Comparative proteomic analysis of brains of naturally aging mice

Neuroscience 154 (2008) 1107–1120

COMPARATIVE PROTEOMIC ANALYSIS OF BRAINS OF NATURALLY AGING MICE S. YANG,a1 T. LIU,a1 S. LI,a X. ZHANG,a Q. DING,a H. QUE,a X. YAN,a K. WEIb AND S. LIUa*

ning in their sixties, while others may live for a century or more with little or no evidence of neuronal degeneration. The cellular and molecular mechanism underlying this difference has long been the focus of studies on brain aging. Successful neural aging has been attributed to success in responding adaptively to age-related changes, whereas neurodegenerative diseases were thought to indicate failure (Mattson and Magnus, 2006). Many mechanisms that focus on particular aspects have been proposed for potential initiation and promotion of brain aging (Raz and Rodrigue, 2006). However, most of them were derived from various neural diseases. In addition, a complex biological process like brain senescence is unlikely to be governed solely by a single mechanism. Rather, coordinated interactions of a multiplicity of mechanisms may regulate brain aging. Therefore, the integration of multiple mechanisms has become the new challenge in understanding the natural aging process of the brain and providing a useful background to study neurodegenerative diseases. Proteome analysis, consisting of advanced separation techniques, mass spectrometry (MS) and bioinformatics tools, is a valuable approach to monitor protein variations and identify new target proteins, leading to the comprehensive understanding of integrated complex biological processes. In recent years, several proteomics studies have described variations of certain proteins in adult rodent and human brains upon aging (Fountoulakis et al., 2000; Tsugita et al., 2000; Chen et al., 2003; Sato et al., 2005). However, this is just the beginning. The brain is the most complex organ in mammals, both in the variability of the cell types and in the number of circulating macromolecules. Therefore, characterization of the proteomes in the naturally aging brain is an enormous and challenging task. In late 2003, the Human Proteome Organization launched pilot studies of the Brain Proteome Project to analyze proteome changes of the brain during aging, in which the brains used were from mice only 7 days and 8 weeks old (Hamacher et al., 2006). The data were far from enough to interpret the complicated mechanism of brain senescence, and much more data remain to be revealed for a thorough insight into the process. To identify new protein targets underlying the natural aging of the brain, we undertook a high-resolution differential proteomic analysis of the brains of mice aged 4 days (neonate), 3 months (young adult), 6 months (mature adult), 12 months (middle age) and 15 months (senile). With the identified proteins by matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, this study should help to explore the intricate aging mechanism of the brain.

a

State Key Laboratory of Proteomics, Department of Neurobiology, Institute of Basic Medical Sciences, 27 Taiping Road, Beijing 100850, PR China

b

National Center of Biomedical Analysis, Beijing 100850, 27 Taiping Road, PR China

Abstract—We used comparative proteomic techniques to identify aging-related brain proteins in normal mice from neonate to old age. By 2-dimensional electrophoresis (2-DE), matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) and peptide mass fingerprint (PMF) analysis, 39 proteins were identified, among which 6 stayed unchanged since 3 months, 6 increased and 27 decreased in various manners during aging. They are mainly involved in processes usually with destructive changes during aging, such as metabolism, transport, signaling, stress response and apoptosis. The 27 proteins’ decrease may be responsible for brain aging. In particular, decrease of proteasome alpha subunits 3/6, ubiquitin carboxyl-terminal esterase L3, valosin-containing protein and calreticulin may be responsible for the declination of protein quality control; glutamate dehydrogenase 1, isocitrate dehydrogenase 1 and ubiquinol cytochrome c reductase core protein 2 for the shortage of energy and reducing agent; ubiquitin-conjugating enzyme E2N and heterogeneous nuclear ribonucleoprotein A2/B1 for the increase of DNA damage and transcription detuning; calbindin 1 and amphiphysin for the disturbance of synaptic transport and ion signals. The six proteins’ increase may be involved in anti-aging processes. In particular, transketolase, mitochondrial creatine kinase 1 and ribosomal protein L37 may help to enhance energy metabolism; triosephosphate isomerase 1 may help to resist oxidative stress. Moreover, most of these proteins were found for the first time to be involved in the natural senescence of brain, which would provide new clues about the mechanism of brain aging. © 2008 IBRO. Published by Elsevier Ltd. All rights reserved. Key words: mouse, senescence, CNS, mass spectrometry, proteomics.

Everyone experiences different degrees of brain aging; some people develop neurodegenerative disorders begin1

These authors contributed equally. *Corresponding author. Tel: ⫹86-10-66931304; fax: ⫹86-10-68213039. E-mail address: [email protected] (S. Liu). Abbreviations: ACTH, adrenocorticotrophic hormone; CALB1, calbindin 1; CHAPS, 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate; CV, coefficient of variation; DHAP, dihydroxyacetone phosphate; ER, endoplasmic reticulum; GO, gene ontology; IEF, isoelectric focusing; IPG, immobilized pH gradient; MALDI-TOF, matrix assisted laser desorption/ionization time-of-flight; MS, mass spectrometry; PMF, peptide mass fingerprint; PSMAs, proteasome alpha subunits; TCA, trichloroacetic acid; TRF, transferrin; 2-DE, 2-dimensional electrophoresis.

