• zebrafish;
  • brain;
  • proteomics;
  • neurodegeneration


  1. Top of page
  2. Abstract
  7. Supporting Information

Understanding the complex biology of the brain requires analyzing its structural and functional complexity at the protein level. The large-scale analysis of the brain proteome, coupled with characterization of central brain proteins, provides insight into fundamental brain processes and processes linked to neurodegenerative diseases. Here we provide a map of the zebrafish brain proteome by using two-dimensional gel electrophoresis (2DE), followed by the identification of 95 brain proteins using mass spectrometry (LC-ESI MS/MS). Our data show extensive phosphorylation of brain proteins but less prominent glycosylation. Furthermore, ∼51% of the identified proteins are predicted to have one or more ubiquitination sites whereas ∼90% are predicted to have one or more SUMOylation sites. Our findings provide a valuable proteome map of the zebrafish brain and associated posttranslational modifications demonstrating that zebrafish proteomic approaches can aid in our understanding of proteins central to important neuronal processes and those associated with neurodegenerative disorders. © 2013 Wiley Periodicals, Inc.

The large-scale analysis and functional characterization of the protein complement expressed in the brain and nervous system holds the potential for elucidating mechanisms by which functional protein networks control and regulate both the structural organization and the functional complexity of the brain (Kim et al., 2004; Becker et al., 2006; Bayes and Grant, 2009). Approximately 50% of the human genome is thought to be expressed in the central nervous system (CNS), and combined with posttranslational modifications (PTMs) and protein–protein interaction networks the number of protein variants in the brain is immense. Because of this, our understanding of how perturbations in brain functions result in neurodegenerative disorders is still limited.

Neurodegenerative diseases are characterized by loss of neuronal structure and function, followed by cell death. Although the exact mechanisms involved in neuronal death are unclear, protein aggregation and reactive oxygen species (ROS) are contributing factors in several disorders such as amyotropic lateral sclerosis (ALS), Parkinson's disease (PD), and Alzheimer's disease (AD) as well as in prion diseases (Grimm et al., 2011; Federico et al., 2012). Proteomic strategies to understand neurodegeneration have gained momentum (Robinson, 2010), and proteomic technologies are being combined with various model organisms (Blesa et al., 2012). One of the aims of these proteomics strategies is the development of early-stage disease biomarkers. Early studies used proteomics to identify oxidatively modified proteins in AD brains, in which creatine kinase BB, glutamine synthase, and ubiquitin carboxy-terminal hydrolase L-1 were revealed as specific targets of protein oxidation in AD (Castegna et al., 2002). More recent studies have used proteomics to identify potential biomarkers involved in several neurodegenerative diseases, including ALS (Ranganathan et al., 2005; Ryberg et al., 2010; Zhou et al., 2010), PD (Chen et al., 2011; Lehnert et al., 2012), and AD (Zabel et al., 2012). Indeed, proteomic approaches have revealed nine proteins, including mitochondrial and ROS scavenging proteins, showing expression level difference in the substantia nigra of PD patients compared with control subjects (Basso et al., 2004).

Although proteomics in relation to disease is often geared toward biomarker discovery, proteomics approaches also offer insight into fundamental biological processes related to disease by identifying protein complements in target tissues. The identification of proteins involved in oxidative stress responses in the brain and in associated neuronal structures will undoubtedly provide insight into ROS-induced neurodegeneration, because superoxide anions (O2·) and ROS play important roles in the pathogenesis of neurodegenerative diseases (Rothstein, 2009; Xu et al., 2010; Fukai and Ushio-Fukai, 2011; Murakami et al., 2011; Wang et al., 2011).

The zebrafish (Danio rerio) disease model offers numerous advantages. Zebrafish are more closely related to humans than are invertebrates. They develop rapidly and are easy to manipulate genetically. The present study uses a proteomics approach to generate a map of the zebrafish brain proteome and associated PTMs, underscoring the value of proteomics for dissecting central proteins associated with neurodegenerative diseases.


  1. Top of page
  2. Abstract
  7. Supporting Information

Zebrafish Maintenance and Breeding

Zebrafish were housed in tanks in a six-shelf stand-alone system (Aquatic Habitats) with recirculating water at 28°C on a regular light–dark cycle (Westerfield, 2000).

Extraction of Intact Zebrafish Brains

Three-month-old fish were sacrificed by transferring them to ice water for 2 min. Fish heads were cut behind the gills and blood removed by immersing the heads in ice water. Eyeballs and optic nerves were removed from the head, the skull was cut open along the dorsal line, and the brain was extracted and transferred immediately to the protein extraction/solubilization buffer.

Extraction and Solubilization of Zebrafish Brain Proteins

Whole brains from 3-month-old male and female zebrafish were homogenized in 1 ml extraction buffer (7 M urea, 2 M thiourea, 25% CHAPS, 25 mM dithiothreitol [DTT], protease inhibitors) by pipetting through a narrow pipette tip for 2–3 min. Five microliters of tributylphosphine (TBP) was added to the homogenate, and the tube was vortexed for 30 min and incubated at room temperature (RT) for 30 min. The homogenate was then centrifuged at 13,000g for 20 min at RT. The Bradford method was used to determine the total protein concentration before adding bromophenol blue (BPB). Twelve microliters of bioLyte ampholyte buffer was added (4 μl of pH 3–10, 3.9–5.1, 6.3–8.3) to enhance isoelectric focusing (IEF) resolution before immobilized pH gradient (IPG) strip loading.

2DE of the Zebrafish Brain Proteins

Supernatant (equivalent to 1 mg protein) was used to passively load 17-cm IPG strips (Bio-Rad, Hercules, CA) for 14 hr at RT. IPG strips were isoelectrically focused using a Protean IEF Cell (Bio-Rad) at 21°C, ramping the voltage rapidly from 0–10 000 V for a total of 35,000 Vhr for pH 3–6 IPG strips, 45,000 Vhr for 7–10 IPG strips, and 55,000 Vhr for both 4–7 and 5–8 IPG strips. IPG strips were equilibrated in buffer I (6 M urea, 2% SDS, 50% glycerol, 2% DTT in resolving gel buffer) and then in buffer II (equilibration buffer I with 2.5% iodoacetamide [IAA]) for 10 min, briefly washed in electrophoresis buffer, and placed atop an 11% vertical SDS gel (20 cm × 20 cm). Second-dimension SDS-PAGE was performed with a Protean II Xi electrophoresis cell (Bio-Rad) at 200 V until the BPB dye reached the end of the gel. Gels were washed in 50% methanol/10% acetic acid twice. All 2DE experiments were performed in triplicate (biological).

