Longitudinal Characterization of the Brain Proteomes for the Tg2576

Article pubs.acs.org/jpr

Longitudinal Characterization of the Brain Proteomes for the Tg2576 Amyloid Mouse Model Using Shotgun Based Mass Spectrometry Ganna Shevchenko,*,† Magnus Wetterhall,† Jonas Bergquist,†,‡ Kina Höglund,§ Lars I. Andersson,∥ and Kim Kultima⊥ †

Analytical Chemistry, Department of Chemistry-BMC, Uppsala University, Box 599, SE-751 24 Uppsala, Sweden Science for Life Laboratory, Uppsala University, Uppsala, Sweden § AstraZeneca Translational Sciences Centre, Science for Life Laboratory, S-171 21 Solna, Sweden ∥ Translational Science, AstraZeneca R&D, S-151 85 Södertälje, Sweden ⊥ Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University Academic Hospital, SE-751 85 Uppsala, Sweden ‡

S Supporting Information *

ABSTRACT: Neurodegenerative disorders are often defined pathologically by the presence of protein aggregates, such as amyloid plaques composed of βamyloid (Aβ) peptide in Alzheimer’s disease. Such aggregates are the result of abnormal protein accumulation and may lead to neuronal dysfunction and cell death. In this study, APPSWE transgenic mice (Tg2576), which overexpress the Swedish mutated form of human amyloid precursor protein (APP), were used to study the brain proteome associated with amyloid plaque deposition. The major aim of the study was to map and compare the Tg2576 model brain proteome profiles during pathology progression using a shotgun approach based on label free quantification with mass spectrometry. Overall, 1085 proteins were identified and longitudinally quantified. Principal component analysis (PCA) showed the appearance of the pathology onset between twelve and fifteen months, correlating with sharp amyloid plaque accumulation within the same ages. Cluster analysis followed by protein−protein interaction analysis revealed an age-dependent decrease in mitochondrial protein expression. We identified 57 significantly affected mitochondrial proteins, several of which have been reported to alter expression in neurological diseases. We also found ten proteins that are upregulated early in the amyloid driven pathology progression with high confidence, some of which are directly involved in the onset of mitochondrial apoptosis and may represent potential markers for use in human neurological diseases prognosis. Our results further contribute to identifying common pathological pathways involved in both aging and progressive neurodegenerative disorders enhancing the understanding of disease pathogenesis. KEYWORDS: neurodegeneration, Tg2576 mouse model, proteomics, cloud-point extraction (CPE), label free quantification, mass spectrometry

1. INTRODUCTION Common pathological features for many neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease, are caused by massive brain cell death that gradually spreads through the affected brain regions.1−3 The progressive neurodegeneration in turn leads to shared disease symptoms, e.g., destroyed memory, impaired learning functions, communication difficulties, and changes in personality. Typically, clinical diagnosis of AD and PD cannot be made until the disease has progressed to the point that dementia or motoric dysfunctions arise.4,5 However, the neuropathology of these diseases has been demonstrated to precede clinical symptoms and is linked to the aggregation and deposition of misfolded peptides and proteins. For instance, AD is characterized by the aggregated β-amyloid (Aβ) peptide in the form of extracellular senile plaques and hyper© XXXX American Chemical Society

phosphorylated tau protein in the form of intracellular neurofibrillary tangles causing massive neuronal and synaptic degeneration.2,6 Alterations of the membrane interactions in the brain tissue are other key features in the biomolecular processes underlying the pathobiology of neurodegeneration. This is confirmed by the fact that abnormalities in the membrane protein interactions are part of the pathogenesis of AD and PD.7,8 Although neurodegenerative diseases like AD, PD, and Huntington’s have been known and diagnosed for more than a century, there are still shortcomings to fully understand the cause and disease progression. Further and more importantly, current available medical treatments might reduce symptoms or Received: August 27, 2012

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dx.doi.org/10.1021/pr300808h | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

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Figure 1. Schematic diagram of experimental setup used in the study.

the Swedish mutated form of human APP and develops many neuropathological features of AD, including amyloid plaques, dystrophic neuritis, and inflammatory changes.9,12,14 The use of the Tg2576 amyloid mouse model in a proteomic study may therefore satisfactorily simulate the preclinical phase of AD9,14 and provides valuable insights into the molecular mechanisms underlying memory loss and cell death in the disease.15 Proper tissue handling and sample preparation techniques are crucial for the quality of the obtained data in any tissue proteomics study, including membrane proteomics. Hydrophobic membrane proteins (MPs) occupy a unique niche in brain proteome research due to their important physiological roles.16 MPs represent more than 60% of known protein drug targets, and it is therefore of utmost importance to develop methods for the enrichment and separation of MPs in biomarker discovery research.17 Recently, our group has shown the use of the cloud point extraction (CPE) methodology for the simultaneous extraction and enrichment of both the MPs and hydrophilic proteins from brain tissue.18,19 The utilized CPE method with the nonionic surfactant Triton X-114 yields a phase separation of the proteins into either a hydrophilic or a hydrophobic phase, which can be processed and analyzed individually. High preconcentration coefficients, good selectivity, and the possibility to combine with various methods of downstream analysis are attractive features promoting the use of nonionic surfactants to extract hydro-

impede the progression of the disease but may not able to recover the already degenerated neural tissue. Clinical studies of the affected tissue and its surroundings during disease development are therefore of utmost importance. However, given that brain tissue sampling is extremely invasive for the patient and is not ethical or practical to perform, thus postmortem tissue collection is praxis. Yet, it often implies rather long postmortem times and tissue collected at the terminal stage of the disease progression. Therefore, animal models of a human disease are extremely valuable. The use of preclinical animal models reduces the heterogeneity resulting from genetic, physiological, and environmental factors that are naturally found in patients. Furthermore, it offers the opportunities to perform precise longitudinal studies of genetically identical animals from an early disease onset. Transgenic mouse models mimicking parts of human AD pathology have been used for more than ten years to investigate the pathogenesis and evaluate potential treatments.9−12 The first preclinical model was introduced in 199511 and has since enabled controlled studies of disease mechanisms and pathogenic processes to get a better understanding of the disease. Aβ peptides have been known to be closely associated with pathogenesis of AD. They are produced from the proteolytic cleavage of the full-length amyloid precursor protein (APP) through the activity of the β- and γ-secretase enzymes.6,13 The Tg2576 amyloid mouse model overexpresses B



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Milli-Q water purification system (Millipore, Bedford, MA, USA).

phobic proteins from mouse brain tissue which can be potential molecular markers of the disease from biological samples. The present study was designed to extract and characterize changes in the proteomic profiles in the Tg2576 amyloid mouse model during amyloid plaque buildup in the brain.

