Psychoproteomic Analysis of Rat Cortex Following Acute Methamphetamine Exposure Firas H. Kobeissy,*,†,‡ Matthew W. Warren,† Andrew K. Ottens,† Shankar Sadasivan,† Zhiqun Zhang,† Mark S. Gold,†,‡,§ and Kevin K. W. Wang*,†,‡ Center for Neuroproteomics and Biomarkers Research, Department of Psychiatry, Center for Traumatic Brain Injury Studies, Department of Neuroscience, College of Medicine, Department of Community Health and Family Medicine, McKnight Brain Institute of the University of Florida, Gainesville, Florida 32610 Received October 8, 2007
Methamphetamine (METH) is recognized as one of the most abused psychostimulants in the United States. METH is an illicit drug that is known to exert neurotoxic effects on both dopaminergic and serotonergic neural systems both in vivo and in vitro. Our laboratory and others have been studying the biochemical mechanisms underlying METH-induced neurotoxicity. Here, we applied a novel psychoproteomic approach to evaluate METH-induced neurotoxicity following acute METH administration (4 × 10 mg/kg, ip injections every 1 h). Samples of cortical tissue collected 24 h post METH treatment were pooled, processed and analyzed via a selective psychoproteomic platform. Protein separation was performed using our previously established offline tandem cation-anion exchange chromatography-SDS-1D-PAGE platform (CAX-PAGE). Gel bands exhibiting 2 or more fold changes were extracted, trypsinized and subjected to reversed-phase liquid chromatography-tandem mass spectrometry (RPLCMS/MS) analyses for protein identification. Differential changes of the selected proteins were further confirmed by quantitative immunoblotting. We identified 82 differentially expressed proteins, 40 of which were downregulated and 42 of which were upregulated following acute METH treatment. Proteins that decreased in abundance included collapsin response mediator protein-2 (CRMP-2), superoxide dismutase 1 (SOD 1), phosphatidylethanolamine-binding protein-1 (PEBP-1) and mitogen activated kinase kinase-1 (MKK-1). Proteins that increased in abundance included authophagy-linked microtubuleassociated protein light chain 3 (LC3), synapsin-1, and Parkinsonism linked ubiquitin carboxy-terminal hydroxylase-L1 (UCH-L1). Lastly, we used these differentially expressed protein subsets to construct a “psychoproteomic” spectrum map in an effort to uncover potential protein interactions relevant to acute METH neurotoxicity. Keywords: Autophagy • neurotoxicity • methamphetamine • proteomics • drug of abuse • proteolysis
Methamphetamine (METH) abuse is a growing epidemic worldwide. The use of METH has increased dramatically in the past two decades as evidenced by emergency room visits arising from METH intoxication.1–3 The United Nations Office on Drugs and Crime estimates that 30 million people regularly use amphetamines, compared to 13 million users of cocaine.4 In addition, according to a recent publication from the Center for Substance Abuse research, it has been shown that the number of new METH users has increased in past years, peaking in
2004, with approximately 300 000 users in the U.S.5 METH is an illicit, addictive psychostimulant drug that is known to be neurotoxic to the dopaminergic and serotonergic systems in the brain. METH neurotoxicity has been demonstrated in the striatum, and has also been investigated in other brain regions including the frontal cortex, hippocampus, and cerebellum.6–10 Because the general neurotoxic effects observed in METH abuse are highly associated with the degeneration of the serotonergic and dopaminergic systems, METH abuse leads to widespread, selective axonal terminal damage that is coupled with neuronal degeneration.11,12
* Corresponding authors: Dr. Kevin K. W. Wang (e-mail, kwang@ psychiatry.ufl.edu) and Dr. Firas Kobeissy (e-mail,
[email protected]); Department of Psychiatry, University of Florida, L4-100F, (P.O. Box 100256), Gainesville, FL 32611. Fax: (352) 392-2579. † Center for Neuroproteomics and Biomarkers Research, Department of Psychiatry, McKnight Brain Institute of the University of Florida. ‡ Center for Traumatic Brain Injury Studies, Department of Neuroscience, McKnight Brain Institute of the University of Florida. § College of Medicine, Department of Community Health and Family Medicine, McKnight Brain Institute of the University of Florida.
In addition, it has been proposed that oxidative stress plays a major role in METH neurotoxicity. The proposed mechanism involves METH-induced redistribution of dopamine (DA) and serotonin (5-HT) from the vesicular storage pool to the cytoplasm and extracellular space, as well as the subsequent oxidation of DA into quinones and other reactive species that induce different forms of neuronal injury.12 Interestingly, studies on rat neocortical neurons and on dopamine-prede-
Introduction
10.1021/pr800029h CCC: $40.75
2008 American Chemical Society
Journal of Proteome Research 2008, 7, 1971–1983 1971 Published on Web 05/02/2008
research articles pleted neurons have shown that METH can induce neurotoxicity independent of dopamine.8,11,13,14 Furthermore, recent in vivo and in vitro studies from our laboratory and others have implicated apoptotic and necrotic pathways activated in METH neurotoxicity.7,12,15,16 The mechanism common to both pathways involves an increase in intracellular calcium concentrations that result from cross-talk between endoplasmic reticulum stress and the mitochondria.15 The increase in calcium leads to activation of caspases and calcium-dependent protein calpains, culminating in apoptotic and necrotic cell death, respectively.7,15 Our laboratory has recently reported that in vivo acute METH treatment induced neurotoxicity in both the cortex and hippocampus brain regions.17,18 METH treatment leads to the activation of two major protease systems (the pro-necrotic calpain and the pro-apoptotic caspase system) which in turn lead to cytoskelatal damage of structural proteins such as RIIspectrin and microtubule-associated protein tau.18,19 As such, the accumulation of corresponding breakdown products of these cytoskelatal proteins can be considered strong evidence of neuronal injury.16–18 Studies by Larsen et al. and Kanthasamy et al. have demonstrated that METH promotes the formation of autophagic bodies within the cell bodies of dopaminergic neurons.20,21 This mechanism may represent an additional mechanism involved in METH-induced cell injury, in addition to necrotic and apoptotic mechanisms. Taken together, results from these studies suggest that METH-induced neurotoxicity may involve apoptotic, necrotic and autophagic cell death mechanisms. In this study, we sought to further investigate the cellular and molecular mechanisms underlying neuronal injury in brain tissues exposed to an acute METH treatment using a proteomic technique recently developed in our laboratory.22–24 In the current study, changes in cortical protein expression in rats treated with acute METH were identified using a multistep protein separation/proteomic platform and confirmed using immunoblotting technique. Proteins were fractionated using tandem ion exchange chromatography, separated using 1DSDS-PAGE, and identified using reversed-phase liquid chromatography-tandem mass spectrometry (CAX-PAGE/RPLCMS/MS).25–27 Differentially expressed protein subsets were used to construct a “psychoproteomic” functional-interaction map that may clarify potential protein interactions relevant to acute METH neurotoxicity. We hypothesize that our “psychoproteomic” platform, when coupled with bioinformatics analysis of protein interaction, will enable us to better understand the psychoproteome dynamics and neurotoxic pathways post-acute METH treatment.