0306-4522/08$32.00⫹0.00 © 2008 IBRO. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.neuroscience.2008.04.012

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S. Yang et al. / Neuroscience 154 (2008) 1107–1120

EXPERIMENTAL PROCEDURES Mice brain aging model Six neonatal male Kunming mice (animal center of Academy of Military Medical Sciences of China, Beijing, PR China) aged 4 days were killed as neonate brain models. Four groups of three male Kunming mice were raised to 3, 6, 12 and 15 months old respectively and then killed as young adult, mature adult, middle age and old age brain models separately. Every effort was made to minimize the number of animals used and their suffering. The animal subjects review board of our institute approved all the experiment procedures, which were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals revised 1996.

Sample preparation A trichloroacetic acid (TCA)/acetone precipitation protocol was adopted to prepare protein extracts from mice brain. The protocol was based on the work of Damerval et al. (1986) with some modifications. Briefly, fresh material was scissored into small pieces on ice, homogenized mechanically in the presence of ice-cold acetone (Sigma, St. Louis, MO, USA)/10% TCA (Sigma)/ 0.2% dithiothreitol (Promega, Madison, WI, USA), and then sonicated for 1 min. The homogenate was precipitated overnight at ⫺20 °C. After centrifugation at 40,000⫻g for 30 min at 4 °C, the supernatant was removed by decanting immediately and the pellet was rinsed twice in ice-cold acetone (Sigma)/0.2% dithiothreitol (Promega). The pellet was then air-dried, resuspended in a lysis buffer containing 7 M urea (Sigma), 2 M thiourea (Sigma), 4% 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS; Sigma), 1% dithiothreitol (Promega) and 0.5% carrier ampholytes (pH 3–10; Sigma). In order to avoid protein degeneration, a cocktail of protease inhibitors (0.7 ␮g/mL aprotinin (Sigma), 0.5 ␮g/mL leupeptin (Sigma), 0.3 mg/mL EDTANa2 (Sigma) and 35 ␮g/mL phenylmethanesulphonylfluoride (Sigma)) was added into the lysis buffer. After centrifugation at 40,000⫻g for 30 min at 15 °C, the supernatant was collected. The protein concentration was determined by the Bradford assay (Bradford, 1976) on a Unicam UV-330 spectrometer (Unicam, Cambridge, UK). The protein extracts were then stored at ⫺80 °C. When the protein samples for all five age groups were ready, they were subjected to two-dimensional electrophoresis (2-DE) simultaneously.

2-DE and image analysis A 2-DE protocol was followed as described previously (Gorg et al., 2000; Zhao et al., 2000). Briefly, 400 ␮g and 2 mg of protein were loaded on immobilized pH gradient (IPG) gel strip holders with the in-gel rehydration mode for analytical and micropreparative purposes respectively. Using the IPGphor isoelectric focusing (IEF) system (Pharmacia Biotech, San Francisco, CA, USA), the IPG gels (pH3-10L, 24 cm; Amersham Biosciences, San Francisco, CA, USA) were rehydrated for 12 h under 30 V at 20 °C. For analysis, IEF was performed using the following parameters: 200 V for 1 h, 500 V for 1 h, 1000 V for 1 h, 8000 V (gradient) for 0.5 h, and finally 8000 V for a total of 35 kVh. For micropreparation, IEF was carried out for 50 kVh. After IEF, the IPG strips were equilibrated with an equilibration buffer (50 mM Tris–HCl pH8.8, 6 M urea (Sigma), 30% glycerol, 2% sodium dodecyl sulfate, 0.002% Bromophenol Blue) containing 1% dithiothreitol (Promega) for 15 min and then with the equilibration buffer with dithiothreitol replaced by 2.5% iodoacetamide (Sigma) for another 15 min. Then the vertical sodium dodecyl sulfate–polyacrylamide gel electrophoresis was performed using the Ettan DALT II system (Amersham Pharmacia Biotech AB, San Francisco, CA, USA) with laboratory-made homogeneous acrylamide gel [13%T (percentage concentration of acrylamide and methylene bisacrylamide in

gel), 3%C (percentage concentration of methylene bisacrylamide in acrylamide and methylene bisacrylamide)]. Protein spots in the micropreparative gels were stained with Coomassie Brilliant Blue R250 (Sigma). As for the analytical gels, protein spots were stained with silver for clearer visualization. Due to the complexity and extensive protein–protein and protein–lipid interactions, proteins in the CNS tissues are extraordinarily resistant to isolation, which may result in horizontal streaking in the gels. To identify a maximum number of detected spots as well as minimize the number of spots that arise from gel artifacts, images were first analyzed using the ImageMasterTM 2D Platinum 5.0 software (Amersham Biosciences) according to the automatic protocols provided by the manufacturer. However, because of the horizontal streaking, multiple spots close together may be detected as one spot by the software. In this case, these close spots were distinguished carefully by manual spot editing. The spot quantification for an analytical gel was calculated as a relative volume, which was the volume of each spot divided by the total volume of all spots of the gel. In this way, differences in sample loading and color intensities among the gels were eliminated. To ensure the reliability, samples were pooled for each age group to run three analytical gels. For each group, quantification of a spot was calculated as the average of its relative volumes on the three analytical gels. Thirteen protein spots typically distributed on the 2-D gels (upper left and upper right, lower left and lower right, and central regions) were selected to assess the coefficient of variation (CV) of their relative volumes within a group. The result indicated that the average CV for experimental relative volume was 10% (⫾3%). Based on this data, a protein expression ratio of less than 1.15 could be empirically considered as an experimental variation; in other words, only a 1.15-fold change (or greater) of protein expression was regarded as a biological difference in this experiment.