2.3. Aβ Analyses

Measurement of human Aβ1−42 in the olfactory bulb from the Tg2576 mice was performed using a commercially available enzyme-linked immunosorbent assay (ELISA) kit (Innogenetics) validated according to in house criteria. Aβ was extracted from the frozen brain tissue by sonication, using Sonic Vibra-Cell (CiAb). Brain tissue was sonicated 3 times 10 s with the amplitude of 80% in 1:18 (w/v) 0.2% DEA and 50 mM NaCl, pH 11.6. After centrifugation (133000g, 4 °C, 1 h), the supernatant was recovered and neutralized to a pH of 8.0 with 2 M Tris-HCl. 70% formic acid (FA) was added (18 mL/ mg wet weight) to the remaining pellet and sonicated 2 times 10 s using CiAb. Homogenates were centrifuged at +4 °C for 1 h at 133000g. Recovered supernatants were neutralized to pH 8.0 with 2 M Tris-HCl at 1:20 (w/v) dilutions. Analysis was performed on the FA (insoluble phase) only.

2. EXPERIMENTAL SECTION A schematic overview of the entire experimental setup is given in Figure 1. The effect of amyloid plaque formation was longitudinally studied in the Tg2576 mouse model. Five animals were sacrificed at four pathology progression time points (12, 15, 18, and 22 months of age). The brains were dissected and the proteomes were CPE extracted and analyzed using a label free shotgun based bottom-up nanoLC−MS/MS approach. Longitudinal disease progression trends were elucidated by multivariate data analysis, and the proteome expression profiles were compared. Finally, functional and protein network analysis as well as protein pathway elucidation were performed on the proteins showing the largest change in differential expression.

2.4. Cloud Point Extraction of Proteins

Commercially available Triton X-114 was precondensated to obtain a homogeneous Triton X-114 mixture.22 Aliquots of 100 mg of hemisphere powder were homogenized for 60 s in a blender (POLYTRON PT 1200, Kinematica) with 1 mL of Triton lysis buffer (1% (v/v) Triton X-114, 10 mM Tris-HCl pH 7.4, 0.15 M NaCl, 1 mM EDTA). Protease inhibitor cocktail (10 μL) was added during the sample preparation to reduce protein degradation. After homogenization, the sample was incubated for 1 h at 4 °C during mild agitation. The cell lysate was clarified by centrifugation for 30 min (10000g at 4 °C) using a Sigma 2K15 ultracentrifuge (Sigma Laborcentrifugen GmbH, Osterode, Germany). The clear supernatant was then transferred directly onto 100 μL of sucrose cushion buffer and incubated at 37 °C for 5 min, which led to the clouding of the solution. The sample was centrifuged for 3 min (400g at 37 °C) to separate the two phases; aqueous on the top and detergent at the bottom. The aqueous phase was transferred to a new tube and incubated on ice. The detergent phase was mixed with 500 μL of cold PBS, and phase separation was repeated again. The second detergent depleted aqueous phase was then pooled with the first and kept on ice. The detergent-rich fraction, containing hydrophobic membrane proteins, was mixed with 1.5 mL of cold PBS. The pool of detergent-depleted aqueous phase was re-extracted by addition of 50 μL of 11.4% Triton X-114 stock solution, incubated at 37 °C for 3 min, and centrifuged for 3 min (400g at 37 °C). This aqueous phase contained hydrophilic water-soluble proteins.

2.1. The Tg2576 Mouse Model

The Tg2576 mouse overexpresses the human gene encoding the APP with the Swedish double mutation (K670N/M671L) driven by the hamster prion protein. The phenotype includes progressive depositing of Aβ and developing plaques in the brain that resemble pathology seen in patients with AD.20 At the age of around 9 months the first amyloid plaques can be detected using immunohistochemistry. Not every aspect of the mouse phenotype mimics that of human AD where neuronal loss and neurofibrillary tangles are not evident in the mice.21 In the present study, five female Tg2576 mice were included at each age: 12, 15, 18, and 22 months. The mice were bought from Taconic Farms (Germantown, New York, USA) at the age of 10−12 weeks and aged in house until termination. They were kept in conventional housing and fed standard rodent chew and tap water ad libitum. The mice were sacrificed by decapitation and brain samples collected. The cerebellum was removed, and the brain was divided into two hemispheres. The left hemisphere and the hippocampus, frontal cortex, and olfactory bulb from the right hemisphere were dissected out on ice, weighed, and fresh-frozen on a piece of foil on dry ice. The samples were put in prefrozen and prelabeled 1.5 mL Eppendorf tubes and stored at −70 °C pending analyses. All experiments with mice brain samples were approved by the Stockholm Södra Animal Research Ethical Committee, S157/ 08. 2.2. Chemicals and Reagents

2.5. Delipidation and Protein Precipitation

Acetonitrile (ACN), methanol (MeOH), acetic acid (HAc), formic acid (FA), ammonium bicarbonate (NH4HCO3), tri-nbutylphosphate (TBP), and sodium chloride (NaCl) were obtained from Merck (Darmstadt, Germany). Acetone, ethylenediaminetetraacetic acid tetrasodium salt dihydrate (EDTA), protease inhibitor cocktail, phosphate buffered saline (PBS), Tris-HCl, diethylamide (DEA), trifluoroacetic acid (TFA), and n-octyl-β-D-glucopyranoside were purchased from Sigma Aldrich (St. Louis, MO, USA). For tryptic digestion, iodoacetamide (IAA), urea, and dithiothreitol (DTT) obtained from Sigma Aldrich and trypsin (sequencing grade from bovine pancreas 1418475; Roche diagnostic, Basel, Switzerland) were used. Sucrose was purchased from Fisher Scientific Company (Göteborg, Sweden). Triton X-114 was obtained from KEBO Lab (Stockholm, Sweden). Ultrapure water was prepared by

A delipidation protocol according to Mastro et al. was used.23 Aliquots (100 μL) of the detergent-depleted aqueous and detergent-rich phases were mixed with 1.4 mL of ice-cold tri-nbutylphosphate:acetone:methanol mixture (1:12:1 (v/v/v)) and incubated at 4 °C for 90 min. The precipitate was pelleted by centrifugation for 15 min (2800g at 4 °C), then washed sequentially with 1 mL of TBP, 1 mL of acetone, and 1 mL of methanol, and finally air-dried. 2.6. Protein Quantification

The total protein content of delipidated proteins was determined using the DC Protein Assay Kit (BioRad Laboratories, Hercules, CA, USA), which is based on the modified Lowry method with bovine serum albumin as standard.24 The protein pellets were redissolved in 100 μL of C



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acetonitrile, 10% water, and 0.5% acetic acid). A 100 min gradient from 2% B to 50% B followed by a washing step with 98% B for 5 min was used. Mass spectrometric analyses were performed using unattended data-dependent acquisition mode, in which the mass spectrometer automatically switches between acquiring a high resolution survey mass spectrum in the FTMS (resolving power 50 000 fwhm) and consecutive low-resolution, collision-induced dissociation fragmentation of up to five of the most abundant ions in the ion trap. Acquired data (.RAW-files) were converted to the .mgf format using an in house written program (C++) and subjected to protein identification using MASCOT search engine (version 2.2.2, Matrix Science, U.K.) against the SwissProt database version 51.6. The search parameters were set to Taxonomy: Mus musculus, Enzyme: Trypsin, Fixed modifications: Carbamidomethyl (C), Variable modifications: Oxidation (M) and Deamidated (NQ), Peptide tolerance: ± 0.02 Da, MS/MS tolerance: ± 0.6 Da and maximum 2 missed cleavage sites. For protein calculations, only peptides with a Mascot score >25 detected in at least three out of five biological replicates in any biological group were included.