Materials and Methods In Vivo Model of Methamphetamine Neurotoxicity. All procedures involving animal handling and processing were done in compliance with guidelines set forth by the University of Florida Institutional Animal Care and Use Committee and the National Institutes of Health guidelines. All experiments were performed using male Sprague-Dawley rats (Harlan) that were aged 60 days and weighed between 250 and 275 g. Experimental groups were divided into two groups (n ) 7), with each group receiving either 40 mg/kg METH or physiological saline. Pharmacologic agent (+/-) methamphetamine hydrochloride (Sigma-Aldrich, St. Louis, MO) was dissolved in 0.9% saline. Rats were intraperitoneally injected with 10 mg/kg doses of METH four times every hour to achieve the desired dosage 1972
Journal of Proteome Research • Vol. 7, No. 5, 2008
Kobeissy et al. of 40 mg/kg in a bolus of 0.3 mL. The Saline group (vehicle group) received similar injection schedules of physiological saline. At 24 h post-intraperitoneal injection, treated animals were briefly anaesthetized with 3-4% isoflurane and were sacrificed by decapitation. METH and saline cortex samples were rapidly dissected, washed with saline solution, snap-frozen in liquid nitrogen, and stored at -80 °C for further processing. METH/ saline cortex tissues were homogenized using a small mortar and pestle set over dry ice. Homogenates were lysed for 90 min at 4 °C with 0.1% SDS lysis buffer containing 150 mM sodium chloride, 1% ethoxylated octylphenol, 1 mM sodium vanadate, 3 mM ethylenediaminetetraacetic acid, 2 mM ethylene glycol bis (2-aminoethyl ether)-tetraacetic acid, and 1 mM dithiothreitol (all from Sigma-Aldrich, St. Louis, MO), with a Complete Mini protease inhibitor cocktail tablet (Roche Biochemicals, Indianapolis, IN). Cortex lysates were then centrifuged at 15 000g for 10 min at 4 °C. The supernatant was retained and collected at 4 °C to prevent proteolysis. The protein content was determined using a DC Protein Assay (Bio-Rad Laboratories, Inc., Hercules, CA), after which the protein concentration was standardized to 1 µg/µL for immunoblotting analysis. Immunoblot Analysis and Antibodies. For the immunoblotting experiments, METH and saline samples were processed with 2× Laemmli sample buffer (Bio-Rad with 5% β-mercaptoethanol). Twenty micrograms of protein from each sample was heated for 10 min at 96 °C, centrifuged for 1 min at 10 000g, and then resolved by SDS-PAGE-Tris/Glycine gels (Invitrogen Life Technologies) at 150 V for 90 min. Following electrophoresis, the fractionated proteins were transferred to polyvinylidene fluoride (PVDF) membrane (Invitrogen) by the semidry method in a transfer buffer (39 mM glycine, 48 mM Tris, and 5% methanol) at 20 V for 2 h at room temperature. Following the transfer, the membranes were blocked in 5% nonfat dry milk in TBST (20 mM Tris-HCl, 150 mM NaCl, and 0.003% Tween20, pH 7.5) for 1 h and then incubated overnight with the primary antibody at 4 °C. On the following day, the membranes were washed three times with TBST, and probed with the secondary antibody for 1 h. Immunoreactivity was detected by using streptavidin-conjugated alkaline phosphatase. Monoclonal anti-RII-spectrin (Affiniti Research Products, Ltd., U.K.) and anti-β actin (Sigma Chemical Co., St. Louis, MO) were used at a dilution of 1:4000 in 5% milk. Antibodies for synapsin-1 (BD Biosciences, NJ), UCH-L1 (gift from Dr. Monica Oli, Banyan Biomarkers, Inc., Alachua, FL), anti-light chain 3 (LC3) (AntiLC3 antibody was raised in rabbits against a synthetic peptide corresponding to the N-terminal of LC3),28 anti-Map kinase kinase-1 (MKK-1) (Cell Signaling Technology, Beverly, MA), superoxide dismutase1 (SOD1) (gift from Dr. David Borchelt laboratory at the McKnight Brain Institute of the University of Florida, Gainesville, FL), anti-phosphatidylethanolamine-binding protein-1 (PEBP-1) (Abcam Ltd., Cambridge, U.K.), and antiCRMP-2 (IBL, Japan) were used at a dilution of 1:1000 in 5% milk. Secondary biotinylated antibodies (Amersham Biosciences, U.K.) and streptavidin-conjugated alkaline phosphatase (Amersham Biosciences, U.K.) were used at a dilution of 1:3000 in 5% milk. Gel Band Visualization and Quantification. For the SDS1D PAGE visualization, protein fractions were run side-by-side on 10-20% gradient Tris-HCl gels (Bio-Rad) and were visualized with Coomassie Blue staining for differential gel bands selection. NIH ImageJ densitometry software (version 1.6, NIH, Bethesda, MD) was used for lane and band detection providing