Visualization, Spot Excision, and Digestion of Gel-Separated Proteins

Gels were stained with a colloidal Coomassie stain (Candiano et al., 2004). Phosphorylated and glycosylated proteins were stained with Pro-Q diamond and Pro-Q emerald 488, respectively (Molecular Probes, Invitrogen, Carlsbad, CA), following the manufacturer's protocols. Staining was performed as follows: Pro-Q diamond, Pro-Q emerald 488, and Coomassie. Pro-Q diamond- and Pro-Q emerald-stained gels were imaged on a Typhoon Trio (GE Healthcare, Little, Chalfont, United Kingdom), and Coomassie-stained gels were imaged on a standard scanner (Epson America). OMX tubes were used to excise and trypsin digest Coomassie-stained protein spots, following the manufacturer's protocol (OMX GmbH).

LC Fractionation and MS Analyses of Peptides

LC-ESI MS/MS analyses were performed using a Dionex Ultimate 3000 HPLC with a 300 μm ID × 0.5 cm Acclaim PepMap100 C18 trap column (Dionex) and a 75 μm ID × 15 cm Acclaim PepMap 100 C18 analytical column (Dionex), coupled online to an LTQ-Orbitrap (Thermo Scientific, Fair Lawn, NJ). Tryptic digests (5 μl) were loaded onto the trap column using 0.1% formic acid (VWR) in water (MilliQ; Elga) at a flow rate of 2 μl/min. The mobile phases for the analytical separation consisted of 1) 0.1% formic acid in 2.5% acetonitrile in water and 2) 0.1% formic acid in 80% acetonitrile in water. The peptides were separated on the analytical column using a gradient of solution B from 0% to 60% over 60 min after a 10-min delay postinjection. A total run time of 120 min was used, including a washing step and 20 min re-equilibration of the columns. A PicoTip emitter (SilicaTip; New Objective) with a 10-μm tip and without coating was used as an ESI interface. The electrospray voltage was set to 1 kV, and no sheath gas was used. The mass spectrometer was used in positive mode. Full scans were performed in the m/z range from 200 to 2,000 daltons, and data-dependent MS/MS scans were performed in the linear ion-trap for the five most abundant masses with z ≥ 2 and intensity ≥ 10,000 counts. Dynamic exclusion was used with 3-min exclusion after fragmentation of a given m/z value four times. Collision-induced dissociation (CID) was used with collision energy of 35% and with activation Q setting of 0.400 and activation time 30 msec for MS2. The mass spectrometer was tuned and calibrated using the calibration solution recommended by Thermo Scientific.

Data Analysis, Protein Identification, and Gene Ontology (GO) Annotation

Raw files were analyzed using Proteome Discoverer 1.0 (Thermo Scientific): Rawfile selector, Spectrum selector, and Sequest search. Files were searched against the NCBI's Danio rerio ( 7955) protein database with trypsin as the digestion enzyme, allowing for two missed cleavages. Precursor ion and fragment ion mass tolerance were set to 50 ppm and 0.8 Da, respectively. Because of the use of DTT and IAA, oxidation (methionine) was set as variable modification, and carbamidomethyl (cysteine) was set as fixed modification.

Proteins identified were mapped to their corresponding UniProt accession numbers using the UniProt web-based portal ( Proteins were analyzed for their cellular localizations, biological processes, and molecular functions using STRAP (Bhatia et al., 2009). Expression, functional, and pathways analysis used the DAVID web-based interface (Huang et al., 2009). For protein–protein interaction maps, STRING software was used. Ubiquitination site prediction analysis used the UbPred predictor software (Radivojac et al., 2010). For SUMOylation site prediction analysis, the SUMOplot analysis program was used (


  1. Top of page
  2. Abstract
  7. Supporting Information

Two-Dimensional Protein Map of the Zebrafish Brain Proteome

The 2D IEF/SDS-PAGE separation of the zebrafish brain proteome was performed with two 17-cm IPG strips with overlapping pH ranges (4–7 and 5–8), which were merged into one image spanning pH 4–8 (Fig. 1A). Although a pH range of 3–10 provides greater overall resolution, our approach increased the resolution within the pH range 4–8, where the majority of proteins are present. The distribution of proteins spanned the entire pH range with molecular weights ranging from <100 kDa to >15 kDa. The 2D map was divided into three main areas: region A (acidic proteins with pI values >5), region B (basic proteins of pI values of ≥8) and the region in between region A and B (Fig. 1A).


Figure 1. 2D proteome map of the zebrafish brain from 3-month-old fish. A: Negative-colored image of the colloidal Coomassie-stained 2D gel covering the pI range of 4–8. Acidic proteins (with pI ≤ 5) are included in region A, and basic proteins (pI ≥ 8) are included in region B. Protein spots identified are labeled with numbers. B: Negative-colored image of the 2D map manipulated to highlight highly expressed proteins identified (circles).

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Multiple protein species were found in all spots excised, with a total of 1,500 proteins identified based on detection of at least one peptide of at least 8 amino acids (Supp. Info. Fig. 1). The number of peptides identified for each protein within a spot varied, and proteins represented by the highest number of peptides were chosen as the principal protein in the spot and used for labeling of the 95 different proteins (Table 1). When more than one protein in a spot had equally high peptide numbers, the spot was labeled as containing all these proteins.

MS analyses of acidic proteins from region A showed that these include structurally and functionally important proteins in the brain such as neurofilaments, 14-3-3 proteins, ependymin, β-synuclein, and β polypeptide of mitochondrial ATP synthase, in addition to a number of calcium binding proteins, including calmodulin, parvalbumin, hippocalcin, S100 protein, and calreticulin. Interestingly, different isoforms were identified for neurofilaments, ependymin, and 14-3-3 proteins. Five 14-3-3 isoforms were identified in our study (Table 1), which highlights that 14-3-3 proteins are highly expressed in the brain and that through multiple interactions 14-3-3 proteins impact many brain functions, including neural signaling, neuronal development, and neuroprotection. Furthermore, 14-3-3 proteins have been implicated in a variety of neurological disorders (Foote and Zhou, 2012).

