Urine proteomic profiling of uranium nephrotoxicity

Biochimica et Biophysica Acta 1794 (2009) 882–891

Contents lists available at ScienceDirect

Biochimica et Biophysica Acta j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / b b a p a p

Urine proteomic profiling of uranium nephrotoxicity Véronique Malard a,⁎, Jean-Charles Gaillard a, Frédéric Bérenguer b, Nicole Sage a, Eric Quéméneur c a b c

CEA, DSV, IBEB, SBTN, Laboratoire de Biochimie des Systèmes Perturbés (LBSP), Bagnols-sur-Cèze, F-30207, France CEA, DSV, IBEB, SBTN, Laboratoire d'Etude des Protéines Cibles (LEPC), Bagnols-sur-Cèze, F-30207, France CEA, DSV, IBEB, SBTN, Bagnols-sur-Cèze, F-30207, France

a r t i c l e

i n f o

Article history: Received 2 July 2008 Received in revised form 23 January 2009 Accepted 27 January 2009 Available online 7 February 2009 Keywords: Mass spectrometry Toxicology Toxicoproteomics Uranium Urine

a b s t r a c t Uranium is used in many chemical forms in civilian and military industries and is a known nephrotoxicant. A key issue in monitoring occupational exposure is to be able to evaluate the potential damage to the body, particularly the kidney. In this study we used innovative proteomic techniques to analyse urinary protein modulation associated with acute uranium exposure in rats. Given that the rat urinary proteome has rarely been studied, we first identified 102 different proteins in normal urine, expanding the current proteome data set for this central animal in toxicology. Rats were exposed intravenously to uranyl nitrate at 2.5 and 5 mg/kg and samples were collected 24 h later. Using two complementary proteomic methods, a classic 2-DE approach and semi-quantitative SDS-PAGE-LC-MS/MS, 14 modulated proteins (7 with increased levels and 7 with decreased levels) were identified in urine after uranium exposure. Modulation of three of them was confirmed by western blot. Some of the modulated proteins corresponded to proteins already described in case of nephrotoxicity, and indicated a loss of glomerular permeability (albumin, alpha-1-antiproteinase, serotransferrin). Others revealed tubular damage, such as EGF and vitamin D-binding protein. A third category included proteins never described in urine as being associated with metal stress, such as ceruloplasmin. Urinary proteomics is thus a valuable tool to profile uranium toxicity non-invasively and could be very useful in follow-up in case of accidental exposure to uranium. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Uranium is a naturally abundant actinide on Earth and is heavily used in many chemical forms in civilian and military industries. Possible accidental exposure to uranium dust or spatters during the process from mining to industrial application and waste disposal is a matter of concern. Whatever its route of entry into the body, uranium reaches the blood and is partly stored in target organs such as bones and kidneys. Uranium is nephrotoxic, in both human and animal models, and its effects have been widely described [1–7]. Following absorption, uranium is mostly eliminated in the urine. Biological monitoring of accidental exposure mainly involves measuring the concentration of metal in the urine. Assessing potential kidney damage deserves additional monitoring, because measuring the quantity of excreted metal does not necessarily reflect organ damage, which varies from one person to another. Therefore, a key issue in monitoring occupational exposure is to be able to evaluate potential damage to the body directly. Biochemical markers are currently Abbreviations: CEA, Commissariat à l'Energie Atomique; DSV, Direction des Sciences du Vivant; IBEB, Institut de Biologie Environnementale et de Biotechnologie; SBTN, Service de Biochimie et Toxicologie Nucléaire; BUN, blood urea nitrogen ⁎ Corresponding author. Service de Biochimie post-génomique et Toxicologie Nucléaire, DSV/IBEB, CEA MARCOULE, B.P. 17171, 30207 Bagnols-sur-Cèze, France. Tel.: +33 0 4 66 79 68 18; fax: +33 0 4 66 79 19 05. E-mail address: [email protected] (V. Malard). 1570-9639/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.bbapap.2009.01.010