Psychoproteomics of Acute Methamphetamine Neurotoxicity differential comparison between the saline and METH band densitometric analysis. Fold increase or decrease between METH and saline samples was computed by dividing the greater value by the lesser value with a negative sign to indicate a decrease after METH treatment. Statistical Analysis of Immunoblotting Data. Densitometric quantification of the immunoblot bands was performed using an Epson Expression 8836XL high resolution flatbed scanner (Epson, Long Beach, CA) and NIH ImageJ densitometry software. Data were acquired as integrated densitometric values and transformed to percentages of the densitometric levels. Densitometry values of the four different individuals of saline and METH samples were evaluated for statistical significance using SigmaStat software (Version 2.03, Systat Software, Inc.) using a Student’s t test. A P-value of 5 kDa. Leammli sample buffer (25 µL) was added to the YM-10 collection filters prior to collection by centrifugation at 3500 rpm for 3 min. For SDS-1D-PAGE analysis, METH and saline protein fractions were run side-by-side (i.e., saline fraction 1 next to METH fraction 1, etc.) using 10-well, 10-20% gradient Tris-HCl gels (Bio-Rad) for differential comparison of METH versus Saline samples. NIH ImageJ software (version 1.6, NIH, Bethesda, MD) was used for quantitative densitometric analysis of gel band intensity. Differential bands were boxed and labeled according to their 2D-position (e.g., the top band excised from the lane of fraction 1 was labeled 1A). Psychoproteome Interaction Map. Protein interaction map of acute METH proteins was searched using the Stratagene
research articles database (www.stratagene.com) and was built using Stratagene PathwayArchitect software package version 2.0.1 (Stratagene, La Jolla, CA). PathwayArchitect is a software for analyzing functional, post-translational modifications and metabolism interactions. It allows the identification and visualization of pathways, constructing regulation networks and proteomic interaction maps. In-Gel Digestion and Reversed-Phase Liquid Chromatography Tandem-Mass Spectrometry. A detailed description of the reversed-phase liquid chromatography tandem-mass spectrometry (RPLC-MS/MS) platform was described elsewhere.26 In brief, differential bands were excised, cut into pieces and washed with HPLC water (Burdick & Jackson, Muskegon, MI) followed by 50:50 100 mM ammonium bicarbonate/acetonitrile (Burdick-Jackson, HPLC grade). Bands were dehydrated with 100% acetonitrile, then rehydrated with 10 mM dithiothreitol (DTT) for 30 min at 56 °C, and then alkylated with 55 mM iodoacetamide in 50 mM ammonium bicarbonate for 30 min in the dark at room temperature followed by acetonitrile dehydration. For protein digestion, 15 µL of a 12.5 ng/µL trypsin solution was added and incubated for 30 min at 4 °C. An additional 20 µL of 50 mM ammonium bicarbonate was then added and that mixture was incubated overnight at 37 °C. The resulting peptide solution was collected and the hydrophobic peptide extraction was performed with 50% acetonitrile/5% acetic acid. The peptide solution was dried by speed vacuum, and then resuspended in 20 µL of mobile phase solution (4% acetonitrile/0.4% acetic aid) for RPLC-MS/MS analysis. Capillary reversed-phase liquid chromatography tandem ion trap mass spectrometry was employed for protein identification by loading 2 µL of sample digest via autosampler. Nanoflow reversed-phased chromatography was performed with a 100µm × 5-cm capillary column packed in-house with Agilent (Palo Alto, CA) 3-µm C18 particles behind an Upchurch 0.5-um PEEK microfilter assembly. As for the gradient, a 30-min gradient of 4% HPLC acetonitrile/0.4% acetic acid (Fisher, Optima grade) to 60% acetonitrile/0.4% acetic acid was used to elute tryptic peptides. Tandem mass spectra were collected on a ThermoElectron (San Jose, CA) LCQ Deca XP-Plus using data-dependent analysis. Database Search. Our protein searches and peak list were generated using Bioworks version 3.2 (based on the Sequest algorithm) (Thermo Electron Corporation, San Jose, CA). The resulting peak lists were searched against a nonredundant Rattus norvegicus (proteome ID 122, download July 11, 2006, with 11 987 protein entries) fasta database released by the Integr8 project (http://www.ebi.ac.uk/integr8) derived from the EMBL Uniprot data set (ver. 8.3). SRF files were generated for the MS/MS spectra with minimal intensity (5 × 104) and a sufficient number of ions of (>35). The database was indexed for trypsin (lysine and arginine) with monoisotopic precursor and fragment mass values, allowing for partial enzymatic cleavage (lysine and arginine at either end) and 2 missed cleavage sites. Peptides included ranged between 500 and 3500 amu. For the search parameters, peptide tolerance was set to 0.75 amu for parent and fragment ions. Differential protein modifications were considered for oxidized methionine (+16) and phosphorylated serine, tyrosine and threonine (+79.9). The complete list of identified proteins is presented in Table 1 indicating the peptide sequence coverage percentage of the highly expressed protein. Most proteins identifications were based on a minimum of two-peptide assignment. Proteins sharing similar peptides were counted for the protein that has Journal of Proteome Research • Vol. 7, No. 5, 2008 1973