Table 1. List of Identified Proteins From the Zebrafish Brain Proteome
Spot No.IDDescriptionNo. peptidesCoverage (%)
A1gi42542468SET translocation (myeloid leukemia-associated) B1213,06
A2gi169153857SET translocation (myeloid leukemia-associated) A914,50
A4gi41055752Synuclein, beta4255,91
A5gi94733993Novel protein similar to vertebrate S100 calcium binding protein family520,00
A7gi182891312Cox5aa protein2018,44
A8gi68359747Hippocalcin-like protein1735,08
A9gi18288946014-3-3 Protein epsilon 113255,29
A10gi5620792614-3-3 Protein theta1217,96
A11gi8259259814-3-3 Protein beta4138,11
A12gi8259259814-3-3 Protein alpha-112546,72
A13gi12583733614-3-3 Protein gamma polypeptide 24436,03
A15gi147905995Mitochondrial ATP synthase beta subunit-like26268,86
A16gi189519129Similar to myosin heavy chain 412017,85
A17gi169153866Novel protein similar to proline 4-hydroxylase beta2817,68
A19gi162287417Neurofilament, medium polypeptide 150 kDa isoform 1123,65
A20gi169146671Novel protein similar to H. sapiens neurofilament, light polypeptide 68 kDa84,32
A21gi190338332Epsin 1188,53
A22gi189533895Similar to neurofilament, light polypeptide169,46
A23gi162287417Neurofilament, medium polypeptide 150 kDa isoform 12211,43
A24gi189526843Similar to NF-M236,83
A25gi189526843Similar to NF-M236,83
1gi189530199Similar to clathrin assembly protein AP18072,49
2gi259421698Adaptor-related protein complex 2, beta 1 subunit123,26
3gi121582316Adducin 1, alpha179,72
4gi56207786Novel protein similar to heat shock protein 90-alpha (hsp90a)10440
5gi123209232Novel protein with an intermediate filament protein domain2716,55
7gi94732278Heat shock 70 kDa protein 88632,67
8gi122890758ATPase, H+ transporting, lysosomal V1 subunit A-like16740,84
9gi52353322Tubulin, alpha 8 like7516,70
10gi35902919Tubulin, alpha 134943,43
11gi66472732Myosin heavy chain 47614,52
12gi124504410Gfap protein7432,73
13gi92087016Actin, cytoplasmic 2 (beta)38431,73
14gi148725250Glutamine synthase, alpha6951,48
15gi39795612Creatine kinase, brain type23653,81
16gi51315868Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; transducin beta chain 12622,65
17gi220941688Novel myosin family protein10020,23
18gi94733848Pyruvate dehydrogenase (lipoamide) beta7840,11
20gi41393111Ubiquitin carboxyl-terminal esterase L1 (UCHL1)3630,28
21gi25989645Brain-type fatty acid binding protein14057,58
22gi28278431Profilin 2514,39
23gi25989645Brain-type fatty acid binding protein14057,58
24gi66773128Peroxiredoxin 51314,20
25gi94734326Ubiquitin-conjugating enzyme E2N2648,70
26gi54312133Phosphatidylethanolamine binding protein1524,06
27gi62955673Stathmin 1b2732,21
28gi197247128Glutathione peroxidase 4b protein1936,61
29gi41152183Protein (peptidyl-prolyl cis/trans isomerase) NIMA-interacting 11722,64
30gi20139980Superoxide dismutase [Cu-Zn]; Sod12652,60
31gi41053595Nucleoside diphosphate kinase B1837,91
32gi47271384Cofilin 2, like4757,58
33gi56207502Novel protein similar to vertebrate ATPase, H+ transporting, lysosomal, V1 subunit F (ATP6V1F)1426,89
34gi50539996Peroxiredoxin 24731,47
35gi189529246Apolipoprotein A-I4641,18
36gi94734541Novel protein similar to glutathione S-transferase family3321,24
37gi47271422Triosephosphate isomerase 113066,13
38gi169642540Vdac1 protein4742,40
39gi220679252Glutathione S-transferase M5047,49
40gi46362488Ldhb protein14729,94
41gi82213363Fructose-bisphosphate aldolase C; brain-type aldolase14555,10
42gi189525553Phosphoglycerate kinase26268,86
43gi48762657Enolase 1 (alpha)13346,06
44gi46358344Isocitrate dehydrogenase 3 (NAD+) alpha126,30
45gi82213363Fructose-bisphosphate aldolase C; brain-type aldolase4134,99
46gi182889140Pyruvate kinase 2a (Pkm2a) protein4219,55
47gi82209620Aldehyde dehydrogenase family 9 member A1-A2119,69
48gi82175661Dihydropyrimidinase-related protein 3 (collapsin response mediator protein 4)4431,39
49gi113195584Vesicle-fusing ATPase8027,28
50gi82175661Dihydropyrimidinase-related protein 3 (collapsin response mediator protein 4)  
51gi56090148Stress-induced-phosphoprotein 1 (Hsp70/Hsp90-organizing protein)5830,44
53gi31044489Heat shock 60 kD protein 18730,09
54gi82188427Transitional endoplasmic reticulum ATPase (valosin-containing protein)3011,66
55gi220678446Novel protein similar to vertebrate phosphoglycerate mutase family member 4515,35
B1gi253735626Synapsin II3518,99
B2gi148224245Mitochondrial trifunctional protein, alpha subunit3125
gi187607906Synapsin I2514,29
gi47086479Solute carrier family 25, member 122112,46
B3gi116325975ATP synthase, H+ transporting, mitochondrial F1 complex, alpha subunit 111243,56
B4gi41054651Isocitrate dehydrogenase 2 (NADP+), itochondrial9443,43
B5gi259665686Aspartate aminotransferase 2a5643,10
gi51230650Acetyl-coenzyme A acetyltransferase 1 precursor4839,49
B6gi41282154Fructose-bisphosphate aldolase A6231,32
B7gi90112045Short chain dehydrogenase/reductase7535,00
B8gi47085883Mitochondrial malate dehydrogenase10846,59
B10gi45709041Atp6v1e1 protein2538,50
B11gi11494375Glutathione S-transferase π5350,48
B12gi41152400Peptidylprolyl isomerase A, like8828,66
B13gi52782967Hemoglobin subunit beta-23868,92

The basic region of the 2D gel appeared dense because of the narrowing of the pH range (Fig. 1A, region B). However, several protein spots were identified as mitochondrial proteins involved in different metabolic pathways, metabolite transfer across the mitochondrial outer membrane, oxidative phosphorylation, and ATP synthesis and hydrolysis, including isocitrate dehydrogenase, fructose bisphosphate aldolase, malate dehydrogenase, aspartate aminotransferase 2, short-chain dehydrogenase/reductase, VDAC isoforms, the alpha subunit of ATP synthase F1 complex, and the e-subunit of of ATP6v1e1 (Table 1, region B). Gluthione S-transferase π and prolyl isomerise A, involved in metabolism of xenobiotics and protein folding, were also identified (Table 1, B11, B12).