analysed to monitor renal damage in general: blood urea nitrogen (BUN), plasma creatinine and proteinurea [8] but they have limitations [9]. Other effect markers have also been proposed such as alkaline phosphatase and beta 2-microglobulin [10]. Urine is a suitable material for the diagnosis of several diseases, which may or may not be related to kidney failure. Urine biomarkers have been sought for use in a proteomic approach for several pathologies such as prostate cancer [11], bladder cancer [12], urothelial carcinoma [13], renal Fanconi syndrome [14], transitional cell carcinoma [15], type 1 diabetes [16], and acute rejection of renal allograft [17] mostly using two-dimensional gel electrophoresis. More recently, using urinary proteomics, predictive biomarkers of clinical evolution of the neonatal ureteropelvic junction were identified [18]. The creation of the Human Urine and Kidney Proteome Project (HKUPP: http://hkupp.kir.jp/) within HUPO, and the establishment of a European Network for Urine and Kidney Proteomics [19] in 2008, demonstrate that urinary proteomics is a fast growing field. The purpose of our study was to use innovative proteomic techniques for qualitative and quantitative assessment of urinary protein modulation associated with acute uranium exposure in rats. We analysed samples after a short time (24 h) to detect early changes. Given that the rat urinary proteome has rarely been studied [20,21], a preliminary step of our work consisted in describing the normal urinary proteome. In order to provide the most comprehensive analysis we identified proteins which are differentially expressed following uranium exposure, by

V. Malard et al. / Biochimica et Biophysica Acta 1794 (2009) 882–891

combining two complementary analysis techniques: (i) bidimensional (2-DE) electrophoresis followed by mass fingerprinting (MALDI-TOF) identification, (ii) SDS-PAGE separation followed by (LC-MS/MS) and the use of spectral counting as a semi-quantitative tool to identify proteins that change in abundance [22]. 2. Materials and methods 2.1. Animal handling and sample collection A total of 19 male Sprague-Dawley rats weighing 220–250 g purchased from the IFFA CREDO company were used. All the studies were performed in compliance with European act no. 2001-486, June 6, 2001. The rats had free access to standard rat chow and drinking water and were housed in metabolic cages. Five rats weighing 250 g received a single intravenous injection of 200 μL of a 6.25 mg/mL or 3.125 mg/mL solution of uranyl nitrate (UO2(NO3)2: 6 H2O, Prolabo Merck, purity 99%) diluted in NaCl 0.9% (U) corresponding to 5 mg/kg or 2.5 mg/kg respectively. 24 h after the U injection, the animals were killed and their blood was collected. 24 h urine was collected at room temperature, and stored at −80 °C after adding protease inhibitor (Complete™; Roche Diagnostics). For analysis, the urine samples were thawed and centrifuged at 14,000 g for 5 min at 4 °C to eliminate precipitates. One mL of the supernatant was transferred to Centricon YM-3 membrane concentrators (Millipore) and spun at 14,000 g to reduce the volumes to about 50 μL. The retentate was then diluted with 400 μL 0.1 M NH4HCO3. This step was repeated twice and the sample was then evaporated with a speed vac. The pellet was resuspended with 0.1 M NH4HCO3. Protein concentration was determined by a Bradford assay (Biorad) using BSA as a standard. The whole proteomic study was performed using urine from rats treated with 5 mg/kg and control rats. Urine from rats treated with 2.5 mg/kg was added to perform western blot confirmation. The variability at biological level (biological replicates) was evaluated by using five animals for each treatment. The technical variability (technical replicates) was checked by repeating the experiments two or three times.

883

was stopped in a storage solution (2% acetic acid, Tris base 40 g/L). The gels were scanned using an Image Scanner (GE Healthcare) at 300 dpi– 8 bits. Differential analysis was performed using Image Master v5.0 (GE Healthcare). The protein content of each spot was determined by its relative volume compared with the sum of all spot volumes in the gel, and expressed as a percentage of volume. Two series of experiments were performed: the first on pooled urine samples and the second on individual urine samples. Statistical analysis was performed on individual urine samples classes: U treated samples (5 gels) and control samples (5 gels). Class ratios N1.5 (meaning 1.5× change from control) were selected, t-test controlled (p b 0.05), and the differences were examined visually prior to identification by MS. Details of image analysis are given in supplementary material (Table 1). 2.4. MALDI-TOF MS 2-DE protein spots were submitted to trypsin digestion using the Montage In-Gel DigestZP Kit (Millipore), and analysed by MALDI-TOF (Biflex, Bruker Daltonics) in reflectron mode. A saturated solution of α-cyano-4 hydroxycinnamic acid prepared in ACN:water 1:1 with 0.1% TFA was 1/4 diluted in the same solvent, and used as a matrix. Peptide and matrix solutions were spotted and air-dried on a stainless steel plate. Spectra were externally calibrated, using a standard peptide mixture (Peptide Calibration Standard, Bruker). Spectra were obtained over a mass range of 600–3500 Da in short-pulsed ion extraction mode using an accelerating voltage of 19 kV. Peptide mass fingerprints were analysed with the SWISS-PROT and NCBInr databases using Mascot software (Matrix Science) [23]. The maximum number of missed cleavages was set at one. Cys carbamidomethylation was set as the fixed modification and Met oxidation as the variable modification. Protein identification reliability was evaluated on the basis of the Mowse score (probability-based Mowse score p b 0.05), mass error, number of peptide matches, percentage cover of matched protein and similarity of experimental and theoretical protein molecular masses and pIs. 2.5. SDS-PAGE — protein digestion and LC ESI MS/MS