In-gel digestion, peptide purification and concentration, MALDI-TOF MS Most of the spots were selected away from the streaking if there was any, in order to eliminate its influence on protein separation and identification. In addition, for the several spots with some extent of streaking, only about 40% of the core part of the spot (represented by the white circle in Fig. 3A) was cut for MS analysis. The excised micropreparative gel plugs of selected spots were destained in 50 mM NH4HCO3/acetonitrile (50:50) and dried by vacuum centrifugation using a Savant SpeedVac SC110A Concentrator System (GMI, Ramsey, MN, USA). Thereafter, modified sequence-grade bovine trypsin (Promega) (10 ng/␮L) dissolved in 50 mM NH4HCO3 digestion buffer was added to the dry gel pieces and incubated on ice for 1 h for re-swelling and then incubated at 37 °C in a water bath for 18 h. The peptide mixture was then concentrated by vacuum centrifugation and purified to remove detergents like sodium dodecyl sulfate, CHAPS and salts using ZipTip C18 (Millipore, Billerica, MA, USA). After being eluted in matrix solution (0.5% trifluoroacetic acid (ACROS, NJ, USA), 50% acetonitrile and 20 mg/mL ␣-cyano-4-hydroxy cinnamic acid (Sigma)), peptide mixtures were deposited on the stainless steel MALDI probe to dry slowly at ambient temperature. MS was performed using a Bruker Daltonics autoflex MALDI-TOF-MS (Bruker Daltonics, Bremen, Germany). Mass spectra were detected in the reflectron mode and recorded by the flexControl v2.4 software (Bruker Daltonics) with default parameters unless specified. Monoisotopic peptide masses were labeled by the Xmass v5.1.1 software (Bruker Daltonics) with default parameters unless specified. The average mass accuracy was less than 0.2 Da in the mass scanning range and the resolution was about 15,000. All spectra were externally calibrated by Peptide Calibration Standard (Bruker Daltonics), which was composed of angiotensin II (MH⫹ 1046.5418), angiotensin I (MH⫹ 1296.6848), substance P (MH⫹ 1347.7354), bombesin (MH⫹ 1619.8223), adrenocorticotrophic

S. Yang et al. / Neuroscience 154 (2008) 1107–1120 hormone (ACTH) clip 1–17 (MH⫹ 2093.0862), ACTH clip 18 –39 (MH⫹ 2465.1983) and somatostatin 28 (MH⫹ 3147.4710). Contaminants were excluded using the PeakClean v2.1 software (http://www.proteomics.cn/PeakClean/). The most frequently seen contaminant peptides were derived from keratin and trypsin autodigestion peptides (Ding et al., 2003). Under user-defined mass accuracy (⬍0.2 Da), 17 contaminants and auto-digestion peaks were removed before the database search.

Database search Protein identification by MS data was accomplished using the Mascot search engine (http://www.matrixscience.com/cgi/search_ form.pl?FORMVER⫽2& SEARCH⫽PMF) with the Rodentia category of the nr database of the National Center for Biotechnology Information searched. The enzyme used for protein digestion was trypsin. The number of allowed missed cleavage sites was set to 1. Peptide modifications included partial oxidation of methionine and partial carbamidomethylation of cysteine. The experimental peptide mass values in a peptide mass fingerprint (PMF) search included the mass of the charge carrier, MH⫹. The error window on experimental peptide mass values was 0.3 Da. A minimum of four matched peptide masses was required to identify a protein. Only protein identifications with scores greater than P⬍0.05 (default threshold) were considered to be positive. As the nr database of the National Center for Biotechnology Information is not a non-redundant database, a protein may appear in the form of the whole and/or fragmental sequence under different names and accession numbers. However, for each protein, this kind of redundancy was recorded in the corresponding unique gene entry encoding for that protein in the standard gene database at the National Center for Biotechnology Information. So, each identified candidate protein entry was traced to its corresponding gene entry through the “GeneID” hyperlink or by the basic local alignment search tool if the hyperlink was not available. Then, the redundant candidates were removed and all identified proteins were referred to by their official names in the gene database. Furthermore, to ensure the identification of a specific protein family member/isoform, the identified peptides unique to it were checked.

