so-called "biomarker candidates" fail to progress beyond the discovery phase, and much emphasis is placed on pre- and post-analytical variability in an attempt to provide explanations for this bottleneck in the biomarker development pipeline. With respect to such variability, there is a large number of pre- and post-analytical factors which may impact the outcomes of proteomics experiments and thus necessitate tight control. This chapter highlights some of these factors and provides guidance for addressing them on the basis of examples from previously published proteomics studies.</p