1974
gel molecular mass (kDa)
61 59 49 28
50 50
20 85 15 112
102
51
28
25 49 25 14 50
72
70
27
60
83
16
20 18
band
1A 1B 1C 1D
2A 3B
3E 4B 4D 5A
5B
5C
5D
5E 6A 6C 6D 9A
12A
12B
12C
13A
17A
17C
1E 1F
Journal of Proteome Research • Vol. 7, No. 5, 2008 20 18
61 63 47 29 31 27 52 52 51 21 85 14 113 113 102 97 51 52 50 53 28 28 29 25 51 25 15 56 50 72 74 74 70 70 26 29 60 61 59 83 83 14
cal. molecular mass (kDa) protein name
Proteins with Decreased Abundance Post METH Treatment P31044 Phosphatidylethanolamine-binding protein 1 P45592 Cofilin-1
Proteins with Increased Abundance Post METH Treatment P38652 Phosphoglucomutase-1 Q6P6V0 Glucose phosphate isomerase P06733 Alpha enolase P27139 Carbonic anhydrase 2 XP_213637 Nitrilase family, member 2 P48500 Triosephosphate isomerase P51650 Succinate semialdehyde dehydrogenase Q8VHF5 Citrate synthase Q9WTT6 Guanine deaminase (Guanase) P84079 ADP-ribosylation factor 1 Q9ER34 Aconitate hydratase, mitochondrial precursor Q9H492 Microtubule-associated proteins 1A/1B light chain 3A (LC3) P22063 Contactin-2 precursor Q63198 Contactin-1 precursor (Neural cell surface protein F3) P05708 Hexokinase-1 (Brain specific) P09812 Glycogen phosphorylase Q5XIL1 ATPase, H+ transporting, lysosomal 50/57 kDa P70645 Bleomycin hydrolase Q9WVC0 Septin-7 Q68FY0 Ubiquinol-cytochrome c reductase core protein-1 P25113 Phosphoglycerate mutase 1 Q68FU3 Electron transfer flavoprotein subunit beta p52555 Endoplasmic reticulum protein ERp29 O35244 Peroxiredoxin-6 (Antioxidant protein 2) P50399 Rab GDP dissociation inhibitor beta Q00981 Ubiquitin carboxyl-terminal hydrolase-L1 P37377 Alpha-synuclein P10719 ATP synthase beta chain, (mitochondrial) P50398 Rab GDP dissociation inhibitor alpha P06761 78 kDa glucose-regulated protein precursor (GRP 78) P48721 Stress-70 protein, mitochondrial precursor P09951 Synapsin-1 P14659 Heat shock-related 70 kDa protein 2 P63018 Heat shock cognate 71 kDa protein Q91Y78 Ubiquitin carboxyl-terminal hydrolase isozyme-L3 P07171 Calbindin-1 P08413 Calcium/calmodulin-dependent protein kinase type II beta P63039 60 kDa heat shock protein, mitochondrial precursor P63329 Serine/threonine-protein phosphatase 2B catalytic subunit P34058 Heat shock protein HSP 90-beta (HSP 84) Q03555 Heat shock protein HSP 86 Q16143 Beta-synuclein
ptn accn no.
Table 1. Identification of Differentially Expressed Proteins Using CAX-PAGE/RPLC-MS/MS Psychoproteomic Platform Post METH Treatmenta
9 7
8 3 4 3 2 0 0 12 6 5 8 3 4 6 15 2 0 0 0 0 4 0 0 2 5 2 0 3 6 4 0 3 2 7 0 0 2 6 0 17 12 2
no. pep. in saline
0 2
13 15 7 5 4 3 8 19 8 8 13 0 11 10 19 5 3 5 4 8 7 4 3 9 8 4 5 15 9 16 4 8 4 10 4 7 4 9 5 22 14 5
no. pep. in METH
73 58
33 38 25 29 23 20 24 63 24 61 25 28 16 13 27 7 13 17 11 20 39 20 17 68 25 26 64 43 28 34 10 18 10 21 27 32 9 20 13 46 25 54
% cov.
research articles Kobeissy et al.
16 60
40
24
98
45 35
44
95 85
60
35
30
140 48
115 105
48
2B 3A
3C
3D
4A
4C 6B
7A
8A 8B
10A
11A
14B
15A 17B
19A 20A
20B
50 50
28 30 30 138 50 47 50 13.0 104
36 39 25 22 97 98 47 36 36 36 36 43 43 47 95 86 77 62 62 67 37
16 57
cal. molecular mass (kDa) protein name
Proteins with Increased Abundance Post METH Treatment P07632 Superoxide dismutase-1 [Cu-Zn] P11980 Pyruvate kinase isozymes M1/M2 Q6P6V0 Glucose phosphate isomerase P51635 Alcohol dehydrogenase [NADP+] P05065 Fructose-bisphosphate aldolase A P07895 Superoxide dismutase [Mn], mitochondrial precursor Q63716 Peroxiredoxin-1 (Thioredoxin peroxidase 2) P09812 Glycogen phosphorylase Q63270 Iron-responsive element-binding protein 1 P00507 Aspartate aminotransferase, mitochondrial precursor O88989 Malate dehydrogenase, cytoplasmic P04636 Malate dehydrogenase, mitochondrial precursor P04797 Glyceraldehyde-3-phosphate dehydrogenase P04642 L-lactate dehydrogenase A chain P07335 Creatine kinase B-type Q01986 Mitogen-activated protein kinase kinase-1 P49185 Mitogen-activated protein kinase-8 P05197 Elongation factor 2 P47860 6-phosphofructokinase type C P05696 Protein kinase C alpha Q62950 Dihydropyrimidinase-related protein 1 (CRMP-1) P47942 Dihydropyrimidinase-related protein 2 (CRMP-2) P61765 Syntaxin-binding protein 1 P09651 Heterogeneous nuclear ribonucleoprotein A1 P22626 Heterogeneous nuclear ribonucleoprotein A2/B2 Q6P503 ATPase, H+ transport, V1 subunit D Q5XI32 F-actin capping protein Q5XIU5 Proteasome inhibitor protein P10687 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase P26641 Elongation factor 1-gamma Q15120 Pyruvate dehydrogenase Q05639 Elongation factor 1-alpha 2 P06685 Sodium/potassium-transporting ATPase alpha-1 chain P62944 AP-2 complex subunit beta-1 Q66HM2 AP-2 alpha 2 subunit P04691 Tubulin beta chain P68370 Tubulin alpha-1 chain P69897 Tubulin beta-5 chain
ptn accn no.