Western blot Commercial antibodies were available only for few of the identified proteins. Out of an economic consideration, calbindin 1 (CALB1), proteasome alpha subunits 3 (PSMA3) and 6 (PSMA6) were selected as representatives for further confirmation of changing patterns by Western blot. Briefly, 40 ␮g of each protein sample was separated by a 10% polyacrylamide gel and electrotransferred to a nitrocellulose membrane in a Trans-Blot® Semi-dry Electrophoretic Transfer Cell (Bio-Rad, Hercules, CA, USA). Nonspecific bands were blocked in TBS-T (25 mM Tris (Sigma), 150 mM NaCl, 0.05% Tween20, pH 7.5) containing 5% skimmed milk overnight at 4 °C. Membranes were subsequently incubated with the primary antibody of ␤-tubulin (Sigma) (1:8000) along with those of CALB1 (Sigma) (1:1000) or PSMAs (Merck KGaA, Darmstadt, Germany) (1:1000) at room temperature for 1 h and followed by anti-mouse or anti-rabbit IgG horseradish peroxidase conjugate. The immunocomplexes were visualized by chemiluminescence using the ECL kit (Amersham Pharmacia Biotech AB). The film signals were digitally scanned and then quantified using the ImageMasterTM 2D Platinum 5.0 software (Amersham Biosciences). For the neonate group, the individual samples of the six mice were randomized into three groups; samples of each group were pooled for individual assay. For other age groups, individual samples were used for assay. The volumes of target bands were normalized to ␤-tubulin. Protein expression ratio to neonate was calculated for each animal. For statistical analysis, the analysis of variance (ANOVA) with Tukey’s post hoc multiple comparisons

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was applied for comparison of the ratios among groups. Significant difference was recognized as a P value less than 0.05.

Gene ontology (GO) annotations The proteins identified in this study were classified by GoMinerTM software (Zeeberg et al., 2003) in combination with the GO database released in June 2007. The proteins are assigned in GO terms, which rely on a controlled vocabulary for describing a protein in terms of its molecular function, biological process, or subcellular localization.

RESULTS Comparative proteomic analysis of the naturally aging mice brain The representative 2-DE patterns of neonatal, young adult, mature adult, middle-aged and old mice brains are displayed in Fig. 1. In the five maps, most of the proteins are distributed in an area of pI 4.0 – 8.0 and molecular weight 14 –97 kDa (Fig. 1). Comparing the different age groups, 60 protein spots with aging-accompanied variations were submitted to MALDI-TOF MS assay. Fig. 2 shows a magnified comparison of the patterns of spot 57 (Fig. 2A), spot 39 (Fig. 2B) and spot 12 (Fig. 2C), which exemplify several types of time courses for concentration levels during the aging process. Identification of proteins by MALDI-TOF MS and PMF Among the 60 protein spots, 40 were identified successfully with the PMF method; they are listed with their agerelated change patterns in Table 1. For each of the protein family members identified in this study, there were no other members appearing in the PMF search result under our criteria. As illustrated in supplemental Fig. S1, protein family members can be separated from other members by their unique peptides identified in this study, which are listed in supplemental Table S1. Spots 17 and 18 were both identified as transferrin (TRF) with higher experimental molecular weights than the theoretical value, which indicated there may be some posttranslational modification of TRF. Validation of differentially expressed proteins by Western blot analysis Representatively, the protein expression patterns of CALB1, PSMA3 and PSMA6 observed in 2-DE gels were further validated by Western blot analysis. As shown in Fig. 3, the variation of CALB1 was again confirmed by Western blot (P⬍0.05). The expression of CALB1 was up-regulated immediately from 3 to 6 months after the neonatal age and then down-regulated gradually from 12 to 15 months old. As for the experiment of PSMA3 and PSMA6, three bands were detected by anti-PSMAs antibody (Fig. 4C). The antibody against PSMAs recognizes each of the seven PSMA subunits with similar sizes, which makes it hard to distinguish them completely from each other just by Western blot. However, according to their molecular weights, PSMAs can be clustered into three groups.

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Fig. 1. Comparison of 2-DE patterns of mice brains in different natural aging stages. (A) Four days; (B) 3 months; (C) 6 months; (D) 12 months; (E) 15 months. Sixty silver-stained protein spots were found to vary in a significant way during the natural aging process of mice brain.

PSMA1 and PSMA4 belong to a group with a relatively higher molecular weight of 29.5 kDa. PSMA2 and PSMA5 belong to a group with relatively lower molecular weights of 26.4 and 25.8 kDa respectively. PSMA3, PSMA6 and PSMA7 belong to the third group, with medium molecular weights of 28.3, 27.4 and 27.9 kDa respectively. So, the high molecular weight band in the gel may represent PSMA1 and PSMA4, the medium one may represent PSMA3, PSMA6 and PSMA7, and the low one may represent PSMA2 and PSMA5. Furthermore, PSMA7 has a

higher basic pI value of 8.59, which retards its movement in the gel, whereas PSMA3 and PSMA6 have lower acidic pI values of 5.29 and 6.35 respectively, which promote their migration in the gel. Consequently, PSMA7 was most likely merged into the band of PSMA1 and PSMA4. Consistent with the analysis above, the density of the medium PSMA band showed the similar variation pattern (P⬍0.05) as that of the sum of PSMA3 and PSMA6 in the 2-DE gels (Fig. 4D), except for the higher P value (0.87) of 12 months group vs. 15 months group.