The characterization of proteomes by 2DE has advantages and disadvantages. Disadvantages include detection limitations and resolution of highly complex proteomes. However, 2DE approaches also have advantages, including direct proteome visualization, the ability to visualize PTMs directly by using staining techniques, and high reproducibility between individual datasets. Indeed, by comparing our data set with a previously published data set of the zebrafish brain proteome using the same approach (Singh et al., 2010), we find that 38 proteins identified in both studies are identical. However, the two studies are complementary inasmuch as 57 proteins identified in our studies were not identified in the study by Singh et al. (2010) and therefore represent newly identified zebrafish brain proteins (Table 1). Combined, both studies have identified 217 individual proteins from the zebrafish brain proteome.

Abundant Proteins in the Zebrafish Brain

The 2D map revealed a number of prominent protein spots, which represent the most abundant proteins in the zebrafish brain (Fig. 1B). Mass spectrometric analysis revealed that these spots include membrane, cytoplasmic, cytoskeletal, and mitochondrial proteins involved in cellular processes such as molecular trafficking, signalling, glycolysis, ATP production, protein folding, and cytoskeleton dynamics (Table 2). Cytoskeletal alpha-tubulins appeared as the most prominent proteins (spots 9 and 10), followed by beta-actin (spot 13), creatine kinase (spot 15), ependymin (spot 19), 14-3-3 isoforms (spots A9), and fatty acid binding protein (spot 21; Fig. 1B, Table 2). Proteins showing high abundance indicate that they may be critical for functional and/or structural integrity and that they are likely expressed in multiple cell types, as is the case for 14-3-3 proteins (Foote and Zhou, 2012). Large numbers of the most abundant proteins are involved in cell morphology maintenance, cell-to-cell communication, and energy homeostasis in addition to being involved in signalling pathways such as 14-3-3-, apoptotic, and ERK5 signalling (Fig. 3D,E, Table 2).

Table 2. Most Abundant Proteins Identified in the Zebrafish Brain Proteome
Spot No.DescriptionBiological processSubcellular localization
A3CalmodulinGPCR protein signalingCytosol, cytoskeleton
A4Synuclein-betaAggregation regulationCytosol
A5S100 proteinAxonogenesisCytosol, nucleus
A914-3-3 proteinsDiverse cellular processesCytosol, nucleus, membrane
A15ATP synthase-betaATP synthesisMitochondrial inner membrane
7HSP 70, protein 8Stress responseCytosol
8H-transporting ATPase, lysosomal V1AATP synthesis, proton transportTransmembrane
9Tubulin, alphaMicrotubule-based movementCytosol, microtubules
10Tubulin, alphaCytoskeleton, microtubule-based movementCytosol, microtubules
13Actin, betaCytoskeleton, cell components movementCytosol, cytoskeleton
14Glutamin synthaseGlutamine synthesisCytosol
15Creatine kinaseCreatine metabolismCytosol
16Guanine-binding protein, polypeptideSignal transductionCytosol, membrane
19EpendyminCell matrix adhesionSecreted, ECM
21Fatty acid binding proteinLipid transportCytoplasm
32CofilinActin dynamicsCytosol, cytoskeleton nuclear
35Apolipoprotein A1Lipid metabolismSecreted
38VDAC1Anion transportMembrane integral
40Lactate dehydrogenase betaGlycolysisCytosol
41Aldolase CGlycolysisCytosol
43Enolase CGlycolysisCytosol, mitochondria, membrane
45Isocitrate dehydrogenase 3 (NAD), alphaOxidation/reduction, TCA cycleMitochondria
46Pyruvate kinase 2a (Pkm2a) proteinGlycolysisMitochondria
47Aldehyde dehydrogenase family 9 member A1-AOxidation/reductionCytosol
52Transferrin-alphaIron homeostasisExtracellular
B3ATP synthase, H+ transporting, mitochondrial F1 complex, alpha subunit 1ATP synthesis, proton transportMitochondria
B4Isocitrate dehydrogenase 2 (NADP+), mitochondrialTCA cycle, stress responseMitochondria
B5Aspartate aminotransferase 2a,Amino acid metabolismCytosol
 Acetyl-Coenzyme A acetyltransferase 1 precursorAcetyl-CoA conversionMitochondria
B6Fructose-bisphosphate aldolase AGlycolysisMitochondria, cytosol
B7Short chain dehydrogenase/reductaseOxidation/reductionMembrane-integral
B8Mitochondrial malate dehydrogenaseGlycolysisMitochondria
B9VDAC2, VDAC3Anion transportMitochondrial outer membrane
B10Atp6v1e1 proteinATP synthesis, H-transportMitochondria
B11Glutathione S-transferase πMetabolic processesCytosol
B12Peptidylprolyl isomerase A, likeProtein foldingCytosol
B13Hemoglobin subunit beta-2Oxygen transportHemoglobin complex

Posttranslational Modifications of Brain Proteins

To test directly for PTMs within the zebrafish brain proteome, we stained the 2D gels with highly sensitive fluorescent dyes specific for phosphorylated and glycosylated proteins (Pro-Q diamond and Pro-Q emerald, respectively). Pro-Q diamond revealed extensive phosphorylation, particularly of proteins above 25 kDa (Fig. 2A). Identification of some of these proteins showed that they include cytoskeletal proteins such as neurofilaments (NF-M, NF-L) and tubulins (α-tubulin), cytoplasmic actin and α-adducin, isoforms of 14-3-3 proteins, collapsing response protein 4, as well as AP180 and epsin involved in membrane curvature and synaptic vesicle formation (Stachowiak et al., 2012). A number of enzymes also showed phosphorylation, including creatine kinase, enolase1, pyruvate dehydrogenase, and the neurologically important enzyme glutamine synthase (Supp. Info. Fig. 2).