6% SDS. The DC assay was carried out according to the manufacturer’s instructions using 96-well microtiter plate reader model 680 (BioRad Laboratories). 2.7. On-Filter Digestion Followed by NanoLC−MS/MS Analysis

2.7.1. On-Filter Tryptic Digestion of Proteins. Aliquots of 100 μL of the detergent-depleted aqueous and detergent-rich phases were delipidated. An on-filter digestion protocol was used for tryptic digestion of the samples25 using 3 kDa filters (Pall Life Sciences, Ann Arbor, MI, USA). Centrifugation was carried out at a centrifugal force of 14000g throughout the protocol. The samples were first redissolved in 100 μL of 50:50 ACN:(8 M urea + 1% n-octyl-β-D-glucopyranoside). A volume of 10 μL of 45 mM aqueous DTT was added to all samples, and the mixtures were incubated at 50 °C for 15 min to reduce the disulfide bridges. The samples were cooled down to room temperature, 10 μL of 100 mM aqueous IAA was added, and the mixtures were incubated for an additional 15 min at room temperature in darkness to carabamidomethylate the cysteines. The samples were transferred to spin filters that had been prewashed with 250 μL of 50% ACN for 15 min and then 500 μL of water for 20 min. The samples were then centrifuged for 10 min to remove the added salts, detergents and other interfering substances. An additional volume of 100 μL of 2% ACN in 50 mM NH4HCO3 was added, and the filters were spun for 10 min followed by 100 μL of 50:50 ACN:(50 mM NH4HCO3) and 100 μL of 50 mM NH4 HCO3, and centrifugation for another 10 min. Finally, a volume of 100 μL of 50 mM NH4HCO3 was added together with trypsin to yield a final trypsin/protein concentration of 2.5% (w/w). The tryptic digestion was performed at 37 °C overnight in darkness. The samples were then centrifuged for 20 min to collect the tryptic peptides in the filtrate while retaining undigested proteins and trypsin in the retentate. An additional volume of 100 μL of 50% ACN, 1% HAc was added, and the filters were spun for 10 min and pooled with the first tryptic peptide filtrate. The collected filtrates were vacuum centrifuged to dryness using a Speedvac system ISS110 (Thermo Scientific, Waltham, MA, USA). Prior to nanoLC−MS/MS analysis, the samples were redissolved in 100 μL of 0.1% TFA. 2.7.2. NanoLC−MS/MS for Protein Identification. All samples were analyzed in a complete randomized block design with a total of six blocks. Each block included repeated replicates of the same sample (Q14) that was analyzed throughout the analysis in a total of seven times as a quality control measurement. Duplicates were also analyzed for a subset of 5 samples and resulted in a total of 32 analyses, using 20 biological samples. The protein nanoLC−MS/MS experiments were performed using a 7 T hybrid LTQ FT mass spectrometer (ThermoFisher Scientific, Bremen, Germany) fitted with a nanoelectrospray ionization (ESI) ion source. Online nanoLC separations were performed using an Agilent 1100 nanoflow system (Agilent Technologies, Waldbronn, Germany). The peptide separations were performed on in house packed 15 cm fused silica emitters (75 μm inner diameter, 375 μm outer diameter). The emitters were packed with a methanol slurry of reversed-phase, fully end-capped Reprosil-Pur C18-AQ 3 μm resin (Dr. Maisch GmbH, Ammerbuch-Entringen, Germany) using a PC77 pressure injection cell (Next Advance, Averill Park, NY, USA). The separations were performed at a flow of 200 nL/min with mobile phases A (water with 0.5% acetic acid) and B (89.5%

2.8. Data Analysis

The LTQ-FTICR raw files were imported into DeCyder MS2.0 (GE HealthcareBio-Sciences AB, Uppsala, Sweden), and ion peaks were automatically detected using a typical peak width of 0.4 min, signal to background threshold of 3, and uniform background subtraction. The resulting intensity maps were aligned using DeCyder MS 2.0 allowing a time tolerance of 0.6 min and m/z tolerance of 0.01 Da. Peaks were manually curated for discrepancy. Normalization was conducted on log2transformed data exported from the DeCyder MS software. To correct for global intensity differences between peptide analyses, reference normalization was used.26 For each data set, the analysis with the highest number of detected peaks was chosen as reference. Peaks that were detected in at least three out of five biological replicates in any biological group were included in subsequent analyses. To find peaks that differ between age groups a linear model was employed to calculate age group means and to calculate moderated F-statistics using eBayes in limma (version 3.4.4)27 and R version 2.11.28 The linear model was fitted with M12 as denominator, both including all age groups and when excluding the 22 month group. The average of technical replicates was used. To get an overview of the data, unsupervised principal component analysis (PCA) was applied to all peaks with an F-value p < 0.05. To calculate protein expression between age groups only peptides with an F-value p < 0.05 was included in the calculations. The median value of relative expression of the peptides for each protein was calculated, and for a protein to be considered as changing statistically significantly in expression the absolute protein fold change had to be log2 > 1. Proteins changing statistically significantly were analyzed using STEM (short time-series expression miner) to detect changes in expression across age groups.29 Prior to the analyses, the protein expression was standardized across age groups. Enrichment analyses were performed on clusters of proteins found by STEM using DAVID (database for annotation, visualization and integrated discovery).30 All identified proteins were used as background for the analysis. An enrichment score >2 of annotated clusters was considered as significant. Typical categories within each cluster were selected to represent the whole cluster. The protein count for each term had to be >15 D



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measured relative change in amyloid β1−42 occurs between the ages of 12 and 15 months, showing an increase of 435%. This dramatic increase in the Aβ is likely to induce multifunctional proteomic responses as a direct consequence of the amyloid plaque formation. Thus, large differences in the proteome expression profiles between 12 and 15 month age groups are to be expected. The plasticity of the brain would imply both protein up- and downregulations following the plaque formation. The relative increase in Aβ1−42 between 15 and 18 months and 18 and 22 months were found to be 67% and 58% respectively. The relative changes at the later stages are not as dramatic as between 12 and 18 months yet strongly confirm the accumulation of amyloid plaque with age.

and p < 0.05. The proteins from each enriched term were extracted and a heat map was constructed with the original protein expressions across age groups. To visualize protein interactions the extracted protein identities from the enriched terms were analyzed using STRING (search tool for the retrieval of interacting genes)31 with a medium confidence score of 0.9 and no more than 20 interactors shown. To find proteins that could be potential disease progression markers a more stringent approach was applied; these proteins had to include a least three significant peptides (p < 0.05) not included in any other protein, the median fold change had to be at least log2 > 1 in any of the age groups compared to M12 (no conflicting peptides allowed), and the trend had to be log2 > 0 in all age groups compared to M12. For selected proteins the standard deviation of peptide expression was calculated and included in Figure 6. All calculations were performed on a log2 scale.

