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Proteomic analysis of cerebrospinal fluid changes related to postmortem interval.

Changes in brain biochemistry attributable to disease or injury can result in altered concentrations of individual proteins in the cerebrospinal fluid (CSF) [3] in living persons (1). The protein content of CSF has also been found to change upon death (2, 3). For example, it is known that the concentration of several enzymes, including creatine kinase B, increases within hours of death (3). Such postmortem changes in CSF protein may result from cell death, breakdown of the blood-brain barrier, or other events that are initiated when an individual dies. It has been proposed that a comparison of antemortem and postmortem CSF protein production may yield biomarkers for some neurological disorders (2). Accordingly, several studies used enzyme activity assays to measure postmortem changes in human CSF proteins such as lactate dehydrogenase, [beta]-glucuronidase, and acid phosphatase (3, 4). In another study, postmortem ventricular CSF from 4 individuals was compared with the antemortem lumbar CSF from 4 other control participants by 2-dimensional gel electrophoresis (2DE) to separate and visualize the proteins (2). This proteomic approach identified 13 protein spots showing a significant increase in postmortem CSF compared with antemortem CSF. Almost all of these proteins were intracellular proteins, suggesting that they were released into the CSF upon cell death. In subsequent studies, those authors demonstrated that at least 2 of the proteins (nucleoside diphoshphate kinase A and posttranslationally modified prostaglandin D-2 synthase) have an altered serum concentration after stroke (5, 6).

We used 2DE to compare antemortem and postmortem CSF. In contrast with previous studies, this analysis compares antemortem and postmortem CSF from the same individuals. This unique collection of antemortem and postmortem CSF from 7 individuals, together with the use of a fluorescent protein stain and laser scanning, enabled the identification of 54 protein spots that show a consistent change in production in postmortem CSF samples. The ability to compare antemortem and postmortem CSF from the same individuals decreases the chance that the observed changes are a direct or indirect result of genetic variability. To explore the question of whether there may be a relationship between the postmortem interval (PMI) and observed changes in protein amounts, perhaps caused by cell lysis or breakdown of the blood-brain barrier, a correlation analysis was performed for production changes and PMI.

Materials and Methods

SAMPLES

Both antemortem and postmortem CSF samples were collected from 7 individuals by the Institute for Brain Aging and Dementia at the University of California, Irvine, CA. The PMIs were 1.5-9.5 h. The antemortem samples were collected by lumbar puncture, and ventricular CSF samples were collected postmortem during autopsy. The samples were frozen at the brain bank and shipped to Cornell on dry ice. The samples remained frozen until used. The project has been reviewed and approved by the Cornell University committee on human participants (protocol 01-01-003).

On the basis of autopsy reports, 6 of the individuals were diagnosed with Alzheimer disease and the 7th individual with Pick disease. The Alzheimer patients (4 male, 2 female) were 62-88 years old (at death), and the Pick disease patient (female) was 44 years old. The mean disease duration was 9 years, and the causes of death, according to the autopsy, were either respiratory failure or unknown. CSF from unaffected persons was not used because of the difficulty in finding samples of both ante-and postmortem CSF from a single healthy individual. It is possible that observations based on 7 individuals with dementia, 6 of whom have Alzheimer disease, would confound the analysis; however, CSF protein changes that have previously been reported for Alzheimer disease are typically less than the 3.5-fold cutoff used in this study (7, 8). Thus, we expected the changes observed to be a result of death rather than the patients' previous disease state.