Figure 2. Detection of posttranslational modifications in 3-month-old zebrafish brains. A: 2D gel of the zebrafish brain proteome stained with the phosphoprotein-specific dye Pro-Q diamond. Proteins detected are shown in green, and proteins identified are labeled with numbers. B: 2D gel of the zebrafish brain proteome stained with the glycosylation-specific dye Pro-Q emerald. Proteins detected are circled. [Color figure can be viewed in the online issue, which is available at]

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Phosphorylation events in the brain are extensive. It has been shown that more than 6,000 nonredundant phosphorylation sites are present in neonatal murine brain tissue and that this number increases threefold after 3 weeks of development (Goswami et al., 2012). The extensive phosphorylation pattern observed in this study is in agreement with these findings (Fig. 2A). Further analysis of the identified phosphorylated proteins in the zebrafish brain revealed that they are involved in diverse signalling pathways contributing to important cellular processes such as the cell cycle and cell death, cell metabolism, and cell-to-cell communication (Supp. Info. Fig. 4). For example, phosphorylation of neurofilament isoforms has been shown to be required for their axonal transport through interaction with each other and with other cytoskeletal proteins to maintain neuronal cell morphology (Grant and Pant, 2000). Furthermore, 14-3-3 protein phosphorylation is important for protein–protein interactions (Berg et al., 2003) and for mediating tau protein phosphorylation by GKS-3 kinase (Yuan et al., 2004). Adducin 1 is known to bind F-actin, leading to association with spectrin beneath the plasma membrane, forming a spectrin-F-actin mesh (Matsuoka et al., 2000). It has been shown that rho-associated kinase phosphorylates adducin, leading to activation of its binding capacity to F-actin and subsequently forming such a cytoskeletal network (Fukata et al., 1999). Collapsing response mediator proteins (CRMPs) and stress-induced phosphoprotein 1 were also identified as phosphorylated proteins (Supp. Info. Fig. 2). CRMPs are known effector phosphoproteins in semaphorin 3A signalling regulating growth cone collapse, so their phosphorylation status is critical (Arimura et al., 2000). Similarly, stress-induced phosphoprotein 1 phosphorylation may be critical for its role as a cochaperone mediating interactions of other chaperone proteins (Hernández et al., 2002).

Compared with our phosphoprotein experiments, Pro-Q emerald staining detected only 14 glycosylated proteins (Fig. 2B). These include the cytoskeletal proteins α-tubulin and actin, isoforms of 14-3-3, ependymin, Hsc71 chaperone, guanine-binding protein, and apolipoprotein A1 in addition to fundamental enzymes such as glutamine synthase, aldolase c and enolase c, and creatine kinase (Supp. Info. Fig. 3). The less prominent glycoprotein staining in the zebrafish brain is not entirely surprising, because glycoprotemics approaches in human brain tissue have identified only 394 nonredundant glycoproteins (Hwang et al., 2010). This could be due in part to fewer glycosylation events in the brain compared with phosphorylation events. However, although this may be the case, glycosylation status is associated with numerous central cellular processes as well as with human disease, including neurodegenerative disorders (Hwang et al., 2010). For example, glycosylation of α-tubulin, actin, 14-3-3 proteins, and ependymin in the zebrafish brain underscores the importance of this PTM in central brain processes. Indeed, tubulins are glycosylated by sialyl-oligosacharides (Hino et al., 2003). The characterization of glycoproteins on a large scale is challenging, because of their inherent complexity and heterogeneity, and it is possible that the Pro-Q emerald staining approach underestimates the level of glycosylation in a given tissue.

Ubiquitination and SUMOylation events are important for neuronal development and function. It has been demonstrated that ubiquitination events take place at all stages of neuronal development and that many neurological diseases are associated with altered ubiquitination activities (Lehman, 2009; Kawabe and Brose, 2011). To this end, we screened for ubiquitination and SUMOylation sites in all of the 95 proteins identified. For ubiquitination site prediction analysis, we used the UbPred predictor software, a random forest-based predictor of potential ubiquitination sites in proteins (Radivojac et al., 2010). For SUMOylation site prediction analysis, we used SUMOplot ( With a high-stringency cutoff, we found that 51% of the identified zebrafish brain proteins had one or more high-probability ubiquitination sites (Supp. Info. Fig. 5). Similarly, we found that 90% of all proteins identified had one or more high-probability SUMOylation sites (Supp. Info. Fig. 5). These data suggest that ubiquitination and ubiquitination-like modifications to brain proteins are highly prominent. Combined with the fact that ubiquitination often operates in parallel with other regulatory processes, such as phosphorylation, the understanding of brain PTM networks is of great importance.

Gene Ontology Annotation and Protein Classification

Eighty proteins of the 95 proteins identified were mapped using STRAP software for gene ontology (GO) annotation (Fig. 3). In terms of subcellular localization, the proteins could be assigned to three major subcellular localizations: cytoplasm (20%), cytoskeleton (18%) and mitochondria (15%; Fig. 3A). Relatively lower numbers of proteins were assigned to macromolecular complexes (13%), the nucleus (7%), endoplasmic reticulum (4%), organelles (4%), the cell surface (2%), and the extracellular space (4%; Fig. 3A). The GO analysis further showed that most of the proteins identified are involved in cellular (47%) and metabolic (15%) processes, regulation (10%), response to stimuli (9%), and developmental processes (7%; Fig. 3B). Furthermore, the identified proteins act mainly through binding (40%) and catalytic activity (40%; Fig. 3C).


Figure 3. GO annotation of all proteins identified on the 2D map of the zebrafish brain proteome. A: Cellular localization distribution of the proteins identified. B: Biological processes in which they are involved. C: Different molecular functions assigned to the different proteins. Cellular processes and pathway analysis of the most abundant proteins identified in the zebrafish brain proteome showing predominant cellular processes in which the most abundant proteins are involved (D) and predominant pathways in which the most abundant proteins identified in the zebrafish brain are involved (E).

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Interestingly, 15% of the identified proteins were predicted to localize to mitochondria compared with a previous report showing only 5% mitochondrial proteins (Singh et al., 2010). Mitochondria are central organelles, particularly in relation to neuron maintenance and neurodegeneration (Nagley et al., 2010; Faes and Callewaert, 2011; de Vries and Przedborski, 2012). The identification of a significant number of proteins localized to mitochondria is therefore not entirely surprising. Similarly, our study shows that 40% of the identified proteins have binding activity, which is in contrast to previous reports showing only 4% involved in binding (Singh et al., 2010). This observed variability demonstrates that isolated proteomics approaches are not necessarily sufficient to dissect parameters important for our understanding of brain complexity, which is further underscored by the fact that Singh et al. (2010) and our study identified 38 common proteins from a combined pool of 255 proteins.