3.2. Multivariate Analysis of the Label Free Quantification

A label-free quantitative LC−MS/MS approach was used for the CPE extracted and enriched brain proteomes to study the relative changes of the proteins in the Tg2576 amyloid mouse model. One biological sample in the hydrophilic fraction (12 months) was removed due to very low overall intensity (both technical replicates). After removal of this sample a total of 1597 peptides out of 4104 peaks and 3433 peptides out of 5996 peaks were successfully matched and identified in the hydrophobic and hydrophilic phases, respectively. A total of 220 peptides (out of 519 peaks) were statistically significantly different in expression (ANOVA, p < 0.05) in the hydrophobic phase, and the corresponding result in the hydrophilic phase was 287 peptides out of 477 peaks. To get an overview of the result a PCA analysis was conducted on these peaks. The first and second component explained a total of 55% and 56% of the total variations in the hydrophobic and hydrophilic phases, respectively. A clear separation between the age groups is visible in the PCA plot of the hydrophobic fraction (Figure 3A). The PCA results of the hydrophilic phase show a less distinctive but still discernible separation of different mouse age groups (Figure 3B). In both fractions there appears to be an onset of the disease between 12 and 15 months. This correlates well with the sharp increase in amyloid levels occurring between 12 and 15 months of age (Figure 2). In both fractions, there is a trend that coincides with increasing age. However, this trend is most accentuated in the hydrophobic fractions, indicating a stronger disease pathology influence on the MPs, which is a common feature in the biomolecular processes underlying the pathobiology of many neurodegenerative diseases. It should be noted that the consistent replicates of the same sample (Q14) are tightly bound on both PCA plots, confirming the reproducibility of the label free nanoLC−MS/MS analysis throughout the analysis (Supplemental Figure 1 in the Supporting Information).

3. RESULTS AND DISCUSSION The major aim of this study was to map and compare the brain proteome profiles of the Tg2576 amyloid model during plaque

Figure 2. ELISA measurements of the human Aβ1−42 concentration in the insoluble formic acid phase (FA) of the olfactory bulb from Tg2576 mice at different ages (12, 15, 18, and 22 months). One sample in the 12 month group was excluded since the level of Aβ1−42 was below the detection limit of the ELISA assay.

formation and pathology progression. Initial Aβ1−42 analyses were performed to confirm amyloid buildup with age and thus verify the disease pathology and progression. This was followed by a shotgun based MS approach to map the longitudinal differential protein expression associated with disease pathology in the preclinical mouse model. Extensive label free quantification and multivariate data analysis were performed to identify the proteins with the largest expression changes connected to pathology progression. These proteins were then further elucidated in functional and protein network analysis and protein pathway analysis to establish their roles in the neurodegenerative pathophysiology.

3.3. Differential Expression Profile in the Extracted Mouse Brain Proteomes

In total, 486 and 599 proteins were identified in the hydrophobic and hydrophilic phase, respectively. Out of these proteins, 212 were common to both phases. The efficiency of nonionic detergent Triton X-114 in simultaneous extraction and separation of hydrophilic and hydrophobic membrane proteins is given by the relatively low level of overlap (24%) between the two phases (Figure 4). The longitudinal comparison of the identified proteins revealed additional interesting findings. In total, 407 hydrophobic proteins were identified in all 4 age groups. A distinct disease related trend was found in the hydrophobic (membrane bound) phase showing a strong decrease of the number of

3.1. Aβ1−42 Analyses To Verify the Amyloid Mouse Model

Aβ1−42 analyses were carried out to measure the increasing amyloid buildup with age and thereby verify the validity of the amyloid model. The Aβ peptide is the major constituent of neuritic plaques and vascular deposits in AD patients. Figure 2 depicts the levels of human Aβ1−42 in the insoluble FA phase of the olfactory bulb from Tg2576 mice at different ages (12, 15, 18, and 22 months) measured by ELISA. The observation of increasing levels with age in the brain of the Tg2576 confirms previous reports12,32 and, thereby, verifies the accuracy of the preclinical model for further studies. The largest E



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Figure 3. PCA plot of the hydrophobic and hydrophilic brain proteome fractions of the Tg2576 brain at different ages.

presynaptic membrane, may reflect changes in synaptic properties. Previous studies have shown that synaptotagmin and synaptoporin levels are reduced in the hippocampus and neocortex during disease progression.34,35 The alpha-SNAP protein plays an important role in vesicular transport and is involved in neurotransmitter release.36 Previously, it has been reported that oxidation of SNAP may lead to loss of synaptic functions and impaired learning in AD.37 Proteasome-related protein UCHL1 appears to play a role in many neurodegenerative diseases. It has been connected to the death of neurons in AD, PD, and Huntington’s.38,39 UCHL1 is also involved in the proteolytic degradation of misfolded or damaged proteins by the proteasome.40 Recently, it has been shown that the activity of UCHL-1 is markedly decreased in the AD brain.41 Similarly, the activities of the ubiquitin-conjugating enzyme UCE2 are reversibly decreased under conditions of oxidative stress. The impairment of ubiquitin-dependent degradation is a result of the decrease in UCE2 activities in the cytosol of the brain cells. This process may contribute to the abnormal accumulation of proteins in AD. Recent reports have suggested that mitochondrial enzyme deficiencies contribute to the progression of the pathology of neurodegenerative diseases.42,43 In this study, we observed significant disease pathology related reduction of the levels of multiprotein enzyme complexes. These include NADHubiquinone oxidoreductase (complex I) and NADH dehydrogenase [ubiquinone] 1alpha subcomplex subunit 6, which are energy metabolism enzymes catalyzing the transport of electrons from NADH to ubiquinone for ATP synthesis.44 Reductions of subunits of complex I may lead to the impairment of energy metabolism and ultimately result in neuronal cell death. The level of malate dehydrogenase, which belongs to the most active enzymes in mitochondria and glyoxysomes, was also significantly reduced in the hydrophobic fractions of the Tg2576 amyloid model with age. The observed results are in good agreement with previous proteomic studies of the Tg2576 mouse brain.45 Dynamin-1 was also found to be downregulated in the hydrophobic fraction. It is believed to

Figure 4. Venn diagram showing the number of unique proteins identified in the hydrophobic and hydrophilic fractions of the Tg2576 mouse brain at different ages and their overlaps.

identified proteins at the age of 22 months (44 proteins lost). Additionally, no proteins unique for the 22 month age group were found in the hydrophobic phase. This tendency was not observed for the hydrophilic fractions, where most of the proteins (549) were present in all 4 age groups. A more thorough look at the 44 proteins missing in M22 revealed synaptic abnormality-related proteins (synaptotagmin 2, synaptoporin, alpha-soluble N-ethylmaleimidesensitive factor (NSF) attachment protein (alpha-SNAP), synaptosomalassociated protein 47); proteasome-related proteins (ubiquitin carboxyterminal hydrolase L-1 (UCHL1) and ubiquitinconjugating enzyme E2 (UCE2)); energy-related enzymes (NADH-ubiquinone oxidoreductase, NADH dehydrogenase [ubiquinone] 1, malate dehydrogenase, citrate synthase mitochondrial, acylglycerol kinase); mitochondrial fission protein (dynamin-1). Neuronal and synaptic loss represents major dementiaassociated abnormalities in AD.33,34 The regulation of synaptic proteins synaptotagmin and synaptoporin, which are involved in the docking process of the synaptic vesicles to the F


a

cytochrome b-c1 complex subunit 1, mitochondrial succinyl-CoA ligase subunit beta, mitochondrial voltage-dependent anionselective channel protein 1 voltage-dependent anionselective channel protein 2 voltage-dependent anionselective channel protein 3 apolipoprotein E