4 30 7 13 3 5 3 14 9 15 5 9 5 4 4 12 3 14 8 6 6 4 5 4 9 5 3 3 11 6 2 4 8 4 4 8 6 3
no. pep. in saline
0 23 0 2 0 3 0 10 4 10 2 8 3 0 0 3 0 9 4 2 3 2 0 2 6 4 0 0 4 4 0 2 5 0 2 3 3 0
no. pep. in METH
38 69 19 56 12 26 17 23 13 48 22 38 22 16 18 43 11 20 16 13 16 10 11 16 36 28 15 16 14 21 7 11 12 6 10 26 21 11
% cov.
a Differentially expressed proteins (increased or decreased abundance in METH tissue lysates) are shown with their respective migrating gel molecular mass [gel molecular mass (kDa)] and the mass spec sequence derived molecular mass [cal. molecular mass (kDa)]. Protein name along with the NCBI accession number (ptn accn no.) are shown and these are searched against rat database. Percent sequence coverage (% cov.) of the higher number of peptides is included with the number of peptides (no. pep.) of each protein identified. Each gel band is given a number/letter label indicative of the position of the excised band from the different gel lanes, i.e., a band with a 6A label represents the first top band excised from lane 6.
gel molecular mass (kDa)
band
Table 1. Continued
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Figure 1. A Schematic illustration of the differential CAX-PAGE/RPLC-MS/MS psychoproteomic platform. The schematic diagram illustrates the 9 sequential steps following acute administration of METH followed by CAX chromatography and 1D-PAGE separation, as the first and second dimension. After CAX-PAGE separation, selected differential protein bands are excised and in-gel digested followed by RPLC-MS/MS generating a differential protein list. Selected protein subsets are then subjected for validation via immunoblotting. Subsequently, using PathwayArchitect software, a functional interactive map is constructed based on the psychoproteomic data.
the most matching peptides. Thus, our protein list represents the smallest set of proteins of the identified peptides. For the acceptance criteria used for peptide identification, we used the stringent filtering of the database search results based on retaining peptides with probability p(pep) < 1 × 10-3 which is embedded in the Bioworks 3.2 interface along with criteria of two peptides per protein and no replicate peptide entries. This processing algorithm is based on the Sequest scores Xcorr and ∆Cn that allow accurate predictions of random matching distributions allowing for a precise calculation of the probabilities associated with individual peptide assignment as well as of the false discovery rate among the peptides identified. Thus, the obtained results led to identification confidence higher than 95% as illustrated by Lopez-Ferrer et al.29,30
Results CAX-PAGE/RPLC-MS/MS Psychoproteomics Experimental Design. With the goal of comprehensive differential proteome identification, this study utilized our previously described differential proteomics platform to evaluate the psychoproteomic changes post METH treatment. Our platform consisted of 9 sequential steps illustrated in Figure 1. Our experimental design called for two groups of pooled rat cortical samples from acute METH-treated rats after 24 h post ip injection and saline injected controls (n ) 7 each). Quality control analysis of our acute METH samples was conducted prior to the psychoproteomic analysis. In brief, our platform included protein separation technique via dual ion exchange (CAX) chromatography which was followed by 1D-PAGE separation. The second phase included differential analysis of the 1D-PAGE protein band intensities, any gel band pair with 2 or more fold changes was selected for proteomic analysis. Reversed-phase liquid chromatography-mass spectrometry analysis (RPLC-MS/MS) was performed on the differential protein bands; psychoproteomic 1976
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data were presented in Table 1. Relevant identified protein hits were subjected for immunoblotting validation performed on individual samples. Finally, the differential protein list in Table 1 was used to construct an interaction map illustrating the differential pathway(s) involved in acute METH abuse. Accumulation of 145 and 150 kDa rII-Spectrin Breakdown in Acute METH-Treated Animals as Positive Control. Prior to the psychoproteomic analysis, METH-treated rats were evaluated for METH-induced neurotoxicity by monitoring RIIspectrin breakdown products as markers for neuronal cell death.7,18,31 Thus, all seven individual acute METH cortical samples were compared to the seven individual saline cortical samples for the accumulation of RII-spectrin breakdown products (RII-SBDPs) as this is considered an acceptable approach reflecting on METH neurotoxicity as shown in various studies.16,18,31,32 Immunoblotting data of the RII-spectrin protein revealed the accumulation of the 145 and 150 kDa RIIspectrin breakdown products (RII-SBDPs) in the cortical samples of the acute METH-treated animals as compared to the saline samples (Figure 2). The presence of the 145 and 150 kDa SBDPs is indicative of the METH-induced neurotoxicity via the subsequent activation of caspase-3 and calpain proteases mediating the degradation of known cytoskeletal proteins as we have previously published.7,18 Similarly, immunobloting of β actin serving as a protein loading control was performed on the seven individual acute METH cortical samples and on the seven control cortical samples. No statistical significance was shown reflecting an equal protein loading in both conditions as shown in Figure 2. As for the validation step of the identified mass spectrometry proteins, we selected four out of the seven individual control/METH-treated samples to validate the trend of protein change post METH treatment. CAX-PAGE Protein Separation. Pooled protein cortical samples from saline and METH-treated animals were sequen-
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Figure 2. Quality control of the METH-induced neurotoxicity. Immunoblots of RII-spectrin breakdown products (RII-SBDPs) reveal the generation of SBDP 145 and SBDP 150 in the cortical samples of all the seven METH-treated animals and saline controls. The presence of SBDP 145 and SBDP 150 is indicative of METH-induced neurotoxicity via the activation of caspase-3 and calpain proteases.18 Graphical representation of the densitometric analysis shows elevated levels of SBDP 145 and SBDP 150. Student’s t test was performed to evaluate statistical significance, (*p < 0.05; mean ( SEM; n ) 4). Data are expressed in Arbitrary Density Units. Immunoblot of β actin serving as a protein loading control shows equal loading in all individual control and treated samples. Saline (S) samples are represented by white bars, and METH (M) samples are represented by gray bars.