2.2. Biochemical analyses Serum samples were analysed twice for blood urea nitrogen (BUN) and creatinine concentration using a Roche Integra 800 automatic analyser (Roche). Anova, and subsequently Tukey paired comparisons as a post-hoc test, were applied to establish the level of differences among groups using SigmaStat (Systat Software). A p value below 0.05 was considered as statistically significant. 2.3. 2-D electrophoresis and image analysis 200 μL of urine was concentrated, dried and resuspended in 350 μL 8 M urea, 2 M thiourea, 4% CHAPS, 10 mM DTT and 10 μg/mL IPG buffer (Pharmacia). Immobilines Drystrips pH 4–7–18 cm (GE Healthcare) were rehydrated overnight with the samples. Focusing took place on a Multiphor system (GE Healthcare) up to 60 kV. After IEF separation, strips were equilibrated for 15 min in 50 mM Tris–HCl pH 8.8, 6 M urea and 30% glycerol containing 10 mg/mL DTT to disrupt disulphide bridges. The reduced thiols were then alkylated with 25 mg/mL iodoacetamide in the same buffer for 15 min. The second dimension was performed using home-made 12% acrylamide gels in a Protean II xi 2-D cell (Biorad) at 25 V for 1 h then at 12.5 W/gel. The gels were first fixed in 5% acetic acid and 30% ethanol overnight, washed 3 times in H2O for 10 min, treated with 0.02% sodium thiosulphate for 1 min then rinsed twice with H2O for 1 min. 2 g/L of silver nitrate containing 280 μL/L of 37% formaldehyde was used to stain the gels for 40 min in the dark. The gels were then quickly rinsed in H2O for 5–10 s, and developed in 24 g/L of sodium carbonate containing 125 μL/L of 10% sodium thiosulphate and 280 μL/L of 37% formaldehyde. The reaction

50 μg of proteins obtained from pooled urine samples were separated on a 4–12% NuPAGE gel in a 2-(N-morpholino)-ethanesulfonic acid running buffer (Invitrogen) at 200 V. The migration process was stopped after 13 min (2.5 cm migration). After staining with colloidal Coomassie (Invitrogen), the gel lane was cut into 9–12 pieces and subjected to in-gel-tryptic digestion. Gel slices were destained with 60% acetonitrile (ACN)/100 mM NH4HCO3 reduced with 10 mM DTT at 56 °C for 45 min and alkylated in the dark with 55 mM iodoacetamide at room temperature for 30 min. The gel pieces were then dried and incubated overnight at 37 °C with 0.6 μg trypsin solution (Roche) in 25 mM NH4HCO3 and 1% CaCl2. The resulting tryptic peptides were extracted from the gel using 100 μL 1% formic acid (FA) then with 100 μL 1% FA and 60% ACN. The extracts were evaporated in a vacuum centrifuge and the pellets resuspended in 0.1% TFA. The peptides were analysed on an UltiMate™ NanoLC system (Dionex-LC Packings) coupled to a Bruker esquire3000 plus™ ion trap, equipped with an online nanoESI source. The complete LC-MS setup was controlled by HyStar™software. The instrument was connected online with a PepMap C18 Nanocolumn (75 μm × 150 mm (LC Packings)). Following the injection of 19 μL of sample, a 5 min wash with 0.1% FA was performed. A linear 90 min gradient was achieved from 100% solvent A (95:5 H2O/ACN 0.1% FA) to 60% solvent B (20:80 H2O/ACN 0.1% FA) at a flow rate of 200 nl/min. As peptides eluted from the microcapillary column, they were electrosprayed into the nanoESI source. Spray voltage and capillary temperature were set at 2000 V and 180 °C respectively. The ion trap was operated in data-dependent MS to MS/MS switching mode using various precursors detected in the 100– 2000 m/z unit window, selected using a 3 m/z unit ion isolation

