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      Brain-specific Proteins Decline in the Cerebrospinal Fluid of Humans with Huntington Disease*S⃞

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          Abstract

          We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.

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          Mass spectrometry-based proteomics.

          Recent successes illustrate the role of mass spectrometry-based proteomics as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. These include the study of protein-protein interactions via affinity-based isolations on a small and proteome-wide scale, the mapping of numerous organelles, the concurrent description of the malaria parasite genome and proteome, and the generation of quantitative protein profiles from diverse species. The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.
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            Comparison of label-free methods for quantifying human proteins by shotgun proteomics.

            Measurements of mass spectral peak intensities and spectral counts are promising methods for quantifying protein abundance changes in shotgun proteomic analyses. We describe Serac, software developed to evaluate the ability of each method to quantify relative changes in protein abundance. Dynamic range and linearity using a three-dimensional ion trap were tested using standard proteins spiked into a complex sample. Linearity and good agreement between observed versus expected protein ratios were obtained after normalization and background subtraction of peak area intensity measurements and correction of spectral counts to eliminate discontinuity in ratio estimates. Peak intensity values useful for protein quantitation ranged from 10(7) to 10(11) counts with no obvious saturation effect, and proteins in replicate samples showed variations of less than 2-fold within the 95% range (+/-2sigma) when >or=3 peptides/protein were shared between samples. Protein ratios were determined with high confidence from spectral counts when maximum spectral counts were >or=4 spectra/protein, and replicates showed equivalent measurements well within 95% confidence limits. In further tests, complex samples were separated by gel exclusion chromatography, quantifying changes in protein abundance between different fractions. Linear behavior of peak area intensity measurements was obtained for peptides from proteins in different fractions. Protein ratios determined by spectral counting agreed well with those determined from peak area intensity measurements, and both agreed with independent measurements based on gel staining intensities. Overall spectral counting proved to be a more sensitive method for detecting proteins that undergo changes in abundance, whereas peak area intensity measurements yielded more accurate estimates of protein ratios. Finally these methods were used to analyze differential changes in protein expression in human erythroleukemia K562 cells stimulated under conditions that promote cell differentiation by mitogen-activated protein kinase pathway activation. Protein changes identified with p<0.1 showed good correlations with parallel measurements of changes in mRNA expression.
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              Bioinformatics

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                Author and article information

                Journal
                Mol Cell Proteomics
                mcp
                Molecular & Cellular Proteomics : MCP
                American Society for Biochemistry and Molecular Biology
                1535-9476
                1535-9484
                March 2009
                March 2009
                : 8
                : 3
                : 451-466
                Affiliations
                From the []Public Health Sciences Division and [§ ]Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, []Department of Anatomy and Cell Biology, McGill University, Quebec H3A 1A4, Canada, []State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing 102206, China, [** ]Medizinisches Proteom-Center, Ruhr-Universität Bochum, D-447801 Bochum, Germany, and [‡‡ ]Department of Medical Genetics, University of British Columbia and Center for Molecular Medicine and Therapeutics, Child and Family Research Institute, Vancouver, British Columbia V5Z 4H4, Canada
                Author notes
                [§§]

                To whom correspondence should be addressed: Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., M2-B230, Seattle, WA 98109. Tel.: 206-667-4612; Fax: 206-667-7264; E-mail: mmcintos@ 123456fhcrc.org

                Article
                M800231-MCP200
                10.1074/mcp.M800231-MCP200
                2649809
                18984577
                84db6b22-cbbd-490a-ba91-9d73e0e76626
                Copyright © 2009, The American Society for Biochemistry and Molecular Biology

                Author's Choice - Final Version Full Access

                Creative Commons Attribution Non-Commercial License applies to Author Choice Articles

                History
                : 22 May 2008
                : 7 October 2008
                Categories
                Research

                Molecular biology
                Molecular biology

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