4

1433B_MOUSE

APOE_MOUSE

VDAC3_MOUSE

VDAC2_MOUSE

VDAC1_MOUSE

SUCB1_MOUSE

QCR1_MOUSE

NCAM1_MOUSE PHB_MOUSE

DYN1_MOUSE

entry

W

W

MIC

MIC

MIC

W

MIC

MIC MIC

W

part

0.65

0.27

1.52

1.75

2.20

1.05

1.53

2.17 1.78

1.05

median log2(M15/ M12)

0.74

0.56

1.37

1.24

1.49

1.01

1.62

1.81 1.72

1.11


1.13

1.34

0.26

1.65

2.10

1.07

1.87

1.25 1.46

1.56


0.04

0.03

0.02

0.02

0.03

0.02

0.02

0.003 0.03

0.03

median p-value

3

4

3

3

5

3

4

3 3

5

n peptides

lipid transport; apoptosis induction catabolic process signaling pathways

ion transport

ion transport

redox regulation; energy metabolism apoptosis; ion transport

electron transport, proteolysis

signaling pathway; cell adhesion biosynthetic process

endocytosis; enzymatic activity

biological pathway

Uniprot knowledge entry. bMolecular weight of proteins in Da according to UniProt. cGrand average of hydrophobicity. dTransmembrane helix.

10

9

8

7

6

14-3-3 protein beta/alpha

neural cell adhesion molecule 1 prohibitin

2 3

5

dynamin-1

protein name

1

no.

a

Table 1. Early Markers of Pathology Progression

membrane, mitochondrion membrane,d mitochondrion membrane,d mitochondrion chylomicron, secreted cytoplasm

cytoplasm, cytoskeleton membraned membrane, mitochondrion membrane, mitochondrion mitochondrion

location

28086

−0.69

−0.91

−0.32 30753 33968

−0.22

−0.33

0.03

−0.24

−0.43 0.01

−0.53

GRAVYc

31732

32351

44422

49302

117233 29820

97803

MWb

Journal of Proteome Research Article

G



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brain.55 Immunohistological analyses of dynamin-1 in transgenic rat56 and mouse45 models have also shown a significant decrease in protein expression. Kelly et al.57 have stated that Aβ induced a significant reduction in dynamin-1 levels that preceded synapse loss in AD animal model system, and such decrease in dynamin-1 is mainly the result of calpain activation. The increase in the hydrophilic phase may thus serve as a potential target to follow up in, for example, human cerebrospinal fluid. NCAM is part of a family of cell-surface glycoproteins that play key roles in normal brain development, including synaptic bond stabilization, and axonal/dendritic growth.58,59 NCAM is a cell surface macromolecule that controls cell−cell interactions during development of the nervous system by regulating processes such as neuronal adhesion and migration, intracellular signaling, etc.60 In the hydrophobic fraction, NCAM-1 was found to be upregulated in the Tg2576 amyloid mouse model. Our results are in agreement with findings in human AD brain, where there tends to be an increase of NCAM levels with increasing AD severity.61 Interestingly, NCAM serum levels were found to be significantly increased in AD compared to healthy controls.62 These increased levels of serum NCAM may be associated with alterations in the expression of this protein in brain tissue, indicating that serum NCAM levels could be potentially useful as differential diagnostic markers of AD. Mitochondrial dysfunction is one of the major intracellular lesions of different neurodegenerative disorders.43,63 Changes in mitochondrial redox potential have also been linked to various physiologic processes including ATP synthesis and cell death initiation. A set of proteins associated with impaired mitochondrial function and oxidative stress (prohibitin, cytochrome b-c1, and three voltage-dependent anion-selective channel proteins (VDAC1, VDAC2, and VDAC3)) were also found to be upregulated in the present Tg2576 amyloid mouse model. Prohibitin, a multifunctional protein involved in mitochondrial function, cell proliferation, and development,64 was found to be upregulated in the Tg2576 mouse brain. Although the mechanism by which prohibitin influences aging remains elusive, clear evidence links the prohibitin to mitochondrial function. A significant reduction of prohibitin levels has been observed in the substantia nigra in PD.65 In contrast with the substantia nigra, prohibitin levels were increased in the frontal cortex in PD. Since prohibitin is activated by mitochondrial stress caused by an imbalance in the synthesis of mitochondrial proteins,66 it is conceivable that mitochondrial abnormalities do exist in the frontal cortex in PD and AD. VDAC 1−3 are proteins that form a channel through the mitochondrial outer membrane and participate in the formation of the mitochondrial permeability transition pore complex (mPTP), which is responsible for the release of proapoptotic products.67 In the hydrophobic fraction, VDAC 1−3 were found to be upregulated in the Tg2576 amyloid mouse model. A recent study has shown that VDAC1 is overexpressed in both transgenic AD mouse models and postmortem brain tissue from AD patients.68 There is also evidence that Aβ soluble oligomers are able to induce upregulation of VDAC1 in a human neuroblastoma cell line supporting a correlation between Aβ levels and VDAC1 expression.69 This class of proteins is also involved in the apoptotic process by releasing several apoptogenic factors such as cytochrome C70 and apoptosis inducing factor71 from mitochondria. In addition, it was found that VDAC-deficient mice show a significant change

Table 2. Enrichment Analysis no.

term

count

p-value

Enrichment Score: 3.3 1 2 3 4 5 6 7 8

mitochondrion mitochondrion inner membrane oxidative phosphorylation Huntington’s disease Parkinson’s disease Alzheimer’s disease Enrichment Score: 2.7 ion transport ATP biosynthetic process

× × × × × ×

10−3 10−6 10−3 10−3 10−3 10−2

46 23 18 19 18 15

1.2 4.2 1.4 2.2 2.8 3.6

24 16

2.2 × 10−7 6.4 × 10−7

maintain the mitochondrial morphology and act as a mechanoenzyme to constrict and divide the mitochondria.46,47 3.4. Early Markers of Pathology Progression