2DE

The details of the protocols used for performing 2DE have been previously published (9). Briefly, 250 [micro]L of CSF (containing ~100 [micro]g of protein) were precipitated with ice-cold ethanol. The protein pellet was then dissolved in a solution of 9 mol/L urea (Bio-Rad Laboratories), 2% 2-mercaptoethanol (J.T. Baker), 2% IGEPAL (Sigma-Aldrich), and 0.25% carrier ampholytes (Bio-Rad). The protein mixture was then loaded onto an 18-cm, 3-10 nonlinear immobilized pH gradient isoelectric focusing gel (GE Healthcare). Isoelectric focusing was then performed at 20 [degrees]C with the Protean IEF unit (Bio-Rad) for a total of 100 kVh to separate proteins in the first dimension by isoelectric point. The immobilized pH gradient gels were equilibrated in solutions containing dithiothreitol (Bio-Rad) and, subsequently, iodoacetamide (Fluka) for reduction and alkylation of the focused proteins. In the second dimension, polyacrylamide gel electrophoresis was performed with 12%-15%T gradient slab gels to separate proteins by size. The gels were fixed, stained with SYPRO Ruby Protein Gel Stain (Molecular Probes), and destained for 24 h in a solution of 10% methanol and 7% acetic acid. The gels were scanned on an FLA-3000 Fluorescent Image Analyzer (Fuji Photo Film Company).

GEL IMAGE ANALYSIS

The gel images were imported into a 2D gel analysis software package, Melanie v4.0 (GeneBio). Spots were autodetected by the software and the detected spots were manually edited to remove technical artifacts. A reference gel image was created by combining the spots present in 1 antemortem gel and 1 postmortem gel. The reference gel image contained all possible spots from these 2 gels. The protein spots from each of the 14 samples were then matched to this reference image to allow an intergel spot comparison. Spot changes that occurred in all individuals were included, and any spot changes that were not present in a particular individual were excluded from this study. Thus, any spots appearing in samples that were not used to create the reference image were not matched and not included in the subsequent analysis. Matching was initially performed with the automatic matching function in the Melanie software and was then manually edited by a single individual to correct for missed or incorrect matches. The percent integrated intensity (%volume) of each spot on each gel was then exported. The fold change for each spot was calculated by dividing the postmortem %volume by the antemortem %volume from the same patient.

Previous studies report a gradient in protein concentration between the ventricle and lumbar regions (10,11). Individual protein concentrations have also been shown to change up to 3.5-fold between these 2 regions (12). Therefore, we used the convention suggested by Conti et al. (13) to compare protein concentrations between these regions: a change had to be at least 3.5-fold to be included in this study. The fold change data were analyzed to identify protein spots that demonstrated at least a 3.5-fold change in %volume for all 7 patients.

A correlation analysis was performed in the R environment v2.0.1 (14) with the difference in %volume (i.e., postmortem %volume--antemortem %volume) and the PMIs. The correlation analysis measures the correlation coefficient for each spot. The correlation coefficient, which can be from -1 to 1, is a measure of the strength of the relationship between the difference in %volume of that spot and the PMI. A positive coefficient indicates a positive relationship (i.e., as the PMI increases, the difference in %volume increases), and a negative coefficient indicates a negative relationship. The closer the absolute value of the coefficient is to 1, the greater the correlation between the 2 variables (15).

PROTEIN IDENTIFICATION

Some of the proteins in the 2DE spots of interest were identified with our 2DE CSF map on the basis of tandem mass spectrometry (MS/MS) identifications with P <0.05 (16). The protein spots that were not previously identified in this antemortem CSF map were identified by tryptic digestion followed by MS/MS (4700 Proteomics Analyzer, Applied Biosystems) by previously published methods (17). Peptide mass fingerprint data were collected in positive reflector mode from 900 to 4000 m/z. An MS/MS analysis was performed on several of the highest intensity nontrypsin peaks. The spectra were analyzed with GPS Explorer (v3.0, Applied Biosystems), which acts as an interface between the Oracle database containing raw spectra and a local copy of the Mascot search engine (v1.97, (18). The spectral data were submitted for a combined MS and MS/MS search against a local copy of the NCBInr sequence database (19). A mass tolerance of 25 ppm was used for the MS data and a relative molecular mass tolerance of 0.2 for the MS/MS data. A value of P <0.05 (calculated by GPS Explorer) was required for a protein identification to be considered correct.