Further analysis, in Ingenuity software, revealed that the most abundant proteins are needed for cellular function and maintenance, cell-to-cell signalling and interaction, posttranslational modifications, amino acid metabolism and small-molecule biochemistry, cell cycle and development, and cellular assembly and organization (Fig. 3D). Furthermore, these processes act mainly through metabolic and signalling pathways including glucose/pyruvate metabolism, fatty acid and amino acid metabolism, glutathione metabolism, glutamate metabolism, oxidative phosphorylation, 14-3-3-mediated signalling, apoptosis, glutamate receptor signalling, ERK5 signalling, and IGF-I signalling among other pathways (Fig. 3E).

Similar analysis was also performed on the phosphorylated proteins, revealing that the phosphorylated proteins were involved mainly in processes such as cell death; cell cycle regulation and cell development; cellular assembly, organization, and morphology; posttranslational modification; cell-to-cell signalling and interaction, and amino acid and protein metabolism (Supp. Info. Fig. 4A). Furthermore, the phosphorylated proteins were identified as being involved in a number of important signalling pathways, including 14-3-3-mediated signalling, PI3K/AKT signalling, Myc-mediated apoptosis signalling, ERK5 signalling, IGF-I signalling, protein kinase A signalling, clathrin-mediated endocytosis signalling, and ERK/MAPK signalling pathways (Supp. Info. Fig. 4B).

To gain insight into the relationship between the zebrafish brain proteins, an interaction map was generated showing established and predicted functional interactions of all proteins identified (Supp. Info. Fig. 6). This interaction map reveals a high degree of direct and indirect interactions, emphasizing the complexity of the brain proteome.

Brain-Specific Proteins and Proteins Involved in Neurodegeneration

Analysis of the proteins identified in the DAVID software showed that they include a number of neuronally specific proteins, including S100 calcium binding protein, peroxiredoxin 2, parvalbumin 7, stathmin 1b, and H+ transporting ATPase V1 subunit F (Table 1). Importantly, 15 of the proteins identified are associated with different neurodegenerative disorders. SET protein, GFAP, and 14-3-3 proteins are associated with AD, whereas NEFL, SOD1, and GFAP are linked to ALS. Furthermore, SOD1, SNCB, STMN1, SYN1, UCHL1, VIM, GSTP1, LDHB, ATP6V1E1, and PEPB1 are all associated with PD.

One of the first functions to be ascribed to 14-3-3 proteins was the regulation of tyrosine hydroxylase activity and its involvement in PD. It has now been demonstrated that 14-3-3 proteins are also involved in AD and ALS, showing the central role of 14-3-3 proteins in multiple neurodegenerative disorders (Steinacker et al., 2011). Similarly, SOD1 has been directly associated with ALS, AD, and PD (Mhatre et al., 2004; Martin, 2007), in which it plays a central role through its abilities to scavenge oxygen radicals and to protect neuronal cells from oxidative insults (Mhatre et al., 2004; Martin, 2007). It has been suggested that SOD1 may form toxic aggregates trapping proteins important for neuronal survival/transport (Takei et al., 2013; Tateno et al., 2009; Ström et al., 2008), that SOD1 may block UPS and lysosomal protein degradation pathways (Cheroni et al., 2005), and that SOD1 may cause neurite mitochondrial fragmentation affecting neurite processes (Magrané et al., 2009). In ALS, NEFL (neurofilament light chain) has been used as a biomarker when CSF concentrations are increased in ALS subjects (Reijn et al., 2009). In AD, different splice isoforms of GFAP, the main astrocytic intermediate filament, are differentially expressed in the human brain. Moreover, with AD mouse models, it has been shown that astrocytes have an increase in isoforms GFAPα and GFAPδ (Kamphuis et al., 2012). Indeed, in post-mortem brains of AD patients, acidic GFAP isoforms show a 60% increase in levels compared with age-matched controls (Korolainen et al., 2005).

SNCB (beta-synuclein) is associated with PD, as are alpha- and gamma-synucleins; they are involved in Lewy body formation (Nishioka et al., 2010). Ubiquitin carboxyl-terminal hydrolase L1 (UCHL1) is also associated with PD, in which alpha-synuclein and UCHL1 not only show interactions but UCHL1 is thought to be involved in the clearance of accumulated alpha-synuclein (Cartier et al., 2012). Through several case studies, as early as 1998, glutathione S-transferase pi (GSTP1) has also been associated with PD (Menegone et al., 1998). It has been shown that dopaminergic neurons in GSTP1 knockout mice have an increased vulnerability to 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and that GSTP1 levels may be important in modulating the progression of PD (Smeyne et al., 2007; Shi et al., 2009). We believe that one powerful approach toward identifying and dissecting neurodegenerative disease-associated proteins is to combine proteomics with downstream protein characterization in suitable models such as the zebrafish.


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  7. Supporting Information

We thank Katherine Moller for critical proofreading. The authors declare that they have no competing interests.