S. Yang et al. / Neuroscience 154 (2008) 1107–1120

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Fig. 2. Magnified comparison maps of spot 57 (A), spot 39 (B), and spot 12 (C) in the 2-DE patterns of different natural aging stages. Spot 57 was up-regulated gradually in the brain as the mouse age increased. Spot 39 emerged in 3 months, reached the highest level in 6 months, and then decreased gradually to an almost undetectable level in 15 months. The content of spot 12 was shown to decrease gradually after the neonatal stage.

GO of the identified proteins To obtain an overview of the brain aging proteome, the identified proteins were classified with respect to their subcellular localization (Fig. 5A), molecular function (Fig. 5B) and biological processes (Fig. 5C) respectively. Due to the multiple localizations, molecular functions and biological processes of some of these proteins, the results of this ontological analysis are shown as bar charts instead of pie charts. The majority of the identified proteins are intracellular proteins (94.9%), among which most are distributed in the nucleus (30.8%), cytosol (25.6%), mitochondrion (20.5%), cytoskeleton (17.9) and endoplasmic reticulum (ER) (12.8%). Of the identified proteins, the largest fraction (89.7%) belongs to binders for other molecules, among which most are specific binders for other proteins (53.8%), and the other are binders for ions (25.6%), nucleotides (25.6%), nucleic acid (23.1%) and other molecules (25.6%). More than half (56.4%) have catalytic activity with a predominant enrichment of hydrolases (28.2%) and oxidoreductases (17.9%). Significantly enriched terms of the “biological process” ontology include the “protein metabolic process” (38.5%), “nucleotide and nucleic acid metabolic process” (25.6%), “transport” (33.3%), “intracellular signaling cascade” (17.9%), “response to stress” (15.4%), “apoptosis” (12.8%) and “nervous system development” (12.8%). The detailed functions of these identified proteins were also summarized and listed in supplemental Table S2.

DISCUSSION The natural aging of the brain is an intricate process in which almost every neural cell and numerous molecules are involved. During aging, cells in the brain experience various destructive changes, including increased amounts

of oxidative stress, metabolic impairment, perturbed energy and ion homeostasis, mitochondrial instability, accumulation of damaged proteins and lesions in their nucleic acids. An individual was thought to have achieved a successful aging period by counteracting the fundamental molecular and cellular mechanisms of these negative changes (Mattson and Magnus, 2006). However, because of the cellular and molecular complexity of the nervous system, many molecules involved in the normal aging process remain to be discovered, and the detailed mechanisms underlying this counteraction remain elusive. Over the past few years, some proteomics studies have described variations of certain proteins in mouse or rat brains in the period from neonate to young (Carrette et al., 2006; Focking et al., 2006; Frohlich et al., 2006; Seefeldt et al., 2006; Stühler et al., 2006) or mature adult (Fountoulakis et al., 2000). In this experiment, we employed proteomic techniques to identify differentially expressed brain proteins from normal mice at various ages from neonate through old age. And their expression patterns obtained by 2-DE were further confirmed selectively by Western blotting. The identified proteins showed various changing patterns during the aging process. Among them, 6 stayed unchanged since 3 months old, 6 increased and 27 decreased in various manners as compared between the adult and elderly stages. In some former studies (Carrette et al., 2006; Focking et al., 2006; Seefeldt et al., 2006), female C57BL/6 mice were used for analysis, whereas in our study male Kunming mice were used. However, in another study on rat brain proteome, sexrelated differences were not detected at the mature adult stage of 8 months old (Fountoulakis et al., 2000). Although the species, sex and age of animals, and the experimental conditions used in these former studies differ from ours, 19 proteins observed in the former studies were also identified in our study. From neonate to adolescence or maturity, the down-regulation of CALR (Fountoulakis et al., 2000),

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Table 1. Proteins identified by MALDI-TOF MS and their expression variations in mouse brain from neonate to old age No.a

Protein nameb

GIc

Massesd

SeqCove

Scoref

1

HSP90B1 (heat shock protein 90 kDa beta, member 1)

14714615

10/14

14%

97/38

2

CALR (calreticulin)

50568

6/11

25%

73/37

4

SET (SET translocation)

3953617

4/6

26%

79/40

5

TPM3 (tropomyosin 3, gamma)

62027399

8/11

28%

100/52

6

YWHAE (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon polypeptide)

31981925

7/17

29%

79/30

7

UCHL3 (ubiquitin carboxyl-terminal esterase L3)

7578956

4/5

22%

84/43

9

RPLP2 (ribosomal protein, large P2)

46397855

4/4

25%

79/43

11

STMN1 (stathmin 1)

12832714

6/10

36%

80/34

14

PPA1 (pyrophosphatase (inorganic) 1)

26353394

4/4

17%

68/40

17

TRF (transferrin)

14250269

10/11

24%

120/45

18

TRF (transferrin)

17046471

11/12

20%

129/45

20

IDH1 (isocitrate dehydrogenase 1 (NADP⫹), soluble)

6647554

21/29

43%

194/36

21

PSMD14 (proteasome 26S subunit, non-ATPase, 14)

12848428

4/10

30%

64/28

23

PSMA6 (proteasome subunit, alpha type 6)