Results

Examples of 2DE CSF gels from a study patient are shown in Fig. 1. The mean (SD) number of detected protein spots is 1233 (275) on the antemortem gels and 1462 (454) on the postmortem gels. A Student t-test analysis of the %volume data from all of the spots indicated that 310 protein spots showed a statistically significant (P <0.01) change in production between the antemortem and postmortem samples.

An analysis of the data identified 54 protein spots with a fold change [greater than or equal to] 3.5 for postmortem vs antemortem CSF in a117 patients. These spots are numbered and labeled with a circle in Fig. 2A. The labeled protein spots covered a wide range of relative molecular masses and isoelectric points. Significantly more spots had an increased %volume in the postmortem samples than had a decreased %volume (53 increased and 1 decreased). The spots with increased amounts ranged from those showing a mean 5-fold change to those detectable only in the postmortem samples. An example of 2 spots showing an increase in %volume in the postmortem CSF of a patient is shown in Fig. 2B. The proteins in many of the spots have been identified, and this information is summarized in Table 1.

[FIGURE 1 OMITTED]

The correlation analysis of the difference in %volume (postmortem minus antemortem) and PMI data identifies 284 protein spots with a correlation coefficient >0.6. When only the spots that showed a consistent increase in postmortem CSF across all patients were included, the list was decreased to 14 spots, which are identified in Table 2. This list contains several spots not in Table 1, because the spots did not show a fold change >3.5 in samples with a smaller PMI. These spots are labeled with a triangle in Fig. 2A. Examples of the change in %volume as a function of PMI are shown in Fig. 3 for several protein spots.

Discussion

The fold change analysis revealed 54 protein spots demonstrating a consistent change in CSF concentration upon death. One may speculate that spot changes at lower relative molecular masses may derive from intact proteins of a higher relative molecular mass and that the change in amount is a result of proteolytic degradation upon death. The data in Table 1 demonstrate that the observed proteins cover a broad range of relative molecular masses (12 000-65 000), and the observed and expected relative molecular masses based on an intact protein are similar for most of the entries. The majority of the proteins in Table 1 are intracellular proteins that are not expected in CSF, which is consistent with their release into the CSF upon cell death.

[FIGURE 2 OMITTED]

Several of the observations about proteins in Table 1 are consistent with previously published findings from postmortem CSF. For example, postmortem CSF has been reported to have an increase in the concentration of creatine kinase B (3). In the present study, the mean %volume of creatine kinase B was found to increase 12-fold in postmortem CSF compared with antemortem CSF from the same individual. The results of the current study also included several of the proteins that Lescuyer et al. (2) observed to increase in postmortem CSF compared with antemortem CSF in antemortem and postmortem CSF samples that were not obtained from the same cohort. These confirmed proteins include cathepsin D, nucleoside diphosphate kinase 1, fatty acid binding protein 5, oncogene DJ1, and peroxiredoxin 5.

Table 1 also includes proteins that have not been previously identified in the context of antemortem vs postmortem changes in CSF proteins. Several of these proteins, including stathmin 1, neuropolypeptide h3, and TPA:LRRC15, are known to be highly produced in the brain (20). Some of the proteins in Table 1 are involved in the glycolysis/carbohydrate metabolism pathways, and several other proteins are believed to be prevent oxidative stress. These 2 categories of proteins are altered after stroke-induced damage to the brain and after death.

[FIGURE 3 OMITTED]

Phosphoglycerate kinase, aldolase, pyruvate kinase, NADP-dependent isocitrate dehydrogenase, a-enolase, and malate dehydrogenase are involved in glycolysis or carbohydrate metabolism and are observed to be increased upon death. Previous investigations of the brain microdialysate have found that the concentration of glucose decreased and that of lactate increased with an occurrence of hypoxia/ischemia (21, 22). This observation is consistent with a change in energy metabolism in an injured brain, and postmortem changes in CSF proteins have been proposed as a model for studying brain injury (2). Experiments with cultured rat astrocytes have also demonstrated that hypoxia induces cells to increase their glycolytic capacity, leading to an increase in the concentration of enzymes such as pyruvate kinase (23).