  1. Top of page
  2. Abstract
  7. Supporting Information
  • Arimura N, Inagaki N, Chihara K, Ménager C, Nakamura N, Amano M, Iwamatsu A, Goshima Y, Kaibuchi K. 2000. Phosphorylation of collapsin response mediator protein-2 by rho-kinase. Evidence for two separate signaling pathways for growth cone collapse. J Biol Chem 275:2397323980.
  • Basso M, Giraudo S, Corpillo D, Bergamasco B, Lopiano L, Fasano M. 2004. Proteome analysis of human substantia nigra in Parkinson's disease. Proteomics 4:39433952.
  • Bayes A, Grant SG. 2009. Neuroproteomics: understanding the molecular organization and complexity of the brain. Nat Rev Neurosci 10:635646.
  • Becker M, Schindler J, Nothwang HG. 2006. Neuroproteomics—the tasks lying ahead. Electrophoresis 27:28192829.
  • Berg D, Holzmann C, Riess O. 2003. 14-3-3 Proteins in the nervous system. Nat Rev Neurosci 4:752762.
  • Bhatia VN, Perlman DH, Costello CE, McComb ME. 2009. Software tool for researching annotations of proteins: open-source protein annotation software with data visualization. Anal Chem 81:98199823.
  • Blesa J, Phani S, Jackson-Lewis V, Przedborski S. 2012. Classic and new animal models of Parkinson's disease. J Biomed Biotechnol 2012:845618. doi: 10.1155/2012/845618.
  • Candiano G, Bruschi M, Musante L, Santucci L, Ghiggeri GM, Carnemolla B, Orecchia P, Zardi L, Righetti PG. 2004. Blue silver: a very sensitive colloidal Coomassie G-250 staining for proteome analysis. Electrophoresis 25:13271333.
  • Cartier AE, Ubhi K, Spencer B, Vazquez-Roque RA, Kosberg KA, Fourgeaud L, Kanayson P, Patrick C, Rockenstein E, Patrick GN, Masliah E. 2012. Differential effects of UCHL1 modulation on alpha-synuclein in PD-like models of alpha-synucleinopathy. PLoS One 7:e34713.
  • Castegna A, Aksenov M, Aksenova M, Thongboonkerd V, Klein JB, Pierce WM, Booze R, Markesbery WR, Butterfield DA. 2002. Proteomic identification of BB, oxidatively modified proteins in Alzheimer's disease brain. Part I: creatine kinase glutamine synthase, and ubiquitin carboxy-terminal hydrolase L-1. Free Radic Biol Med 33:562571.
  • Chen HM, Lin CY, Wang V. 2011. Amyloid P component as a plasma marker for Parkinson's disease identified by a proteomic approach. Clin Biochem 44:377385.
  • Cheroni C, Peviani M, Cascio P, Debiasi S, Monti C, Bendotti C. 2005. Accumulation of human SOD1 and ubiquitinated deposits in the spinal cord of SOD1G93A mice during motor neuron disease progression correlates with a decrease of proteasome. Neurobiol Dis 18:509522.
  • de Vries RL, Przedborski S. 2012. Mitophagy and Parkinson's disease: be eaten to stay healthy. Mol Cell Neurosci (in press).
  • Faes L, Callewaert G. 2011. Mitochondrial dysfunction in familial amyotrophic lateral sclerosis. J Bioenerg Biomembr 43:587592.
  • Federico A, Cardaioli E, Da Pozzo P, Formichi P, Gallus GN, Radi E. 2012. Mitochondria, oxidative stress and neurodegeneration. J Neurol Sci 322:254262.
  • Foote M, Zhou Y. 2012. 14-3-3 Proteins in neurological disorders. Int J Biochem Mol Biol. 3:152164.
  • Fukai T, Ushio-Fukai M. 2011. Superoxide dismutases: role in redox signaling, vascular function, and diseases. Antioxid Redox Signal 15:15831606.
  • Fukata Y, Oshiro N, Kinoshita N, Kawano Y, Matsuoka Y, Bennett V, Matsuura Y, Kaibuchi K. 1999. Phosphorylation of adducin by rho-kinase plays a crucial role in cell motility. J Cell Biol 145:347361.
  • Goswami T, Li X, Smith AM, Luderowski EM, Vincent JJ, Rush J, Ballif BA. 2012. Comparative phosphoproteomic analysis of neonatal and adult murine brain. Proteomics 12:21852189.
  • Grant P, Pant HC. 2000. Neurofilament protein synthesis and phosphorylation. J Neurocytol 29:843872.
  • Grimm S, Hoehn A, Davies KJ, Grune T. 2011. Protein oxidative modifications in the ageing brain: consequence for the onset of neurodegenerative disease. Free Radic Res 45:7388.
  • Hernández MP, Sullivan WP, Toft DO. 2002. The assembly and intermolecular properties of the hsp70-Hop-hsp90 molecular chaperone complex. J Biol Chem 277:3829438304.
  • Hino M, Kijima-Suda I, Nagai Y, Hosoya H. 2003. Glycosylation of the alpha and beta tubulin by sialyloligosaccharides. Zool Sci 20:709715.
  • Huang daW, Sherman BT, Lempicki RA. 2009. Systematic and integrative analysis of large gene lists using David bioinformatics resources. Nat Protoc 4:4457.
  • Hwang H, Zhang J, Chung KA, Leverenz JB, Zabetian CP, Peskind ER, Jankovic J, Su Z, Hancock AM, Pan C, Montine TJ, Pan S, Nutt J, Albin R, Gearing M, Beyer RP, Shi M, Zhang J. 2010. Glycoproteomics in neurodegenerative diseases. Mass Spectrom Rev 29:79125.
  • Kamphuis W, Mamber C, Moeton M, Kooijman L, Sluijs JA, Jansen AH, Verveer M, de Groot LR, Smith VD, Rangarajan S, Rodríguez JJ, Orre M, Hol EM. 2012. GFAP isoforms in adult mouse brain with a focus on neurogenic astrocytes and reactive astrogliosis in mouse models of Alzheimer disease. PLoS One 7:e42823.
  • Kawabe H, Brose N. 2011. The role of ubiquitylation in nerve cell development. Nat Rev Neurosci 12:251268.
  • Kim SI, Voshol H, van Oostrum J, Hastings TG, Cascio M, Glucksman MJ. 2004. Neuroproteomics: expression profiling of the brain's proteomes in health and disease. Neurochem Res 29:13171331.
  • Korolainen MA, Auriola S, Nyman TA, Alafuzoff I, Pirttilä T. 2005. Proteomic analysis of glial fibrillary acidic protein in Alzheimer's disease and aging brain. Neurobiol Dis 20:858870.
  • Lehman NL. 2009. The ubiquitin proteasome system in neuropathology. Acta Neuropathol 118:329347.
  • Lehnert S, Jesse S, Rist W, Steinacker P, Soininen H, Herukka SK, Tumani H, Lenter M, Oeckl P, Ferger B, Hengerer B, Otto M. 2012. iTRAQ and multiple reaction monitoring as proteomic tools for biomarker search in cerebrospinal fluid of patients with Parkinson's disease dementia. Exp Neurol 234:499505.
  • Magrané J, Hervias I, Henning MS, Damiano M, Kawamata H, Manfredi G. 2009. Mutant SOD1 in neuronal mitochondria causes toxicity and mitochondrial dynamics abnormalities. Hum Mol Genet 18:45524564.