884

V. Malard et al. / Biochimica et Biophysica Acta 1794 (2009) 882–891

window and excluding singly charged ions. The charge state of precursor ions was detected instantaneously during acquisition using a Bruker proprietary algorithm. Ion parent and fragmentation mass spectra were analysed using DataAnalysis software (Bruker Daltonics). Searches against the Swissprot database were performed for each sample using MASCOT after concatenation of mgf files. MASCOT search parameters used with MS/MS data were: Taxonomy = Rattus, Enzyme = trypsin, 2 miscleavages allowed, variable modifications = acetyl (N-ter)/oxidation (M)/carbamidomethyl (C)/deamidation (NQ), peptide tolerance = 0.4 Da, MS/MS tolerance = 0.4 Da, peptide charge = 1+/2+/3+. IRMA software (J. Garin, CEA) was used to process the data. IRMA filters the results of Mascot research starting with the “parser” marketed by Matrix Science and builds its own dataprocessing model of the data representation. For the normal urinary proteome, the results were filtered using the following criteria: analysis was performed only on rank 1 peptides with ion scores N30, a stringent ion score limit to avoid misidentification. Proteins identified by two different peptides were selected (p b 0.0002). Proteins identified with only one peptide and more than one peptide hit (one peptide identified several times) were also selected if their MOWSE score was higher than 51 (p b 0.001). For the differential analysis only rank 1 peptides with ion scores N15 were taken into account and proteins with a Mascot MOWSE score higher than 34 (p b 0.05). The spectral counting, which determines relative changes in protein concentration on the basis of peptide counts from a particular protein, was used to perform the differential analysis [22,24–26]. The Excel spreadsheet file output by IRMA was processed using a macro for spectral counting, then further statistical analysis was performed using SigmaStat software (Systat Software). According

to Gao [27], normalization was performed using the total number of counts for each gel lane, and according to Fach et al. [28], a t-test was used to establish differences for each protein independently. We selected ratios N1.5 (meaning 1.5 or 0.66 fold change from control). We used a 95% confidence threshold and selected differences with p b 0.05. Details are given in supplementary material (Table 2). 2.6. Western blot Protein samples were separated on a 4–12% NuPAGE gel in a MOPS buffer (Invitrogen). The gels were electroblotted on PVDF membranes (Millipore) using a semi-dry system (Biometra). The membranes were then incubated overnight at 4 °C in a 5% milk solution containing mouse monoclonal anti-rat albumin clone RSA 17 (0.4 μg/gel, produced in-house), mouse monoclonal anti-rat ceruloplasmin (1/ 333, BD Biosciences) and rabbit polyclonal anti-rat EGF (1/5000, Centaur). The primary antibodies were then detected using a mouse anti rabbit IgG HRP (1/5000, sc2357, Santa Cruz) or a goat anti-mouse (1/5000, Novagen) coupled to HRP. The images were revealed using the chemoluminescence substrate SuperSignal West Pico or Femto (Pierce). Signal quantification was performed using Image master and p values were calculated using SigmaStat (Systat Software). 3. Results 3.1. Analysis of normal rat urine We used ultrafiltration for protein concentration and desalting with a 3 kDa cut-off of the ultrafiltration membrane that prevented

Fig. 1. Rat urinary protein separation. (A) SDS-PAGE. 50 μg of concentrated urinary proteins were applied to a 4–12% Bis–Tris gel, separated at 200 V up to 2.5 cm, and stained by colloidal Coomassie. (B) 2-DE electrophoresis. 250 μg of concentrated urinary proteins were separated by 2-DE (first dimension, 18 cm 4–7 IPG strips, second dimension 12% SDS PAGE) and visualised by silver staining.