Oxidative stress is also known to play a major role in the pathogenesis of brain injury (24). Some evidence exists that after cerebral ischemia, oxidative stress is a primary mediator of neurologic injury (25). Several of the identified proteins (peroxiredoxin 2, peroxiredoxin 5, carbonyl reductase 1, and quinonine oxioreductase 2) have been shown to play a role in protecting cells from oxidative stress (26-28). All of these proteins show an increase in the postmortem CSF sample compared with the antemortem samples.

The 2 other kinases identified in this study, creatine kinase B and nucleoside-diphosphate kinase 1, have previously been found to increase in concentration after stroke, and differences in CSF proteins from antemortem and postmortem samples have been previously proposed as a means to identify stroke biomarkers (2). Increases in nucleoside-diphosphate kinase 1 have been reported in blood (5), and increases in creatine kinase B have been reported in both blood (29) and CSF (30, 31).

The proteins in Table 1 that do not derive from these previously discussed categories include cytoskeletal proteins (actin [beta], CAPZ [beta] subunit, cofilin, and stathmin 1), transport-related proteins (calbindin 1, fatty acid binding protein 5, transthyretin, and tetranectin), proteolytic proteins (cathepsin D and proteasome subunit [alpha] type 5), and proteins with other or unknown functions (stress-induced phosphoprotein 1, peptidylprolyl isomerase A, oncogene DJ1, TPA:LRRC15, neuropolypeptide h3, esterase D, pyrophosphate phosphorhydrolase, carbonic anhydrase, and calmodulin 3).

In terms of correlating changes with PMI, previous studies have found that the CSF amounts of some enzymes increase with increasing PMI and hypothesize that this is because of progressive cellular breakdown (3). The 14 spots that showed a consistent increase in the postmortem CSF and a correlation score >0.6 are shown in Table 2. This table contains proteins from most of the previously discussed functional groups, and no one group is represented significantly more than the others. Among these, previous work demonstrates a correlation between increased CSF concentration of a-enolase and the volume of an infarction (32).

By performing a proteomic analysis on antemortem and postmortem CSF samples from 7 individuals, we identified several proteins that demonstrate altered CSF amounts upon death. A correlation analysis indicates that some of these CSF proteins have amounts that change with PMI. Taken together, these observations of the CSF protein changes may provide a better understanding of changes that occur upon death and that relate to PMI. Furthermore, it has been proposed that such changes may lead to validated biomarkers for stroke. However, additional studies of a larger cohort, ideally with antemortem and postmortem CSF samples from stroke patients, are necessary to confirm these observations in that context.

We thank the Institute for Brain Aging and Dementia Tissue Repository (University of California, Irvine, CA) for providing CSF samples and Leila Choe for important discussions. E.J.F. is supported by an American Association of University Women fellowship. We gratefully acknowledge support of this work by Cornell University; New York State Office of Science, Technology, and Academic Research; and NIH Grant MH59926.

References

(1.) Davson H, Welch K, Segal MB. The Physiology and Pathophysiology of the Cerebrospinal Fluid. New York: Churchill Livingstone, 1987:1013.

(2.) Lescuyer P, Allard L, Zimmermann-Ivol CG, Burgess JA, Hughes-Frutiger S, Burkhard PR, et al. Identification of post-mortem cerebrospinal fluid proteins as potential biomarkers of ischemia and neurodegeneration. Proteomics 2004;4:2234-41.

(3.) Paulson GW, Stickney D. Cerebrospinal fluid after death. Confin Neurol 1971;33:149-62.

(4.) Karkela JT. Critical evaluation of postmortem changes in human autopsy cisternal fluid. Enzymes, electrolytes, acid-base balance, glucose and glycolysis, free amino acids and ammonia. Correlation to total brain ischemia. J Forensic Sci 1993;38:603-16.