Our data provide evidence that there appears to be a rapid onset of disease pathology occurring between 12 and 15 months. To further investigate this we used a more conservative approach to identify proteins that increase in expression compared to the 12 month age group. Apart from Aβ, these proteins may also serve as potential markers of disease onset/ progression and contribute to the understanding of the underlying disease mechanisms. Ultimately, these markers could be potential markers also used in human neurological diseases. In total we identified ten upregulated proteins: dynamin-1; neural cell adhesion molecule 1(NCAM-1); prohibitin; cytochrome b-c1 complex subunit 1, mitochondrial; voltagedependent anion-selective channel proteins (VDAC) 1,2 and 3; apolipoprotein E (ApoE); 14-3-3 protein beta/alpha; and succinyl-CoA ligase subunit beta, mitochondrial (SCL) (Table 1). Apolipoprotein E is a multifunctional protein that plays a pivotal role in regulating the lipid metabolism, namely, in the mobilization and redistribution of cholesterol and phospholipid during membrane remodeling associated with synaptic plasticity.48−50 It is unique among apolipoproteins in that it has a special relevance to nervous tissue and is involved in maintaining synaptic integrity during aging. We have found a clear increase in ApoE expression with age in the Tg2576 amyloid mouse model. This is consistent with the increased level of ApoE in post-mortem human AD brains.51,52 Although the mechanisms underlying the role of ApoE in AD pathogenesis are still not well understood, many research groups suggest that ApoE contributes to AD by interacting with different factors through various pathways. It was suggested that ApoE4 allele promotes the polymerization of Aβ into plaqueforming fibrils and might impair neuronal regeneration.53 At the same time, ApoE is produced in neurons in response to oxidative stress, excessive Aβ, or head trauma.54 The increased level of expression of ApoE in our model could also be an indication of altered cholesterol metabolism, in relation with neuronal stress. Dynamin-1 was found to increase in the hydrophilic phase. This is very interesting considering that the level of dynamin-1 is decreasing in all age groups (not statistically significantly, median p = 0.11, minimum p = 0.07) in the hydrophobic CPE phase. This shift between phases could be an indication that the protein is degraded in the hydrophobic membrane fraction and released to the soluble fraction. This is supported by a previous study demonstrating the decrease of dynamin-1 in human AD H


glycerol-3-phosphate dehydrogenase, mitochondrial

sodium/potassium- transporting ATPase subunit alpha-1 ATP synthase lipid-binding protein, mitochondrial

potassium-transporting ATPase alpha chain 1

ADP/ATP translocase 1

sodium/potassium-transporting ATPase subunit alpha-2 ATP synthase subunit beta, mitochondrial

3

4

5

6

7

8

ATP synthase subunit b mitochondrial

60 kDa heat shock protein, mitochondrial potassium-transporting ATPase alpha chain 2 NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 8, mitochondrial excitatory amino acid transporter 2

prohibitin

ATP synthase subunit delta, mitochondrial

mitochondrial glutamate carrier 1

heat shock protein 75 kDa, mitochondrial voltage-dependent anion-selective channel protein 3

syntaxin-1B

ATP synthase subunit gamma, mitochondrial

heat shock 70 kDa protein 1A

plasma membrane calcium-transporting ATPase 2

12

13 14 15

I

17

18

19

20 21

22

23

24

25

16

11

sodium/potassium-transporting ATPase subunit alpha-3 sideroflexin-3

10

9

peptidyl-prolyl cis-trans isomerase FKBP1A phosphate carrier protein, mitochondrial

protein name

1 2

no.

AT2B2_MOUSE

HS71A_MOUSE

ATPG_MOUSE

STX1B_MOUSE

TRAP1_MOUSE VDAC3_MOUSE

GHC1_MOUSE

ATPD_MOUSE

PHB_MOUSE

EAA2_MOUSE

CH60_MOUSE AT12A_MOUSE NDUB8_MOUSE

AT5F1_MOUSE

SFXN3_MOUSE

AT1A3_MOUSE

ATPB_MOUSE

AT1A2_MOUSE

ADT1_MOUSE

ATP4A_MOUSE

AT5G1_MOUSE

AT1A1_MOUSE

GPDM_MOUSE

FKB1A_MOUSE MPCP_MOUSE

entry

a

−1.53

−0.23 −0.47 −0.25 −0.32

0.07 −0.13 −0.22 −0.23

−2.04

−0.01 −0.09 −0.61 −0.24 0.17

0.01 −0.07 −0.34 −0.08 0.76

−1.26 −0.44

−1.08 −0.27

−1.71

−1.95

1.47

−0.21 0.23

0.01

0.002

0.03

0.02

−0.24 1.55

1.21

0.03

−1.45

0.01 0.02

−0.19 1.36

−0.32 1.52

0.04

0.03 −1.55

1.46

0.03

−0.79

−1.70 0.26

0.19

−0.87

−0.12 0.79

1.78

1.47

1.72

1.77

0.01 0.04 0.01

0.03

−1.63 −1.52 −1.04

−1.38

0.01

−0.99

0.33

0.21 0.01

0.01

0.33

−0.99

0.21

0.02

0.02

0.03

0.01

0.01

−1.44

−1.98

−2.21

−1.88

−0.19

−0.16

0.01

0.40

−1.21

median p-value 0.01 0.03


Hydrophobic Fraction −0.33 −2.09 −0.28 −1.65


1.43

0.04 −0.07


Table 3. List of Enriched Proteins Identified by Nano-LC−MS/MS Analysis

4

1

1

3

2 3

1

1

3

1

1 1 1

1

3

5

5

3

3

1

1

9

1

1 2

n peptides

ion transport, biosynthetic process

stress response

neurotransmitter transport ion transport

stress response ion transport

ion transport

DNA biosynthetic process ion transport

electron transport

T cell activation ion transport electron transport

ion transport

ion transport

ion transport

ion transport, biosynthetic process ion transport



metabolic process

T cell proliferation ion transport

biological pathway

membrane, mitochondrion cytoplasm, mitochondrion membraned

membrane, mitochondrion membrane, mitochondrion membrane,d mitochondrion membrane, mitochondrion mitochondrion membraned membrane, mitochondrion membrane, mitochondrion membrane, mitochondrion membrane, mitochondrion membrane, mitochondrion mitochondrion membrane,d mitochondrion membraned

membrane,d mitochondrion membraned

membrane,d mitochondrion membraned

cytoplasm membrane,d mitochondrion membrane, mitochondrion membraned

location

132587

70079

30125

33244

73783 30753

15024

15024

29820

18816

57926 114727 18816

24765

35406

51749

51749

111745

32773

113884

7608

112510

76552

11791 34844

MWb

−0.15

−0.38

−0.22

−0.69

−0.36 −0.32

0.20

0.20

0.01

−0.80

−0.09 0.05 −0.80

−0.28

0.08

−0.004

−0.004

−0.01

0.07

1.14

0.001

−0.25

−0.40 0.11

GRAVYc



ATPO_MOUSE COX2_MOUSE

mitochondrial 2-oxoglutarate/malate carrier protein

ADP/ATP translocase 2

ubiquinone biosynthesis protein COQ7 homologue

NADH dehydrogenase [ubiquinone] 1 alpha subcomplex assembly factor 4 NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 10 mitochondrial calcium-binding mitochondrial carrier protein

hexokinase-1

ATP synthase subunit O, mitochondrial

cytochrome c oxidase subunit 2

29

31

32

33

34

37

38

39

J

14-3-3 protein epsilon 14-3-3 protein zeta/delta ATP synthase subunit alpha, mitochondrial AP-2 complex subunit beta L-lactate dehydrogenase B chain creatine kinase B-type fumarate hydratase, mitochondrial creatine kinase U-type, mitochondrial peroxiredoxin-5, mitochondrial

stress-70 protein, mitochondrial

prohibitin-2

V-type proton ATPase subunit B, brain isoform annexin A6

40 41 42 43 44 45 46 47 48

49

50

51 52

36

35

30

NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 4 isocitrate dehydrogenase [NAD] subunit alpha, mitochondrial dihydropyrimidinase-related protein 2