tially separated by CAX chromatography and the two separation chromatograms are shown overlaid in Figure 3. The initial impression after the first dimension separation was that there was a visible difference between the two proteomes with optimal distribution of protein along the 23 fractions. Twenty of the collected fractions (20 out of 23) from each CAX chromatography experiment were paired (i.e., fraction 1 of saline was paired with fraction 1 of acute METH) and loaded side-by-side onto 1D-SDS polyacrylamide gel for the second dimension protein separation (Figure 4). The gels were visualized with Coomassie Blue staining for differential gel band detection which were boxed and labeled as shown in Figure 4. Differential Gel Band Analysis. Relative fold changes (increase/decrease) of differential protein gel bands were calculated based on the relative intensities between saline and METH bands. Twenty gel bands were found to show 2 or more fold decrease, while 24 gel bands which were found to exhibit 2 or more fold increase (Supplementary Figure 1) Fold changes were compared to data obtained from the proteins identification which highly correlated with band intensity (decrease or increase). An average of number of two proteins was identified per band. Identification of Differentially Expressed Proteins by RPLCMS/MS. Following CAX-PAGE differential band selection, peptide separation and analysis was achieved using RPLC-MS/MS. Tandem MS-generated tryptic peptide sequences were searched using Bioworks Browser against a rat-indexed protein database revealing between zero and four proteins per gel band, each
Figure 3. Overlay of METH and saline CAX liquid chromatograms. CAX liquid chromatography separations of METH and saline pooled cortical lysates (n ) 7) are overlaid using the 280 nm absorbance scale showing individual CAX chromatogram of each lysate: saline (black), methamphetamine (gray), and the conductivity (light gray). Twenty-three fractions were collected from each chromatographic run.
having two or more peptides. For those bands with multiple identified proteins, we utilized the number of matched peptides per protein as a semiquantitative measure of protein abunJournal of Proteome Research • Vol. 7, No. 5, 2008 1977
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Figure 4. Comparison of rat METH and saline psychoproteomes by sequential CAX-1D- SDS-PAGE separation. Shown is the side-byside (saline on the left and METH on the right) pairing of 20 fractions of the 23 collected fractions on 1D-PAGE. Selected bands are boxed and letter labeled for correlation later with Table 1. Differential bands were number/letter labeled according to their position in each specific gel lane, i.e., a band with a 6A label represents the first top band excised from lane 6. These bands are then excised for subsequent RPLC-MS/MS identification.
dance to confirm which protein represents the observed differential gel pattern as illustrated by Peng et al.33 Thus, in most cases, we were able to isolate an average of two proteins per gel piece. In a few cases, three or more proteins were identified (Table 1). The 82 proteins were presented with their respective protein accession number along with the Mass Spectrometry derived molecular weight (cal. MW), and SDS-PAGE apparent molecular mass (gel MW) as shown in Table 1. The identified proteins were grouped into decreased (40 proteins) or increased (42 proteins) in abundance post METH treatment. The proteins that decreased in abundance post-acute METH included the several cytosolic glycolytic proteins (glycerdehyde-3-phosphate dehydrogenase, glycogen phosphorylase, aldehyde dehydrogenase and malate dehydrogenase), cytoskeleton-associated proteins [F-actin capping protein; and the neurite guidance protein collapsin response mediator protein-2 (CRMP-2)], oxidative stress associated proteins [superoxide dismutase 1 (SOD1) and superoxide dismutase 2 (SOD2)], cytosolic cell signaling proteins [MAP kinase kinase-1 (MKK-1), MAP kinase 8 (MAP kinase 8)] and the neuronal phosphatidylethanolaminebinding protein-1(PEBP-1). Proteins that increased in abundance post METH treatment included glycolytic proteins (glucose phosphate isomerase, hexokinase and citrate synthase), the ubiquitin proteosome system proteins [ubiquitin carboxyl-terminal hydrolase isozyme-L1 (UCH-L1) and ubiquitin carboxyl-terminal hydrolase isozyme-L3 (UCH-L3)] and 1978
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the autophagic microtubule-associated proteins 1A/1B light chain 3A (LC3 and LC3-II) (see Table 1). Validation of Proteins with Increased Abundance after Acute METH Treatment. A number of relevant proteins were selected for validating the mass spectrometry-identified upregulated protein hits post METH treatment using the immunoblotting technique. These included UCH-L1, synapsin-1 and LC3 proteins and the processed form LC3-II protein. Densitometric analysis showed a statistically significant increase in the acute METH samples of UCH-L1, synapsin-1, and LC3 and LC3II proteins (p < 0.05; Student’s t test) compared to the saline samples (Figure 5A). The biological significance of these proteins is discussed later. Validation of Proteins with Decreased Abundance after METH Treatment. Of the proteins decreased in abundance after acute METH treatment, four different proteins were subjected to biochemical validation using the immunoblotting technique. These included phosphatidylethanolamine-binding protein-1 PEBP-1, SOD1, MKK-1 and CRMP-2 protein (Figure 5B). Our selection was based on several factors including antibody availability, literature relevance, and levels of peptide abundance. On the basis of these criteria, several proteins were excluded from the validation step which is considered to be the bottleneck in the field of proteomics where the discovery rate of potential biomarkers exceeds the rate of preliminary validation by several folds.34 Densitometric analysis showed a statistically significant decrease in acute METH samples of
Psychoproteomics of Acute Methamphetamine Neurotoxicity
research articles
Figure 5. Validation of acute METH altered proteins in individual saline and METH cortex. Shown is the immunoblot analysis of intact UCHL-1 (52 kDa), synapsin-1 (72 kDa) and intact LC3 (21 kDa) and LC3-II (18 kDa) proteins from four cortical METH-treated samples and saline control samples (n ) 4). These immunoblots show higher protein abundance in acute METH samples compared to the saline samples. Graphical densitometric analysis shows elevated proteins (UCH-L1, synapsin-1, LC3, and LC3-II) in METH-treated samples compared to the saline controls (A). Immunoblot analysis of intact SOD1 (19 kDa), MAPKK (40 kDa), CRMP-2 (62 kDa) and PEBP-1 (20 kDa) proteins from four cortical METH treated samples and saline control samples (n ) 4). These immunoblots show lower protein abundance in acute METH samples compared to the saline (n ) 4). Immunoblot data are suggestive of either downregulation or degradation of proteins following METH treatment. Graphical representation of the densitometric analysis shows decreased protein abundance (SOD1, MKK-1 and CRMP-2) (B). Saline (S) samples are represented by white bars and acute METH (M) samples are represented by gray bars. Student’s t test was performed to evaluate statistical significance (*p < 0.05; mean ( SEM; n ) 4).