V. Malard et al. / Biochimica et Biophysica Acta 1794 (2009) 882–891

885

Table 1 Rat urinary proteins identified by SDS-PAGE-LC-MS/MS Acc. no.a

Protein identity

MOWSE scoreb

Sequence coverage (%)c

Matching peptidesc

Total peptides hitsd

MW

pI

P02761 P02770 P81827 P81828 P07522 P05544 P05545 P01836 P01835 P27590 P83121 P12346 Q64240 P22282 P15684 P00689 P20059 P21704 P17475 P02781 Q9R0T4 P00758 P15399 Q6IG01 P20767 Q9EPB1 P84855 P98158 P14046 P36373 P01048 P24090 Q64230 P02782 P06760 P14841 P36953 P14740 P15083 P07647 P22283 P36376 Q63257 P30152 Q6P6Q2 Q62867 P08932 Q64319 Q6IFW6 P08723 P13635 P02650 O35568

Major urinary protein Serum albumin Urinary protein 1 Urinary protein 2 Pro-epidermal growth factor Contrapsin-like protease inhibitor 3 Contrapsin-like protease inhibitor 1 Ig kappa chain C region, A allele Ig kappa chain C region, B allele Uromodulin Urinary protein 3 Serotransferrin p AMBP protein Cystatin-related protein 1 Aminopeptidase N Pancreatic alpha-amylase Hemopexin Deoxyribonuclease-1 Alpha-1-antiproteinase Prostatic steroid-binding protein C2 chain Epithelial-cadherin Nerve growth factor gamma chain Probasin Keratin, type II cytoskeletal 1b Ig lambda-2 chain C region Dipeptidyl-peptidase 2 RT Major urinary protein Low-density lipoprotein receptor-related protein 2 Alpha-1-inhibitor 3 Glandular kallikrein-7, submandibular/renal T-kininogen 1 Alpha-2-HS-glycoprotein Meprin A subunit alpha Prostatic steroid-binding protein C1 chain Beta-glucuronidase Cystatin C Afamin Dipeptidyl peptidase 4 Polymeric-immunoglobulin receptor Submandibular glandular kallikrein-9 Cystatin-related protein 2 Glandular kallikrein-12, submandibular/renal Interleukin-4 receptor alpha chain Neutrophil gelatinase-associated lipocalin Keratin, type II cytoskeletal 5 Gamma-glutamyl hydrolase T-kininogen 2 Neutral and basic amino acid transport protein rBAT Keratin, type I cytoskeletal 10 Prostatic spermine-binding protein Ceruloplasmin Apolipoprotein E EGF-containing fibulin-like extracellular matrix protein 1 Haptoglobin Prostaglandin-H2 D-isomerase Liver carboxylesterase 1 C-reactive protein Prostatic steroid-binding protein C3 chain Cathepsin B Actin, cytoplasmic 2 Endothelial cell-selective adhesion molecule Alpha-1-acid glycoprotein Prolactin-inducible protein homolog Plasminogen precursor Retinoid-inducible serine carboxypeptidase Prothrombin precursor Myosin-10 Cleavage and polyadenylation specificity factor 7 Gamma-glutamyltranspeptidase 1 Contrapsin-like protease inhibitor 6 Keratin, type I cytoskeletal 14 Keratin, type I cytoskeletal 15 Keratin, type I cytoskeletal 19 Mucin and cadherin-like protein

1907 3042 240 238 2295 1270 1085 372 408 659 167 1545 692 509 778 385 902 517 857 255 404 242 241 252 302 316 221 955 524 306 481 300 390 103 388 194 486 431 319 207 286 112 176 275 246 174 326 396 399 373 460 357 240

82.3 73.0 29.7 30.7 35.1 49.2 39.7 44.3 49.1 19.4 32.7 38.0 32.4 33.5 15.9 15.2 35.4 35.6 32.4 35.7 10.5 29.5 21.5 8.3 64.4 14.4 100.0 5.2 8.6 24.5 22.6 23.6 9.5 19.8 14.2 29.3 24.7 12.6 10.8 24.3 36.9 8.5 3.2 16.2 7.1 10.7 20.2 10.8 10.8 15.1 11.9 22.8 11.0

28 49 3 2 43 21 15 6 6 12 2 28 12 9 12 7 18 9 14 4 7 4 5 4 5 5 4 21 11 5 9 6 7 2 8 3 11 9 6 4 6 2 2 6 4 3 7 7 6 5 10 6 5

2011 759 441 398 350 228 223 182 173 156 137 126 102 87 72 69 67 60 58 48 46 41 39 39 38 36 35 34 34 32 30 30 27 27 26 24 23 22 22 22 21 21 18 17 16 16 15 15 14 14 13 12 10

20723 68686 10952 11060 124046 46248 46532 11725 11594 71015 11092 76346 38826 21047 109380 57141 51318 32044 46107 12820 98654 28833 20767 57220 11311 55079 3426 518937 163670 28953 47745 37958 85085 12754 74746 15427 69290 88033 84745 28349 21000 28740 86663 22544 61788 35807 47673 78457 56470 31061 120764 35731 54560

