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      Gray Matter Is Targeted in First-Attack Multiple Sclerosis

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          The cause of multiple sclerosis (MS), its driving pathogenesis at the earliest stages, and what factors allow the first clinical attack to manifest remain unknown. Some imaging studies suggest gray rather than white matter may be involved early, and some postulate this may be predictive of developing MS. Other imaging studies are in conflict. To determine if there was objective molecular evidence of gray matter involvement in early MS we used high-resolution mass spectrometry to identify proteins in the cerebrospinal fluid (CSF) of first-attack MS patients (two independent groups) compared to established relapsing remitting (RR) MS and controls. We found that the CSF proteins in first-attack patients were differentially enriched for gray matter components (axon, neuron, synapse). Myelin components did not distinguish these groups. The results support that gray matter dysfunction is involved early in MS, and also may be integral for the initial clinical presentation.

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          Most cited references 35

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          A statistical model for identifying proteins by tandem mass spectrometry.

          A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. Peptides that correspond to more than a single protein in the sequence database are apportioned among all corresponding proteins, and a minimal protein list sufficient to account for the observed peptide assignments is derived using the expectation-maximization algorithm. Using peptide assignments to spectra generated from a sample of 18 purified proteins, as well as complex H. influenzae and Halobacterium samples, the model is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications. This method allows filtering of large-scale proteomics data sets with predictable sensitivity and false positive identification error rates. Fast, consistent, and transparent, it provides a standard for publishing large-scale protein identification data sets in the literature and for comparing the results obtained from different experiments.
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            Diagnostic criteria for multiple sclerosis: 2005 revisions to the "McDonald Criteria".

            New diagnostic criteria for multiple sclerosis integrating magnetic resonance image assessment with clinical and other paraclinical methods were introduced in 2001. The "McDonald Criteria" have been extensively assessed and used since 2001. New evidence and consensus now strengthen the role of these criteria in the multiple sclerosis diagnostic workup to demonstrate dissemination of lesions in time, to clarify the use of spinal cord lesions, and to simplify diagnosis of primary progressive disease. The 2005 Revisions to the McDonald Diagnostic Criteria for MS should simplify and speed diagnosis, whereas maintaining adequate sensitivity and specificity.
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              Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.

              Liquid chromatography and tandem mass spectrometry (LC-MS/MS) has become the preferred method for conducting large-scale surveys of proteomes. Automated interpretation of tandem mass spectrometry (MS/MS) spectra can be problematic, however, for a variety of reasons. As most sequence search engines return results even for 'unmatchable' spectra, proteome researchers must devise ways to distinguish correct from incorrect peptide identifications. The target-decoy search strategy represents a straightforward and effective way to manage this effort. Despite the apparent simplicity of this method, some controversy surrounds its successful application. Here we clarify our preferred methodology by addressing four issues based on observed decoy hit frequencies: (i) the major assumptions made with this database search strategy are reasonable; (ii) concatenated target-decoy database searches are preferable to separate target and decoy database searches; (iii) the theoretical error associated with target-decoy false positive (FP) rate measurements can be estimated; and (iv) alternate methods for constructing decoy databases are similarly effective once certain considerations are taken into account.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                10 September 2013
                : 8
                : 9
                [1 ]Departments of Medicine, and Epidemiology and Biostatistics, Rutgers University New Jersey Medical School, Newark, New Jersey, United States of America
                [2 ]Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States of America
                [3 ]Department of Chemistry-Biomedical Center, Analytical Chemistry and SciLife Lab, Uppsala University, Uppsala, Sweden
                [4 ]Faculty of Medicine, Albert Szent-Györgyi Clinical Center and Neuroscience Research Group of the Hungarian Academy of Sciences and University of Szeged, Szeged, Hungary
                [5 ]Department of Neurology, Stony Brook University, Stony Brook, New York, United States of America
                University of Utah School of Medicine, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SES TEA TL RDS. Performed the experiments: SES TEA TL AAS DGC. Analyzed the data: SES TEA TL RDS PKC BKH JB. Contributed reagents/materials/analysis tools: SES TEA TL RDS PKC BKH DGC FX AAS DZ LV. Wrote the paper: SES TEA TL JB RDS PKC.


                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 7
                Funding was provided by National Institutes of Health, through NIAID (grant AI088765), NIDA (grant DA021071), National Center for Research Resources (5 P41 RR018522-10), and the National Institute of General Medical Sciences (8 P41 GM103493-10) from the National Institutes of Health and Laboratory Directed Research and Development program at Pacific Northwest National Laboratory. PNNL proteomics research described was performed in the Environmental Molecular Sciences Laboratory, a U. S. Department of Energy/BER national scientific user facility at Pacific Northwest National Laboratory, operated by Battelle Memorial Institute; the Swedish Research Council (621-2008-3562, 621-2011-4423), Uppsala Berzelii Technology Center for Neurodiagnostics, SciLifeLab-Uppsala. The authors acknowledge assistance of Peter Klivenyi, Krisztina Bencsik, Judit Füvesi, Cecilia Rajda. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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