  • Martin LJ. 2007. Transgenic mice with human mutant genes causing Parkinson's disease and amyotrophic lateral sclerosis provide common insight into mechanisms of motor neuron selective vulnerability to degeneration. Rev Neurosci 18:115136.
  • Matsuoka Y, Li X, Bennett V. 2000. Adducin: structure, function and regulation. Cell Mol Life Sci 57:884895.
  • Menegon A, Board PG, Blackburn AC, Mellick GD, Le Couteur DG. 1998. Parkinson's disease, pesticides, and glutathione transferase polymorphisms. Lancet 352:13441346.
  • Mhatre M, Floyd RA, Hensley K. 2004. Oxidative stress and neuroinflammation in Alzheimer's disease and amyotrophic lateral sclerosis: common links and potential therapeutic targets. J Alzheimers Dis 6:147157.
  • Murakami K, Murata N, Noda Y, Tahara S, Kaneko T, Kinoshita N, Hatsuta H, Murayama S, Barnham KJ, Irie K, Shirasawa T, Shimizu T. 2011. SOD1 (copper/zinc superoxide dismutase) deficiency drives amyloid β protein oligomerization and memory loss in mouse model of Alzheimer disease. J Biol Chem 286:4455744568.
  • Nagley P, Higgins GC, Atkin JD, Beart PM. 2010. Multifaceted deaths orchestrated by mitochondria in neurones. Biochim Biophys Acta 1802:167185.
  • Nishioka K, Wider C, Vilariño-Güell C, Soto-Ortolaza AI, Lincoln SJ, Kachergus JM, Jasinska-Myga B, Ross OA, Rajput A, Robinson CA, Ferman TJ, Wszolek ZK, Dickson DW, Farrer MJ. 2010. Association of alpha-, beta-, and gamma-Synuclein with diffuse lewy body disease. Arch Neurol 67:970975.
  • Radivojac P, Vacic V, Haynes C, Cocklin RR, Mohan A, Heyen JW, Goebl MG, Iakoucheva LM. 2010. Identification, analysis and prediction of protein ubiquitination sites. Proteins Structure Function Bioinformatics 78:365380.
  • Ranganathan S, Williams E, Ganchev P, Gopalakrishnan V, Lacomis D, Urbinelli L, Newhall K, Cudkowicz ME, Brown RH Jr, Bowser R. 2005. Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosis. J Neurochem 95:14611471.
  • Reijn TS, Abdo WF, Schelhaas HJ, Verbeek MM. 2009. CSF neurofilament protein analysis in the differential diagnosis of ALS. J Neurol 256:615619.
  • Robinson PA. 2010. Understanding the molecular basis of Parkinson's disease, identification of biomarkers and routes to therapy. Expert Rev Proteomics 7:565578.
  • Rothstein JD. 2009. Current hypotheses for the underlying biology of amyotrophic lateral sclerosis. Ann Neurol 65(Suppl 1):S3S9.
  • Ryberg H, An J, Darko S, Lustgarten JL, Jaffa M, Gopalakrishnan V, Lacomis D, Cudkowicz M, Bowser R. 2010. Discovery and verification of amyotrophic lateral sclerosis biomarkers by proteomics. Muscle Nerve 42:104111.
  • Shi M, Bradner J, Bammler TK, Eaton DL, Zhang J, Ye Z, Wilson AM, Montine TJ, Pan C, Zhang J. 2009. Identification of glutathione S-transferase pi as a protein involved in Parkinson disease progression. Am J Pathol 175:5465.
  • Singh SK, Sundaram CS, Shanbhag S, Idris MM. 2010. Proteomic profile of zebrafish brain based on two-dimensional gel electrophoresis matrix-assisted laser desorption/ionization MS/MS analysis. Zebrafish 7:169177.
  • Smeyne M, Boyd J, Raviie Shepherd K, Jiao Y, Pond BB, Hatler M, Wolf R, Henderson C, Smeyne RJ. 2007. GSTpi expression mediates dopaminergic neuron sensitivity in experimental parkinsonism. Proc Natl Acad Sci U S A 104:19771982.
  • Stachowiak JC, Schmid EM, Ryan CJ, Ann HS, Sasaki DY, Sherman MB, Geissler PL, Fletcher DA, Hayden CC. 2012. Membrane bending by protein–protein crowding. Nat Cell Biol 14:944949.
  • Steinacker P, Aitken A, Otto M. 2011. 14-3-3 Proteins in neurodegeneration. Semin Cell Dev Biol 22:696704.
  • Ström AL, Shi P, Zhang F, Gal J, Kilty R, Hayward LJ, Zhu H. 2008. Interaction of amyotrophic lateral sclerosis (ALS)-related mutant copper-zinc superoxide dismutase with the dynein-dynactin complex contributes to inclusion formation. J Biol Chem 283:2279522805.
  • Takei YI, Oguchi K, Koshihara H, Hineno A, Nakamura A, Ohara S. 2013. α-Synuclein coaggregation in familial amyotrophic lateral sclerosis with SOD1 gene mutation. Hum Pathol S0046-8177(12)00422-4 (in press).
  • Tateno M, Kato S, Sakurai T, Nukina N, Takahashi R, Araki T. 2009. Mutant SOD1 impairs axonal transport of choline acetyltransferase and acetylcholine release by sequestering KAP3. Hum Mol Genet 18:942955.
  • Wang J, Xu G, Borchelt DR. 2002. High molecular weight complexes of mutant superoxide dismutase 1: age-dependent and tissue-specific accumulation. Neurobiol Dis 9:139148.
  • Westerfield M. 2000. The zebrafish book. A guide for the laboratory use of zebrafish Danio rerio, 4th ed. Eugene, OR: University of Oregon Press.
  • Xu XM, Lin H, Maple J, Björkblom B, Alves G, Larsen JP, Møller SG. 2010. The arabidopsis DJ-1a protein confers stress protection through cytosolic SOD activation. J Cell Sci 123:16441651.
  • Yuan Z, Agarwal-Mawal A, Paudel HK. 2004. 14-3-3 Binds to and mediates phosphorylation of microtubule-associated tau protein by Ser9-phosphorylated glycogen synthase kinase 3beta in the brain. J Biol Chem 279:2610526114.
  • Zabel M, Schrag M, Mueller C, Zhou W, Crofton A, Petersen F, Dickson A, Kirsch WM. 2012. Assessing candidate serum biomarkers for Alzheimer's disease: a longitudinal study. J Alzheimers Dis 30:311321.
  • Zhou JY, Afjehi-Sadat L, Asress S, Duong DM, Cudkowicz M, Glass JD, Peng J. 2010. Galectin-3 is a candidate biomarker for amyotrophic lateral sclerosis: discovery by a proteomics approach. J Proteome Res 9:51335141.

Supporting Information

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  2. Abstract
  7. Supporting Information

Additional Supporting Information may be found in the online version of this article.

jnr23297-sup-0001-suppinfo.tif321KSupporting Information Figure 1
jnr23297-sup-0002-suppinfo.tif191KSupporting Information Figure 2
jnr23297-sup-0003-suppinfo.tif115KSupporting Information Figure 3
jnr23297-sup-0004-suppinfo.tif23543KSupporting Information Figure 4
jnr23297-sup-0005-suppinfo.tif245KSupporting Information Figure 5
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