(5.) Allard L, Burkhard PR, Lescuyer P, Burgess JA, Walter N, Hochstrasser DF, et al. PARK7 and nucleoside diphosphate kinase A as plasma markers for the early diagnosis of stroke. Clin Chem 2005; 51:2043-51.

(6.) Zimmermann-Ivol CG, Burkhard PR, Le Floch-Rohr J, Allard L, Hochstrasser DF, Sanchez JC. Fatty acid binding protein as a serum marker for the early diagnosis of stroke: a pilot study. Mol Cell Proteomics 2004;3:66-72.

(7.) Carrette O, Demalte I, Scherl A, Yalkinoglu O, Corthals G, Burkhard P, et al. A panel of cerebrospinal fluid potential biomarkers for the diagnosis of Alzheimer's disease. Proteomics 2003;3:1486-94.

(8.) Puchades M, Hansson SF, Nilsson CL, Andreasen N, Blennow K, Davidsson P. Proteomic studies of potential cerebrospinal fluid protein markers for Alzheimer's disease. Mol Brain Res 2003; 118:140-6.

(9.) Hatzimanikatis V, Choe LH, Lee KH. Proteomics: theoretical and experimental considerations. Biotechnol Prog 1999;15:312-8.

(10.) Sommer JB, Gaul C, Heckmann J, Neundorter B, Erbguth FJ. Does lumbar cerebrospinal fluid reflect ventricular cerebrospinal fluid? A prospective study in patients with external ventricular drainage. Eur Neurol 2002;47:224-32.

(11.) Weisner B, Bernhardt W. Protein fractions of lumbar, cisternal, and ventricular cerebrospinal fluid. Separate areas of reference. J Neurol Sci 1978;37:205-14.

(12.) Reiber H. Dynamics of brain-derived proteins in cerebrospinal fluid. Clin Chim Acta 2001;310:173-86.

(13.) Conti A, Sanchez-Ruiz Y, Bachi A, Beretta L, Grandi E, Beltramo M, Alessio M. Proteome study of human cerebrospinal fluid following traumatic brain injury indicates fibrinogen) degradation products as trauma-associated markers. J Neurotrauma 2004;21:85463.

(14.) R Project. http://www.r-project.org (accessed July 5, 2005).

(15.) Glantz SA. Primer of Biostatistics. New York: McGraw-Hill Inc, 1987:230-97.

(16.) Finehout EJ, Franck Z, Lee KH. Towards two-dimensional electrophoresis mapping of the cerebrospinal fluid proteome from a single individual. Electrophoresis 2004;25:2564-75.

(17.) Finehout EJ, Lee KH. Comparison of automated in-gel digest methods for femtomole level samples. Electrophoresis 2003;24: 3508-16.

(18.) Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999;20:3551-67.

(19.) NCBInr protein database. http://www.ncbi.nlm.nih.gov/entrez/ query.fcgi?db=Protein&itool=toolbar (accessed July 9, 2005).

(20.) Swiss-Prot database. http://us.expasy.org/sprot/ (accessed July 9, 2005).

(21.) Hlatky R, Valadka AB, Goodman JC, Contant CF, Robertson CS. Patterns of energy substrates during ischemia measured in the brain by microdialysis. J Neurotrauma 2004;21:894-906.

(22.) Vespa P, Bergsneider M, Hattori N, Wu HM, Huang SC, Martin NA, et al. Metabolic crisis without brain ischemia is common after traumatic brain injury: a combined microdialysis and positron emission tomography study. J Cereb Blood Flow Metab 2005;25: 763-74.

(23.) Marrif H, Juurlink BH. Astrocytes respond to hypoxia by increasing glycolytic capacity. J Neurosci Res 1999;57:255-60.

(24.) Warner DS, Sheng H, Batinic-Haberle I. Oxidants, antioxidants and the ischemic brain. J Exp Biol 2004;207:3221-31.