28

VATB2_MOUSE ANXA6_MOUSE

PHB2_MOUSE

GRP75_MOUSE

1433E_MOUSE 1433Z_MOUSE ATPA_MOUSE AP2B1_MOUSE LDHB_MOUSE KCRB_MOUSE FUMH_MOUSE KCRU_MOUSE PRDX5_MOUSE

HXK1_MOUSE

CMC1_MOUSE

NDUAA_MOUSE

NDUF4_MOUSE

COQ7_MOUSE

ADT2_MOUSE

M2OM_MOUSE

DPYL2_MOUSE

IDH3A_MOUSE

CY1_MOUSE

CY1_MOUSE

cytochrome c1_ heme protein, mitochondrial

27

ATP5H_MOUSE

entry

ATP synthase subunit d mitochondrial

protein name

a

26

no.

Table 3. continued

−1.50 −1.40 −2.41

−0.78 −0.92 −0.63 −1.37

0.14 −0.61 0.24 −0.37

−0.73 −0.64 −0.65 −0.20

−0.56 −0.37 0.16

−2.13 −1.36

−1.51

−1.46

0.02 0.03

0.02

0.03

0.02 0.03 0.03 0.01 0.04 0.04 0.04 0.03 0.01

Hydrophilic Fraction −0.47 −2.91 −0.32 −2.11 0.05 −1.45 0.16 −2.14 −0.59 −1.46 0.03 −1.95 −0.39 −1.29 0.19 −1.64 −0.43 −1.51 −0.77

−0.98 −0.52 0.67 0.25 −0.56 0.12 0.02 0.84 −0.001

0.02

−1.09

0.01

−4.15

−3.08

0.01

0.01

0.01

0.01

0.10

−2.38

0.91

−2.74

0.02

−1.18

−4.62

0.74

0.58

2.04

−1.13

−0.09 1.86

1.43

−0.97

−0.71 2.60

0.44

0.70

0.02

−2.47

0.01

0.02

0.04

0.02

−0.25

0.82 0.82

1.32

1.32

1.07 0.02

median p-value

−0.25

median log2(M22/ M12) 0.01

median log2(M18/ M12) Hydrophobic Fraction −0.44 −1.10


2 1

1

1

1 2 4 2 3 4 1 3 2

1

1

2

1

1

1

1

2

1

2

2

3

3

3

n peptides

ion transport ion transport

signaling pathways signaling pathways ion transport protein transport glycolysis metabolic process tricarboxylic acid cycle metabolic process oxidative stress, redox signaling protein folding; stress response transcription regulation

electron transport

ion transport

glycolysis

ion transport

electron transport

ubiquinone biosynthesis; metabolic regulator cell proliferation

ion transport

axon growth and path finding ion transport

tricarboxylic acid cycle

electron transport

electron transport

ion transport

biological pathway

membrane, mitochondrion cytosol cytoplasm

cytoplasm cytoplasm mitochondrion membrane mitochondrion cytoplasm mitochondrion mitochondrion mitochondrion peroxisome mitochondrion

membrane, mitochondrion membrane,d mitochondrion membrane, mitochondrion membrane, mitochondrion membrane,d mitochondrion

intracellular, mitochondrion membrane,d mitochondrion membrane,d mitochondrion membrane, mitochondrion mitochondrion

membrane, mitochondrion membrane,d mitochondrion membrane,d, mitochondrion mitochondrion

location

56550 75754

33165

68613

29174 27771 55310 104583 36441 42513 49921 43080 17015

25976

21006

108303

74570

36943

20082

20495

32800

34024

62278

36707

27276

27276

18618

MWb

−0.18 −0.44

−0.27

−0.394

−0.54 −0.68 −0.13 −0.10 0.04 −0.43 −0.08 −0.52 0.1

0.27

−0.09

−0.27

−0.03

−0.51

−0.72

−0.20

0.01

0.07

−0.28

−0−08

−0.01

−0.01

−0.67

GRAVYc



48626

−0.31

in synaptic transmission, deficits in long- and short-term synaptic plasticity and in behavior72 which are well-known characteristics of AD. In our study, 14-3-3 protein beta/alpha was also found to be significantly increased with age in the hydrophilic fractions of the Tg2576 amyloid model. 14-3-3 proteins, a family of chaperones, are known to play a role in the regulation of many signaling and cell cycle pathways and apoptosis.73 It was shown that 14-3-3 proteins may play a central role in PD and Lewybody disease pathogenesis.74,75 Xu et al. have suggested that the 14-3-3 protein family interacts with α-synuclein, which is a major constituent of Lewy bodies.75 The elevated levels of this complex formation could increase neuronal vulnerability to prooxidative agents such as Aβ,53 potentially contributing to the pathogenesis of Lewy-body variant of AD. Increased levels of 14-3-3 proteins were also found in AD and Down’s syndrome brains. On the other hand, it was shown that the presence of 14-3-3 proteins in the CSF may suggest a more rapid AD progression.76 Succinyl-CoA ligase is a mitochondrial energy metabolism enzyme involved in ATP production.77 It also plays a key role in the citric acid cycle, ketone metabolism, and heme synthesis. Our results demonstrate that succinyl-CoA ligase expression was increased in the hydrophilic fraction of brain with the age of Tg2576 mice. Similar observation of upregulation of the SCL levels was also observed in parkin knockout mice.78 3.5. Functional and Protein Network Analysis

To investigate longitudinal changes in protein expression in the Tg2576 mouse model, the differentially expressed (d.e.) proteins were further analyzed. In total, 276 out of 873 proteins were differentially expressed. The distribution between the hydrophobic and hydrophilic fraction of these proteins was quite similar, 54 and 46 percentages, respectively. The overlap of d.e. proteins was only 11%, which indicates that phase fractionation of the samples may improve the chance of detecting more protein changes. Cluster analysis was conducted using STEM, and the largest cluster contained 115 proteins (23 proteins expected, p-value = 2.5 × 10−50) out of the 276 d.e. proteins. All of these proteins displayed an age related decrease in relative expression compared to the age group M12. Enrichment analysis of these proteins resulted in two significant clusters (enrichment score 3.3 and 2.7, respectively). The first cluster contained 20 categories and the second 124 categories, respectively. There was a large overlap of proteins between categories such that a few typical categories represented the majority of significantly enriched categories (Table 2). Typical enriched categories were associated with energy production in the mitochondrion, such as oxidative phosphorylation and ion transport involved in ATP biosynthesis. In total, 57 proteins were identified to contribute to the enrichment (Table 3 and Figure 5). Further analysis of the protein−protein interaction of these proteins revealed that several key steps in the mitochondrial electron transport chain are affected. The largest numbers of proteins affected are involved in the transfer of electrons from NADH to the respiratory chain (Cx I) and mitochondrial membrane bound ATP synthases, which produces ATP from ADP in the presence of a proton gradient across the inner mitochondrial membrane (Cx V). A majority of these proteins (83%) are found in the hydrophobic fraction, with the exception of NDUV1 and ATPA. According to the gene-ontology analyses all of these proteins have been reported as deregulated in neurological diseases, such as Huntington,

a

57

Uniprot knowledge entry. bMolecular weight of proteins in Da according to UniProt. cGrand average of hydrophobicity. dTransmembrane helix.