5.85 6.09 6.67 7.53 5.71 5.48 5.31 4.99 4.97 4.78 6.85 7.14 5.77 8.87 5.30 8.34 7.58 5.06 5.70 5.14 4.67 4.55 9.65 5.48 5.76 4.87 6.77 5.03 5.70 5.63 6.08 6.05 5.60 4.47 6.27 9.35 5.87 5.93 5.07 7.01 9.21 5.31 5.03 8.41 7.61 8.16 5.94 5.48 5.10 3.83 5.34 5.23 4.99

208 257 217 153 99 243 181 174 139 99 145 172 146 83 53 315 209 220 201 169 173

14.1 22.2 9.7 13.9 23.2 23.0 15.2 12.2 16.1 24.0 6.0 11.7 5.2 0.7 1.5 12.0 11.0 6.0 5.6 6.9 4.2

5 5 4 3 2 5 4 3 3 2 4 4 3 2 1 6 3 3 3 3 3

10 10 10 10 10 9 9 9 8 8 7 7 7 7 7 6 6 6 6 6 6

38539 21288 60136 25452 10723 37446 41766 41910 23560 16428 90477 51142 70367 228824 51042 61517 46622 52651 48840 44609 90920

6.10 5.66 5.51 4.89 5.53 5.36 5.31 9.33 5.64 4.58 6.79 5.37 6.28 5.49 7.33 7.21 5.32 5.08 4.80 5.21 4.69

P06866 P22057 P10959 P48199 P02780 P00787 P63259 Q6AYD4 P02764 O70417 Q01177 Q920A6 P18292 Q9JLT0 Q5XI29 P07314 P09006 Q6IFV1 Q6IFV3 Q63279 Q9JIK1

(continued on next page)

886

V. Malard et al. / Biochimica et Biophysica Acta 1794 (2009) 882–891

Table 1 (continued) Acc. no.a

Protein identity

P42854 P23680 Q6IUU3 P07151 Q99041 P04937 Q9JHY1 P38918 P01015 P01026 Q10758 P27274 P02091 P11517 P42123 P28826 P07861 Q03191 Q6P734 P51635 P50123 P19218

Regenerating islet-derived protein 3 gamma Serum amyloid P-component Sulfhydryl oxidase 1 Beta-2-microglobulin Protein-glutamine gamma-glutamyltransferase 4 Fibronectin Junctional adhesion molecule A Aflatoxin B1 aldehyde reductase member 1 Angiotensinogen Complement C3 Keratin, type II cytoskeletal 8 CD59 glycoprotein Hemoglobin subunit beta-1 Hemoglobin subunit beta-2 L-lactate dehydrogenase B chain Meprin A subunit beta Neprilysin Trefoil factor 3 Plasma protease C1 inhibitor Alcohol dehydrogenase Glutamyl aminopeptidase Pancreatic secretory granule membrane major glycoprotein GP2 Keratin, type II cytoskeletal 4 Ig kappa chain V region S211 Urokinase-type plasminogen activator Complement C4 Ezrin Multiple inositol polyphosphate phosphatase 1

Q6IG00 P01681 P29598 P08649 P31977 O35217

MOWSE scoreb

Sequence coverage (%)c

Matching peptidesc

Total peptides hitsd

MW

pI

135 55 150 89 174 96 133 71 176 136 144 115 130 100 123 110 111 60 55 84 92 68

28.2 5.3 5.1 18.5 8.2 1.0 9.0 4.5 8.6 2.3 5.8 16.7 21.1 19.7 8.1 3.0 3.2 19.8 2.6 6.8 3.2 7.0

3 1 3 2 4 2 2 1 3 3 3 2 2 2 2 2 2 1 1 2 2 2

6 6 5 5 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 2 2 2

19131 26159 82360 13711 75424 272341 32349 36723 51949 186342 53985 13781 15969 15972 36589 79199 85741 8938 55576 36483 107927 58670

7.60 5.50 8.11 7.80 8.48 5.50 6.18 6.80 5.37 6.12 5.83 8.90 7.87 8.91 5.70 5.16 5.75 4.77 5.53 6.84 5.24 4.90