(25.) Fiskum G, Rosenthal RE, Vereczki V, Martin E, Hoffman GE, Chinopoulos C, et al. Protection against ischemic brain injury by inhibition of mitochondrial oxidative stress. J Bioenerg Biomembr 2004;36:347-52.

(26.) Long DJ, Jaiswal AK. NRH: quinine oxidoreductase 2 (NQ02). Chem Biol Interact 2000;129:99-112.

(27.) Wood ZA, Schroder E, Harris JR, Poole LB. Structure, mechanism and regulation of peroxiredoxins. Trends Biochem Sci 2003;28: 32-40.

(28.) Wermuth B, Platts KL, Seidel A, Oesch F. Carbonyl reductase provides the enzymatic basis of quinine detoxification in man. Biochem Pharmacol 1986;35:1277-82.

(29.) Capocchi G, Tassi C, Ricci S, Zampolini M, Fausti R, Rossi A. Creatine kinase BB activity in serum of patients with acute stroke: correlation with the severity of brain damage. Ital J Neurol Sci 1987;8:567-70.

(30.) Donnan GA, Zapf P, Doyle AE, Bladin PF. CSF enzymes in lacunar and cortical stroke. Stroke 1983;14:266-9.

(31.) Vaagenes P, Urdal P, Melvoll R, Valnes K. Enzyme level changes in the cerebrospinal fluid of patients with acute stroke. Arch Neurol 1986;43:357-62.

(32.) Hay E, Royds JA, Davies-Jones GA, Lewtas NA, Timperley WR, Taylor CB. Cerebrospinal fluid enolase in stroke. J Neurol Neurosurg Psychiatry 1984;47:724-9.

ERIN J. FINEHOUT, [1] ZSOFIA FRANCK, [1] NORMAN RELKIN, [2] and KELVIN H. LEE [1] *

[1] School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY; [2] Department of Neurology and Neuroscience, Cornell University Medical College, New York, NY.

[3] Nonstandard abbreviations: CSF, cerebrospinal fluid; 2DE, 2-dimensional gel electrophoresis; PMI, postmortem interval; volume, percent integrated intensity; MS/MS, tandem mass spectrometry.

* Address correspondence to this author at: 120 Olin Hall, Ithaca, NY 14853. Fax 607-255-9166; e-mail: KHL9@cornell.edu.

Received March 21, 2006; accepted June 21, 2006.

Previously published online at DOI: 10.1373/clinchem.2006.070508
Table 1. Proteins identified in spots showing a consistent-fold change
>3.5 when comparing postmortem and antemortem CSF. (a)

 NCBInr
Spot no. Protein ID accession no. [DELTA]

 1 Stress induced phosphoprotein 1 5803181 +
 2 Pyruvate kinase 478822 +
 3 [alpha] enolase 29792061 +
 4 Creatine kinase B 49457530 +
 5 No ID +
 6 Actin [beta] 14250401 +
 7 No ID +
 8 NADP-dependent isocitrate 3641398 +
 dehydrogenase
 9 Phosphoglycerate kinase 48145549 +
 10 No ID +
 11 No ID +
 12 Aldolase A 4930291 +
 13 Aldolase A 4930291 +
 14 [alpha]-Enolase 29792061 +
 15 No ID +
 16 No ID +
 17 Malate dehydrogenase 7431153 +
 18 Carbonyl reductase 1 4502599 +
 19 No ID +
 20 Esterase D 33413400 +
 21 Pyrophosphate phospho-hydrolase 33636766 +
 22 No ID +
 23 Calbindin 1 4826655 +
 24 CAPZ B protein 19352984 +
 25 No ID +
 26 Cathepsin D, chain H 5822091 +
 27 Cathepsin D, chain H 5822091 +
 28 Carbonic anhydrase II 809316 +
 29 No ID +
 30 Proteasome subunit, [alpha] 53734656 +
 type, 5
 31 No ID +
 32 No ID +
 33 NRH: quinone oxidoreductase 2 5822494 +
 B-chain
 34 TPA: LRRC15 34146792 +
 35 No ID +
 36 Oncogene DJ1 31543380 +
 37 No ID +
 38 No ID +
 39 Peroxiredoxin 2 33188452 +
 40 Peroxiredoxin 2 2507619 +
 41 Neuropolypeptide h3 4261934 +
 42 Neuropolypeptide h3 4261934 +
 43 Tetranectin 4507557 -
 44 Nucleoside-diphosphate kinase 1 38045913 +
 45 No ID +
 46 Stathmin 1 51895905 +
 47 Cofilin (nonmuscle) 30582531 +
 48 Peptidylprolyl isomerase A 51895760 +
 49 Peroxiredoxin 5, chain H 16975162 +
 50 Transthyretin 339685 +
 51 Fatty acid binding protein 5 30583737 +
 52 Calmodulin 3 58218968 +
 53 No ID +
 54 No ID +