SOD1, MKK-1, PEBP-1 and intact CRMP-2 proteins (p < 0.05; Student’s t test) compared to the saline samples. Psychoproteomic Interaction Map of the METH Differential Proteins. With the use of advanced protein pathwaysconstructing bioinformatics software (PathwayArchitect), dif-
ferential protein subsets were used to construct an interaction pathway map illustrating their potential role in acute METH abuse (Figure 6). Among the predicted proteins are the apoptotic peptidase activating factor-1 (APAF-1) and BCL2-associated protein as positive and negative regulators of apoptosis. Journal of Proteome Research • Vol. 7, No. 5, 2008 1979
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Figure 6. A schematic of the interaction psychoproteome map of differential proteins following acute METH treatment. Shown is a schematic of the interaction functional psychoproteome map post-acute METH treatment. Proposed functional processes of altered protein expression have been highlighted. Upregulated proteins are shown in the blue-bordered octagon boxes, while downregulated proteins are in pink-bordered octagon boxes. Predicted altered proteins are shown in light blue ellipses. Proposed functional mechanisms are depicted in light green rectangular boxes.
In addition to the 27 mass spectrometry-identified proteins depicted in the interaction map (11 downregulated and 16 upregulated), 29 additional proteins were predicted and were shown to be relevant to this pathway map. Finally, other identified cellular mechanism(s) include such as the oxidative stress, apoptosis, calcium signaling, signal transduction, protein folding, and autophagic cell death.
Discussion Widespread assessment of methamphetamine effects on the molecular and cellular levels within the CNS remains a largely uncharacterized field. In this study, global proteomic alteration following acute METH treatment (40 mg/kg) was evaluated in rat cortex. For this purpose, we utilized a novel multidimensional separation technique (CAX-PAGE/RPLC-MS/MS), comprising 9 sequential steps including tandem column chromatography analysis coupled with 1D-gel electrophoresis-LC-MS/ MS proteomic identification (Figures 1-3). Using this platform, 1980
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we are able to obtain a more definitive assessment of alterations in protein expression through better separation as compared to other conventional techniques such as 2D-gel electrophoresis.26,35 Prior to our proteomic analysis, immunoblotting of RII-spectrin proteolysis was done to evaluate METH-induced cortical neurotoxicity. Immunoblots of cortical samples revealed the accumulation of the 145 and 150 kDa RII-spectrin breakdown products (SBDPs) in the METH-treated samples indicative of apoptotic/necrotic neuronal injury as shown in Figure 4. This study is among the few papers that investigates the proteomic changes associated with acute METH abuse. In this study, we have introduced the terminology of “psychoproteomics” as an integrated proteomic approach dedicated to study protein alteration involved in psychiatric disorders and substance abuse. Results from our study reveal 82 differential proteins; 42 proteins are upregulated, while 40 are downregulated following METH treatment as shown in Table 1. As evident from the data, differentially identified proteins are
Psychoproteomics of Acute Methamphetamine Neurotoxicity associated with important biological functions such as energy metabolism (hexokinase, malate and lactate dehydrogenase), synaptic transmission (synapsin I), protein folding (HSPs), or signal transduction (MAP kinase family) as indicated in Table 1. Biological significance of some of the upregulated and downregulated proteins is discussed. Among the 40 identified upregulated proteins (Table 1) following METH treatment, we selected a number of protein candidates for validation (Figure 6). We believe that these proteins would reflect on the underlying pathways involved in METH treatment. Of interest are the METH-upregulated proteins involved in the ubiquitin proteosome (UP) pathway including UCH-L3 and UCH-L1 (Table 1 and Figure 5A). UCHL1 protein is of key importance as it is has been found to be differentially expressed in a number of METH proteomic studies.24,36 UCH-L1, a neuronal protease, plays a major role in proteosomal regulation and is involved in the process of proteolytic degradation of denatured proteins via the UP system.37–42 Moreover, a number of studies have linked METHinduced neurotoxicity with some characteristics observed in Parkinson’s disease (PD),20,36,41,43 which shows an increased accumulation of R-synuclein and ubiquitin inclusions such as UCH-L1.37,41,44 Cytoplasmic inclusions have been identified in neuronal cells following METH neurotoxicity. Furthermore, a number of studies have shown that UCH-L1 upregulation has been implicated in regulating apoptotic cell death in retinal ischemia and Alzheimer’s disease.45–47 Another identified protein with increased abundance was synapsin I (Figure 5A). Synapsin I protein is localized at the presynaptic nerve terminals and is involved in the dynamics of psychostimulant-induced dopamine release such as following administration of METH.48 A recent study by Vetron et al. on the effect of cocaine dependent dopamine release demonstrated that dopamine release from reserve pools of dopaminecontaining synaptic vesicles was mediated via a synapsindependent mechanism.49 In our acute METH regimen, which includes an abrupt increase in dopamine, we expect that protein alteration might be upregulated to promote more dopamine synaptic release. Along the same line, the microtubule-associated protein light chain 3 (LC3) was upregulated following METH treatment (Figure 5A). The LC3 protein is the mammalian homologue of autophagic ATG-8 protein. LC3 occurs in three distinct forms: an untruncated full-length pro LC3, a C-terminal truncated LC3-I and a lipidated processed form (LC3-II) which is attached to autophagosomes.50 Our data shows an increase in the LC3 (21 kDa) and LC3-II (18 kDa) which is highly indicative of autophagic stress induction upon acute METH treatment. Autophagic cell death or type II cell death is a cellular process characterized by engulfment of organelles within autophagic vesicles. In a similar study, Kanthasamy et al. showed that autophagosome induction was observed following METH treatment, occurring prior to apoptotic cell death. Thus, this may represent a protective mechanism to eliminate oxidized or damaged proteins that may be deleterious to neuronal survival.21 On the other hand, proteins with decreased abundance postMETH treatment (Table 1) are likely the result of altered cellular pathways and/or due to proteolytic proteolysis and degradation as observed in other biochemical studies.7,18,31 Included in this group are the neuronal proteins, CRMP-2, PEBP-1, MKK-1, and SOD1. All defined proteins were validated by immunoblotting technique as shown in Figure 5B. Copper zinc superoxide