6755198

5/8

20%

70/32

24

PAFAH1B3 (platelet-activating factor acetylhydrolase, isoform 1b, alpha1 subunit)

44890813

5/11

18%

79/54

26

UBE2N (ubiquitin-conjugating enzyme E2N)

12838544

8/12

51%

131/43

Variationg

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Table 1. continued No.a

Protein nameb

GIc

Massesd

SeqCove

Scoref

27

TTR (transthyretin)

19354093

4/6

39%

86/38

31

NME1 (expressed in non-metastatic cells 1, protein)

13542867

7/10

60%

145/39

32

TKT (transketolase)

6678359

7/9

19%

125/40

34

CFL1 (cofilin 1, non-muscle)

6680924

6/11

42%

97/29

35

AMPH (amphiphysin)

32451969

12/18

20%

125/48

36

NEF3 (neurofilament 3, medium)

74228116

17/21

35%

184/51

38

CALB1 (calbindin 1)

26347175

5/5

20%

86/50

39

OMP (olfactory marker protein)

15826764

4/7

34%

64/36

42

PSMA3 (proteasome subunit, alpha type 3)

74224914

10/15

31%

121/54

43

NDUFS8 (NADH dehydrogenase Fe-S protein 8)

56540975

7/15

44%

67/43

44

MAPK9 (mitogen activated protein kinase 9, isoform b)

56205899

8/19

25%

66/45

45

LONP1 (mitochondrial lon peptidase 1)

26984237

6/11

7%

68/46

47

DNM1 (dynamin 1)

487851

13/24

22%

144/43

50

UQCRC2 (ubiquinol cytochrome c reductase core protein 2)

26346450

13/15

32%

177/40

51

HNRPA3 (heterogeneous nuclear ribonucleoprotein A3)

38328278

7/15

26%

80/n/a

52

MDH2 (mitochondrial malate dehydrogenase 2)

387422

7/17

29%

103/40

Variationg

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Table 1. continued No.a

Protein nameb

GIc

Massesd

SeqCove

Scoref

53

HNRPA2B1 (heterogeneous nuclear ribonucleoprotein A2/B1 isoform 1)

7949053

13/27

53%

148/36

54

VDAC1 (voltage-dependent anion channel 1)

74212025

5/21

30%

70/38

55

GLUD1 (glutamate dehydrogenase 1)

30931187

13/18

27%

132/43

56

EIF5A (eukaryotic translation initiation factor 5A)

56800106

5/8

48%

70/42

57

TPI1 (Triosephosphate isomerase 1)

28436836

9/19

44%

112/48

58

MRPL37 (mitochondrial ribosomal protein L37)

20380947

9/17

23%

68/44

59

CKMT1 (ubiquitous mitochondrial creatine kinase 1)

663017

13/19

33%

179/40

60

VCP (valosin-containing protein)

74139564

26/32

39%

256/37

Variationg

a

Spot numbers were defined according to spot positions in 2-DE gels. The protein names and symbols were provided by the Mouse Genomic Nomenclature Committee (MGNC). c The GI numbers are a series of digits that were assigned consecutively by the NCBI to each sequence. d Number of matched mass values/number of total mass values searched. e The sequence coverage, which is calculated as the percentage of identified sequence to the complete sequence of the matched protein. f Mowse score for the identified protein/Mowse score for the highest ranked hit to a non-homologous protein; the Mowse score is ⫺10*Log (P), where P is the probability that the observed match is a random event. g Expression variation in protein level from neonate to old age. b

EIF5A (Seefeldt et al., 2006; Stühler et al., 2006), IDH1 (Carrette et al., 2006; Frohlich et al., 2006), PSMA6 (Stühler et al., 2006), PSMD14 (Stühler et al., 2006), STMN1 (Fountoulakis et al., 2000; Carrette et al., 2006; Seefeldt et al., 2006; Stühler et al., 2006), TPM3 (Focking et al., 2006; Stühler et al., 2006), TTR (Focking et al., 2006; Stühler et al., 2006), UCHL3 (Stühler et al., 2006) and YWHAE (Fountoulakis et al., 2000; Focking et al., 2006), as well as the up-regulation of CKMT1 (Stühler et al., 2006), DNM1 (Fountoulakis et al., 2000), GLUD1 (Fountoulakis et al., 2000; Stühler et al., 2006), MDH2 (Frohlich et al., 2006), TPI1 (Fountoulakis et al., 2000; Carrette et al., 2006; Frohlich et al., 2006) and VDAC1 (Frohlich et al., 2006; Stühler et al., 2006), was detected with statistical significance in both this study and the former studies. However, another 20 proteins identified in this study had not been observed in the former studies. It has been demonstrated that the analysis of even the same sample could result in different sets of proteins by different protein separation systems. This could be ascribed to the different nature of the systems used and the different

resulting separation properties thereof. Even within similar systems, slightly variable handling or parameters can lead to detection of different protein sets (Hamacher et al., 2006; Stühler et al., 2006). Considering the differences in sample separation, as well as the differences in samples, sample handling and preparation, the partial overlap between our study and the former studies should be reasonable. In addition to proteomes, the validation of the proteins identified is another essential step. In this study, PSMA3 and CALB1 were discovered to be implicated in brain aging, which was further validated by Western blotting. In accordance with the conclusion drawn by Hamacher et al. (2006) and Stühler et al. (2006) that the datasets detected by different studies should be seen as complementary, our study contributed to the proteome of aging brain by detecting another 20 proteins. More importantly, for the first time, the natural changing patterns of the 39 proteins were observed from neonate through old age in our study, which should contribute to the understanding of the development, maturation and senescence of the brain.