110 64 100 99 59 55

3.4 36.1 5.1 0.9 1.5 2.3

2 2 2 1 1 1

2 2 2 2 2 2

57631 11940 47926 192042 69348 54555

7.52 7.94 8.07 6.99 5.83 6.44

a

Description, accession number according to Swiss–Prot database. Mowse score is − 10Log (p), where p is the probability that the observed match is a random event. A score higher than 51 is significant (p b 0.001). It is based on the NCBInr database using the MASCOT searching program as MS/MS data (http://www.matrixscience.com). c The number of matching peptides and the percentage of amino acid sequence coverage are given by MASCOT for the identified protein. d The total number of peptides was calculated using a spectral counting technique. b

the loss of low molecular weight proteins. Samples from two sets of animals were pooled, comprising 5 and 4 different animals, respectively. Each pool was analysed in triplicate using SDS-PAGE, and each gel lane was cut into 9 to 12 pieces. Fig. 1A shows a representative SDS-PAGE analysis of normal urine (left). Most of the proteins are located at MW lower than the 28 kDa standard. From the 63 LC-MS/MS experiments carried out, 102 unique proteins were identified using stringent statistical criteria. Of those, 94 were found to have at least two different peptides and 8 were found with a single peptide which was identified at least twice. No false-positive protein hits were found in our experimental dataset when performing a decoy database search using MASCOT. Table 1 shows the list of proteins identified, classified by the number of peptide hits (spectral counting). The ten major proteins are: major urinary protein, serum albumin, urinary protein 1 and 2, pro-epidermal growth factor, contrapsin-like protease inhibitor 1 and 3, Ig kappa chain C region and uromodulin. Up to 2011 peptide counts and 82% coverage were obtained for major urinary protein. Based on spectral counting, these ten proteins account for almost three-quarters of the total protein content. The kidney plays a central part in protein conservation, the glomerular capillary barrier is critical in preventing the loss of proteins of molecular weight higher than 70 kDa. Unexpectedly, we noticed 26 proteins with a higher molecular weight than this cut-off. To determine whether we had identified fragments or entire proteins, we checked the original gel slice number for some proteins. For the low-density lipoprotein receptor-related protein 2 (518,936 Da), the 21 different peptides were found in gel slices corresponding to an MW higher than 188 kDa. This information and the fact that the first and last amino acids identified are n°187 and 4085 respectively, clearly indicate the presence of the entire form, or very large fragments of the protein. This protein is expressed in clathrin-coated pits and the soluble form is possibly derived by cleavage of kidney glomerular and proximal tubule epithelial cells at the cell surface. This could explain

its presence in urine. EGF (124,126 Da, one transmembrane domain) peptides were mainly found around 97 kDa in the gel, again suggesting the presence of the whole polypeptide. In other cases, as for myosin 10 and complement C3 and C4, protein fragmentation could be detected. 3.2. Urinary proteome modulation after uranium exposure 3.2.1. Uranium exposure and biochemical parameters The animals were exposed intravenously to 2.5 and 5 mg/kg uranium. The choice of these doses was based on the results of previous studies and are known to cause renal failure [2–4,29,30]. In this study, urine samples were collected 24 h after uranium exposure, and serum creatinine (sCr) and Blood-Urea Nitrogen (BUN) were assayed to assess renal function. No significant variation was seen in the 2.5 mg/kg group (Fig. 2). BUN and sCr increased slightly at 5 mg/ kg treatment (fold 1.5 and 1.4, p b 0.05) but remained within the reference interval for rat, which is consistent with previous work [3] where noticeable elevation was seen later. 3.2.2. 2-DE analysis Two experiments were performed using urine samples from 5 rats treated with 5 mg uranium/kg (U+) and 5 control rats (U−). An example of 2-DE is shown on Fig. 1B. After statistical analysis of the scanned gels obtained from individual samples, 19 spots were found to be modulated (ratio N 1.5, p b 0.05, Table 2). As shown in Fig. 3 (close-up of main spots), they all either appeared or increased in gels produced from the U treated animals. The shapes of some groups of spots are representative of protein isoforms. Of these, 17 spots were identified by mass fingerprinting (MALDI-TOF, Table 2). The two remaining spots n°104 and n°105 gave no identification in MALDI-MS in spite of a proper signal. They were analysed by LC-MS/MS (ESI-IT) and transthyretin was identified. The unsuccessful MALDI analysis was clearly due to a mixture