research articles dismutase (SOD1) is a cytoplasmic enzyme that acts as a scavenger of superoxide radicals protecting from endogenous and exogenous sources of superoxide anions such as those arising from METH-induced oxidation.51,52 It has been shown that the acute and chronic effects of METH-induced neurotoxicity are attenuated in SOD1 transgenic mice highlighting a major role of SOD1 in this mechanism. Nevertheless, to our knowledge, the current work is the first study demonstrating that SOD1 protein is downregulated following acute METH treatment. On the other hand, similar to RII-spectrin and Tau proteins, phosphatydilethanolamine-binding protein-1 (PEBP1) has been recently identified as calpain substrate.53 PEBP-1 protein, a raf-kinase inhibitor, is a neuronal protein responsible for cell signaling. We hypothesize PEBP-1 downregulation is due to the calpain activation mediating proteolysis of this protein as shown in previous METH studies.7,18,31 Interestingly an emerging role of PEBP-1 as a potential inhibitor of the proteosome has been proposed. Thus, its downregulation may potentially lead to proteosomal activation as a feedback mechanism due to accumulation of oxidatively nonfunctional proteins.53,54 Similar to PEBP1, CRMP-2 is another neuronal axonal/dendritic guidance protein that has been shown to be a calpain substrate.55 Taken together, these data strengthen the concept that calpain activation is a common theme involved in METH-induced neuronal injury. Another protein of decreased abundance is the F-actin capping protein, a primary scaffolding proteins involved in stabilizing the three-dimensional structure of neurons.36 This is consistent with our previous data indicating that cytoskeletal proteins are susceptible for METH-mediated modifications.7,18 Finally, our data, in agreement with other findings by Sokolov et al., showed that MAP kinase-related proteins (PKC, MKK-1 and MAP kinase-1, Table 1) were decreased after METH treatment.6 METH might induce perturbation in MAP kinase pathways leading to a form of neuromodulation in the cortical and striatal circuits manifested by the observed aggressive behavior in METH-treated rats. It is of note that, in our study, METH-treated animals exhibited aggressive behavior coupled with other phenotypic aberrations including loss of teeth and significant weight loss (data not shown). Furthermore, these behavioral data parallel human METH data showing increased impulsivity, aggressiveness and hyper-intensities relevant to cerebral frontal deficits as demonstrated by imaging and behavioral studies.56–58 Coupled with the psychoproteomic differential platform, our study utilized novel bioinformatics software (PathwayArchitect) for constructing a psychoprotoeomic interaction map reflecting the altered perturbed cellular pathways following METH treatment. As shown in Figure 6, the functional interaction map highlights the potential role of oxidative stress, apoptosis, neural differentiation and the autophagic pathways as major events involved following METH-treatment. In addition, utilizing bioinformatics tools coupled with genomics and/or proteomics analysis is a powerful strategy to investigate and elucidate different pathways that may not be related to the neurotoxic event of METH but may be reflecting molecular and cellular changes coupled with the observed neurotoxic mechanisms. This approach assisted in identifying additional key proteins that are modulated or altered after METH treatment but missed by the actual proteomic data analysis as shown in the depicted altered pathways in Figure 6. Finally, the importance of these pathways provides compelling targets for future experiments such as treating rats with different drugs including Journal of Proteome Research • Vol. 7, No. 5, 2008 1981
research articles glutamate antagonists or calpain/caspase inhibitors along with METH treatment. This would be ideal in validating the exact roles of the identified proteins and whether these proteins are involved in METH-induced neurotoxicity. Finally, as there is no one technique that is exhaustive, comprehensive and capable of fully examining the full spectrum of a specific proteome,59 we have utilized our multidimensional separation platform to assess proteomic changes after acute METH treatment.26 The enhanced utility of this separation technique (better resolution and resolving power, separation capabilities and cost effectiveness) over other conventional proteomics methods has been fully discussed in our methodology paper (for detailed description of this method refer to ref 26). The utility of this method has been applied on two studies including brain injury and liver ischemia; results from these studies have yielded promising protein hits that are now tested as being potential biomarkers.25,27 In this regard, we expect that some of the identified proteins from this study would serve as potential markers indicative of METH neurotoxicity, since the field of drug abuse and particularly club drugs lacks any studies investigating biomarker identification. Abbreviations: METH, methamphetamine; CAX-PAGE, combined cationic-anionic-exchange chromatography-polyacrylamide gel electrophoresis; RPLC-MS/MS:,reversed-phase liquid chromatography-tandem mass spectrometry; CRMP-2, collapsin response mediator protein 2; BDP, breakdown product; SBDP, RII-spectrin breakdown product; UP, ubiquitin proteosome; PD, Parkinson’s disease; DA, dopamine; MS, mass spectrometry; UCH-L1, ubiquitin C terminal hydrolase-L1.
Acknowledgment. This work was supported in part by the Donald and Irene Dizney Eminent Scholar Chair, held by Mark S. Gold, M.D. Distinguished Professor, McKnight Brain Institute and also by the Department of Defense (DOD) grant no. DAMD17-03-1-0066. Special thanks go to Professor Sue Semple-Rowland, Department of Neuroscience at the University of Florida, for revising and editing the manuscript. Dr. K. K. W. Wang holds equity in Banyan Biomarkers, Inc., a company commercializing biomarker technology in brain injury. Supporting Information Available: Figure 1, differential CAX-PAGE gel band analysis. This material is available free of charge via the Internet at http://pubs.acs.org.
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