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Fig. 3. Validation of the age-related change pattern of CALB1 by Western blot analysis. (A) Magnified comparison map of CALB1 in 2-DE gels of different aging stages. The white circles on the spot represent the careful cutting of the core parts of spots with streaking to eliminate the influence of streaking on protein identification. (B) Western blot analysis of CALB1 in mice brain at different aging stages. ␤-Tubulin was used as the internal control to normalize the content of CALB1 in different lanes. (C) Percentage changes of CALB1 vs. CALB1 in neonate (4 days old). Data of Western blot were expressed as mean⫾S.D. (P⬍0.05). The protein change patterns of CALB1 detected by 2-DE and Western blot agreed with each other.

As shown by ontological analysis, more than one third of the identified proteins are involved in protein metabolic

processes (Fig. 5C), of which one third participate in the ubiquitin-dependent protein catabolic process. Among

Fig. 4. Validation of the age-related change patterns of PSMA3 and PSMA6 by Western blot analysis. (A) Magnified comparison map of PSMA6 in 2-DE gels of different aging stages. (B) Magnified comparison maps of PSMA3 in 2-DE gels of different aging stages. (C) Western blot analysis of PSMAs in mice brain at different aging stages. ␤-Tubulin was used as the internal control to normalize the content of PSMAs in different lanes. (D) Percentage changes of PSMA vs. PSMA in neonates (4 days old). Data of Western blot were expressed as mean⫾S.D. (P⬍0.05). The protein change pattern of the sum of PSMA3 and PSMA6 detected by 2-DE is similar with that detected by Western blot.

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Fig. 5. Stacked bar charts depicting the distribution of the identified aging-related brain proteins among various cellular components (A), molecular functions (B) and biological processes (C). The black and gray represent respectively the portion of the down- and up-regulated proteins in the brain senescence process (since 12 months old) as compared with the adult stages (3 and 6 months old). The dark gray represents the portion of the stably expressed proteins since 3 months old.

them, VCP plays an important role in the ubiquitination and the subsequent translocation of misfolded proteins from the ER to cytosol for degradation in proteasomes (Rao et al., 2004); PSMA3 and PSMA6 are constitutive core endopeptidases in proteasomes; and UCHL3 is a deubiquitinating enzyme that can replenish the free ubiquitin pool

that feeds the ubiquitin–proteasome system (Hochstrasser, 1996). So the down-regulation of these proteins at elderly stages indicates the decline of the ubiquitin– proteasome system, which should result in not only the inefficient regulation of multiple cellular processes including apoptosis and cell signaling (Powell, 2006), but also

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the unreasonable accumulation of the damaged and misfolded proteins usually experienced more by aging brain cells. What is worse, even more misfolded proteins will be created in ER lumen due to the down-regulation of CALR at elderly stages, because CALR plays a pivotal role in quality control retention of glycoproteins in the ER (Bedard et al., 2005). It has been demonstrated that the excessive accumulation of damaged and/or misfolded proteins was aging-related, as it leads to oxidative stress and finally causes cell death (Mattson and Magnus, 2006). About 20% of the identified proteins can be located in mitochondria, and most of them are related to energy metabolism. Among them, GLUD1 provides important intermediates for citric acid cycle (Smith et al., 2001); MDH2 is an NADPH-producing enzyme in the citric acid cycle and is important in the metabolic coordination between cytosol and mitochondria (Gietl, 1992); IDH1 is a key speed controller in the citric acid cycle, playing an important role in cellular defense against oxidative damage by supplying NADPH for antioxidant systems (Kil et al., 2006); UQCRC2 is a core subunit of complex III in the mitochondrial respiratory chain (Duncan et al., 1993); and VDAC1 regulates the permeability of ions and small molecules, which is needed by the aerobic respiration chain (Cesura et al., 2003). As shown in this study, the decrease of these key proteins at elderly stages implies the decline in the supply of energy and reducing agent, which should contribute to the senescence of the brain. For example, cells with low levels of VDAC1 showed fourfold lower ATP-synthesis capacity and contained low ATP and ADP levels (AbuHamad et al., 2006). In the cells expressing lower levels of IDH1, oxidative DNA damage and intracellular peroxide generation were higher and the cellular redox status shifted to a pro-oxidant condition (Kil et al., 2006). In addition, another mitochondrial protein was also identified as being down-regulated especially at elderly stages. LONP1 is a major regulator of multiple mitochondrial functions. Its down-regulation can lead to massive caspase 3 activation, extensive apoptosis and ultimately, necrosis (Bota et al., 2005). Of the identified proteins, about 30% can be located in the nucleus. Among them, UBE2N is needed in DNA repair and contributes to the survival of cells after DNA damage (Ashley et al., 2002); VCP also participates in DNA damage repair (Zhang et al., 2000); and HNRPA2B1 is involved in telomere maintenance (Moran-Jones et al., 2005). Their decrease at elderly stages is consistent with the progressive increase of DNA damage encountered by senile brain cells. Many nuclear proteins involved in gene expression regulation were also identified as decreasing at elderly stages. For example, UBE2N can promote the transcription of antiapoptotic genes (Andersen et al., 2005); SET can promote the transcription of CREB-binding protein (CBP) –mediated transcription (Karetsou et al., 2005); and EIF5A may be responsible for the translation of a subset of special mRNAs directly necessary for cell growth (Xu et al., 2004). The decrease of these proteins may contribute to brain aging by down-regulating the expression of certain genes. For example, EIF5A has been