 Observed Calculated
 [M.sub.r] [M.sub.r] Mascot
Spot no. (in thousands) (in thousands) score

 1 65 63 346
 2 63 58 346
 3 50 47 494
 4 49 43 74
 5 48
 6 46 42 439
 7 44
 8 44 47 230
 9 44 45 362
 10 43
 11 40
 12 40 39 466
 13 40 39 486
 14 39 47 163
 15 39
 16 37
 17 35 36 101
 18 32 30 465
 19 34
 20 32 31 101
 21 32 29 72
 22 32
 23 30 30 220
 24 30 21 107
 25 31
 26 29 26 90
 27 28 26 181
 28 27 29 503
 29 25
 30 25 26 96
 31 25
 32 25
 33 24 26 151
 34 23 64 113
 35 22
 36 22 20 274
 37 22
 38 21
 39 21 16 432
 40 -- (b)
 41 21 16 37i
 42 20 16 332
 43 -- (b)
 44 18 20 230
 45 17
 46 17 17 31i (c)
 47 16 19 206
 48 15 18 192
 49 15 17 232
 50 14 13 36i
 51 13 15 132
 52 14 17 146
 53 13
 54 12

(a) The spot number corresponds to the label in Fig. 2A. The direction
of change ([DELTA]) is shown as is the approximate observed [M.sub.r],
the calculated [M.sub.r], the Mascot MOWSE score, and the NCBInr
accession number (19). A positive [DELTA] indicates the expression of
the protein was greater in the postmortem CSF. Protein identifications
are based on a combined MS and MS/MS search except where noted. ID,
identification.

(b) Identification made using previously published 2DE CSF map (16).

(c) i indicates that score is based only on MS/MS data.

Table 2. Spots identified to have a change in %volume that correlated
with the PMI. (a)

Spot no. Protein ID NCBInr Correlation
 accession no. coefficient

 3 [alpha]-Enolase 29792061 0.80
 17 Malate dehydrogenase 7431153 0.74
 40 Peroxiredoxin 2 33188452 0.90
 47 Cofilin (nonmuscle) 30582531 0.85
 53 No ID 0.67
 55 [alpha]-Enolase 29792061 0.62
 56 No ID 0.62
 57 Glyoxylate reductase 6912396 0.92
 58 No ID 0.85
 59 Haloacid dehalogenase-like 14149777 0.69
 hydrolase domain
 60 No ID 0.70
 61 No ID 0.60
 62 Proteasome subunit, 30583113 0.73
 [beta] type 2
 63 No ID 0.79

(a) The spot number corresponds to the labels in Fig. 2A. ID,
identification.
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Title Annotation:Proteomics and Protein Markers
Author:Finehout, Erin J.; Franck, Zsofia; Relkin, Norman; Lee, Kelvin H.
Publication:Clinical Chemistry
Article Type:Clinical report
Date:Oct 1, 2006
Words:4262
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