membrane, mitochondrion 1 0.05 NDUV1_MOUSE

−0.25

−1.70

0.02

lipid metabolism 1 −0.28

hydroxyacyl-coenzyme A dehydrogenase, mitochondrial NADH dehydrogenase [ubiquinone] flavoprotein 1, mitochondrial

55

56

HCDH_MOUSE

−0.41

−1.27

0.03

electron transport

−0.16 32995

−0.20 68326

membrane, cytosol mitochondrion 3 0.01 −0.47 VATA_MOUSE

−0.32

−1.89

1 −0.60

dihydrolipoyllysine-residue succinyltransferase component of 2-oxoglutarate dehydrogenase complex V-type proton ATPase catalytic subunit A 54

ODO2_MOUSE

−0.56

−1.10

0.02

ion transport

−0.16 41469 mitochondrion

−0.78 11474 mitochondrion

apoptosis, electron transport tricarboxylic acid cycle 1 0.03 −0.36 CYC_MOUSE cytochrome c, somatic 53

entry protein name no.

Hydrophilic Fraction −0.94 −1.26

n peptides median log2(M22/ M12) median log2(M18/ M12) median log2(M15/ M12) a

Table 3. continued

Article

median p-value

biological pathway

location

MWb

GRAVYc


K



Article

Figure 5. (A) A heat map of proteins that contribute to the enrichment associated with energy metabolism. Several of these proteins are known to be affected in neurological diseases. The color scheme for the age group M15 through the group M22 illustrates the relative protein expression compared to the age group M12, where blue illustrates downregulation and red upregulation. The color red in the first eight gene-ontology groups illustrates that the gene for the protein has been associated with the ontology group, whereas gray illustrates that the protein has not been associated with the group. The color red in the group called “compartment” illustrates that the regulated protein is found in the hydrophilic compartment, and blue illustrates hydrophobic compartment. (B) A protein−protein interaction map. There were found several protein−protein interactions for proteins active in different parts of the mitochondrial energy chain: four complexes Cx I, Cx III, Cx IV, Cx V.

mitochondrial damage may play a pivotal role in the cell death decision. The Aβ aggregation is also likely to cause decrease in oxidative phosphorylation and mitochondrial permeability transition pore (mPTP) mediated apoptosis. Proteins from almost all parts of the respiratory complex displayed a decrease in expression with age. The ADT1−2 proteins (also known as Slc25a4 and Slc25a5), which catalyze the exchange of ADP and ATP across the membrane, were downregulated with increasing age. Several key mediators in the mPTP were found to be upregulated (VDAC 1,2,3). VDAC1 upregulation is known to result in altered functions of mPTP leading to apoptosis and mitochondrial dysfunction resulting in altered signal transduction pathways (Figure 6).

PD, and AD. One key protein in the mitochondrial electron transport chain is cytochrome C. Cytochrome C is a small highly water-soluble protein and is apart from NDUV1 and ATPA one of few proteins that has a clear decrease in expression. Decreased cytochrome C activity has been reported in several neurological diseases, and it is also believed to be a key protein in the regulation of apoptosis.79 3.6. Aβ Mediated Apoptosis

Our data suggests that the Tg2576 amyloid model is suitable for studying pathological mechanism downstream of Aβ. We believe that the disease pathology found in the Tg2576 mouse model is a result of Aβ mediated apoptosis, and this is also supported by data from human studies.53 There is a clear increase in Aβ levels as well as ApoE levels with age. Accumulation and oligomerization of Aβ is thought to play a central role in the pathogenesis of AD by probably directly leading to mitochondrial dysfunction. At the same time,

4. CONCLUSIONS In summary, this study illustrates the potential of proteomic applications to identify sets of pathology-specific proteins L



Article

Figure 6. Aβ mediated apoptosis. A schematic figure illustrating proteins and key functions in the mitochondrial energy metabolism, which were found to be affected in this study (yellow stars). In conclusion, the age related degeneration of brain cells seem to be an apoptosis mediated process, driven by an increase in β-amyloid plaque formation. The lower graphs show the longitudinal changes of ApoE, Cyt C, and several key mediators in the mPTP (ADT1−2, VDAC 1,2,3). Abbreviations: ApoE, apolipoprotein E; Cx I−V, energy chain parts I−V; Cx I, complex I (NADH dehydrogenase); Cx II, complex II (succinate dehydrogenase); Cx III, complex III (cytochrome bc1 complex); Cx IV, complex IV (cytochrome c oxidase); Cx V, complex V (ATP synthase); ABAD, amyloid beta-binding alcohol dehydrogenase; mPTP, mitochondrial permeability transition pore; IDE, insulin-degrading enzyme; NEP, membrane metallo-endopeptidase; LPL, lipoprotein lipase; LPL, low density lipoprotein receptor-related protein 1; Cyt C, cytochrome c, somatic; Apaf1, apoptotic peptidase activating factor 1; CASP9, caspase 9, apoptosis-related cysteine peptidase; CASP9, caspase 3, apoptosis-related cysteine peptidase.

pathogenesis as well as for the discovery of potential biological markers.

linked to individual neurodegenerative diseases, as well as proteins involved in processes common to many neurodegenerative diseases. In total, a set of 10 proteins were found to be significantly upregulated early in the brain of Tg2576 mice. These proteins play roles in mitochondrial function (VDAC 1−3; prohibitin); energy metabolism (cytochrome b-c1 complex subunit 1); cellular transport (dynamin); lipid metabolism (apolipoprotein E); cytoskeletal structural integrity (14-3-3 protein beta/alpha); energy metabolism/mitochondrial function (succinyl-CoA ligase subunit beta); and neuronal communication (NCAM-1). We have also found clear indications that the age related pathology progression probably is partly mediated by apoptosis initiated by the sharp increase of Aβ occurring between 12 and 15 months of age. This report is our initial study of age related changes in brains of mice and, as such, forms a framework for our future studies, which are currently in progress. Our study may provide knowledge for the identification of disease

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ASSOCIATED CONTENT

* Supporting Information S

PCA plot of the hydrophobic and hydrophilic brain proteome fractions of the Tg2576 brain at different ages. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: +46 18 471 3688. Fax: +46 18 471 3692. Notes

The authors declare no competing financial interest. M



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ACKNOWLEDGMENTS This research was supported by Uppsala Berzelii Technology Centre for Neurodiagnostics, with financing from the Swedish Governmental Agency for Innovation Systems and the Swedish Research Council P29797-1 JB grant (621-2008-3562). M.W. further acknowledges Lars Hiertas Minne Foundation (FO2009-0695) and Signe och Olof Wallenius Foundation (R103). K.K. acknowledges Stiftelsen Olle Engkvist Byggmästare for the financial support.

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