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proven to be essential for cell survival (Nishimura et al., 2005). One third of the identified proteins participate in the process of biological transport. Among them, DNM1 is essential for the fission of endocytic pits in the process of synaptic vesicle endocytosis (Newton et al., 2006), and AMPH cooperatively participates in the fission process, which is critical in learning and memory (Di Paolo et al., 2002; Yoshida and Takei, 2005); TRF can antagonize apoptosis and ensure cell survival by transferring iron into cells and maintaining intracellular iron homeostasis (Han et al., 2003); CALB1 is a mobile Ca2⫹ buffer shaping the spatiotemporal extent of cellular Ca2⫹ signals, which is critical to the precision of motor coordination (D’Orlando et al., 2002; Barski et al., 2003); and HNRPA2B1 and HNRPA3 mediate intracellular trafficking of specific mRNAs to establish the necessary asymmetry in cells (Ma et al., 2002). So the decrease of these proteins at elderly stages can contribute to brain senescence in various ways. Additionally, the down-regulation of another two proteins was also related to brain senescence. One is PAFAH1B3, whose expression is restricted to actively migrating neurons (Manya et al., 1998). PAFAH1B3 deficits have been demonstrated to be responsible for mental retardation, ataxia and atrophy of the brain (Nothwang et al., 2001). The other one is PPA1, which controls the level of intracellular pyrophosphate (Islam et al., 2003). PPA1 is considered to be essential for life, being intimately involved in cell survival (Kolakowski et al., 1988). In this study, several proteins were identified as perhaps being regulated to resist the senile process of senility. For example, MAPK9 was up-regulated at elderly stages to maintain genomic stability (MacCorkle and Tan, 2004). To compensate for the decline of energy metabolism, TKT, CKMT1 and MRPL37 are up-regulated in elderly stages. TKT controls the nonoxidative branch of the pentose phosphate pathway, channeling excess sugar phosphates to glycolysis and thus maintaining the production of NADPH and ATP under different metabolic conditions (Schenk et al., 1998; Horecker, 2002). MRPL37 is required for the translation of mitochondrial encoded genes (Levshenkova et al., 2004). The increase of MRPL37 may be an attempt to reconstruct the declining energy metabolic machine by synthesizing more mitochondrial-encoded proteins, especially those involved in the citric acid cycle and electron transport chain. CKMT1 catalyzes the transfer of high energy phosphate from mitochondria to the cytosolic carrier creatine (Payne et al., 1991). So the up-regulation of CKMT1 may possibly be an adaptation to the low production of ATP in mitochondria and high energy demand in brain cells. In addition, glycolysis can accumulate dihydroxyacetone phosphate (DHAP), which leads to oxidative stress, DNA damage, and apoptosis. TPI1 was identified as being up-regulated in the elderly brain to eliminate DHAP and thus it functions as a neuroprotector (Gnerer et al., 2006). To illustrate their possible combined influences on brain aging as a whole, these proteins were mapped in the

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Fig. 6. The map of the identified aging-related brain proteins in the context of all the involved biological processes. Protein symbols underlined meant their down-regulation, whereas bold italic up-regulation. The solid arrows meant activation or enhancement. The “*” and “,” meant inhibition. The dotted arrows meant transition or flow.

background of all the involved biological processes in Fig. 6. However, this provides a far from comprehensive snapshot of all the aging-related proteins because of the limited number of proteins identified in this study. Although many mechanisms have been inferred relating to the aging and anti-aging of the brain, many questions remain to be answered. For example, many more proteins involved in brain aging, especially those low abundance proteins, remain to be revealed to get a comprehensive view of the complete molecular mechanism of brain aging. Furthermore, the protein samples were taken from the mice brain as a whole, and thus it is unknown whether or not the differentially expressed proteins are limited to a certain region or whether they cover a relatively wide range in the brain. Regional proteome analysis may help to answer this question.

CONCLUSION In conclusion, we identified quite a few aging-associated proteins in the naturally aging mice brain from neonate through old age. These proteins play various roles in widespread biological processes. Most of these proteins were identified for the first time as being involved in the natural senile process of brain, which will provide new insight into the molecular mechanism of brain development, maturation and aging.

Acknowledgments—This work was supported by the Chinese National Key Project of Basic Research (001CB510206) and the Chinese National Natural Science Foundation (30430310).

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APPENDIX Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroscience.2008.04.012.

(Accepted 9 April 2008) (Available online 12 April 2008)