V. Malard et al. / Biochimica et Biophysica Acta 1794 (2009) 882–891

887

3.2.3. SDS-PAGE-LC-MS/MS differential analysis Three experiments were performed using urine samples from uranium treated rats (5 mg/kg) and control rats. We subjected the samples to one-dimensional SDS-PAGE and cut each gel lane into 9 or 10 pieces (Fig. 1A). Gel slices were processed as described in the materials and methods section. The results from each experiment were processed using spectral counting to perform differential analysis and semi-quantitate proteins [27]. Thirteen proteins were identified (ratio N 1.5, p b 0.05, Table 3), 6 with increased levels (ceruloplasmin, serum albumin, alpha-1-antiproteinase, transthyretin, haptoglobin and serotransferrin), and 7 with decreased levels (pro-epidermal growth factor, dipeptidyl-peptidase 2, contrapsin-like protease inhibitor 3, gamma-glutamyl hydrolase, nerve growth factor gamma chain, pancreatic alpha-amylase and epithelial-cadherin). These proteins were ranked according to their p values in Table 3. The most significant differences were observed for EGF and ceruloplasmin. The higher differences in term of peptide counts were obtained for EGF, ALBU and TFRE.

Fig. 2. Biochemical parameters Blood Urea Nitrogen (BUN) (A) and Serum creatinine (B) were measured on samples from individual animals in each dose group (0, 2.5 and 5 mg/ kg) collected 24 h after uranium exposure. Each bar represents mean +/− SEM values calculated from measurements of 5 animals performed twice (n = 10). ⁎ =p b 0.05.

of proteins in the spots. Of the 19 spots identified, 7 corresponded to alpha-1-antiproteinase, 8 to albumin, 1 to transferrin, 2 to transthyretin and finally 1 to vitamin D-binding protein (Fig. 3 and Table 2).

3.2.4. Cross-checking results Albumin, alpha-1-antiproteinase, transthyretin and transferrin are identified by both methods. Vitamin D binding protein was clearly selected as appearing in 2-DE. It was also seen to appear using the LCMS/MS approach with a peptide difference of 10, but the p value (p = 0.1) was lower than the limit we chose. This modulation was confirmed using an ELISA kit reacting with human protein, but the signal was very faint due to weak reactivity of the polyclonal antibody with the rat protein (data not shown). The SDS-PAGE-LC-MS/MS technique detected 9 more proteins than the 2-DE approach. We noticed that of those proteins, only two proteins (gamma-glutamyl hydrolase and pancreatic alpha-amylase) with pI higher than 7 are not technically detectable in 2-DE using a 4–7 pH gradient. The SDS-PAGELC-MS/MS is thus a sensitive technique and is advantageous for differential analysis, particularly given that only a very small amount of sample is required compared with 2-DE (five times less). The presence of several charge isoforms, particularly for albumin and alpha 1 antiproteinase in 2-DE gels, indicates post-translational modifications; these variations cannot be detected in our SDS-PAGELC-MS/MS which shows a global amount of each protein (through the

Table 2 Identification of 2-DE differentially expressed proteins in urine from rats treated with uranium Spot numbera

Ratiob

Image analysis p valueb

Protein identity

Name

Acc. no.

Sequence coverage %c

Matching peptidesc

63 64 65 68 70 74 76 38 41 84 93 101 110 111 123 59 104 105 69

1,6 6,4 3,5 3,4 4,1 3,5 7,9 5,7 2,5 ++ ++ 2,2 1,7 2,4 ++ 3,5 9,8 ++ 5,3

p b 0.025 p b 0.0005 p b 0.005 p b 0.001 p b 0.005 p b 0.001 p b 0.005 p b 0.001 p b 0.005 p b 0.01 p b 0.0005 p b 0.005 p b 0.005 p b 0.005 p b 0.005 p b 0.005 p b 0.005 p b 0.025 p b 0.001

Alpha-1-antiproteinase

A1AT_RAT

P17475

Serum albumin

ALBU_RAT

P02770

Serotransferrin Transthyretin

TRFE_RAT TTHY_RAT

P12346 P02767

Vitamin D binding protein

VTDB_RAT

P04276

20 29 50 28 19 32 19 29 22 25 23 18 22 14 20 13 26 17 28

6 9⁎⁎ 19⁎ 11 7 11 6⁎⁎ 12⁎⁎ 8 13 13 10⁎ 14⁎⁎ 9 11 11 3⁎⁎⁎ 2⁎⁎⁎ 10

a

The spot number corresponds to the protein spot number in Fig. 2. b Differential analysis was performed using statistical tools included in ImageMaster software as described in Material and methods. The p value was calculated by a t-test. ++ indicates a spot only found in gels performed with urine from uranium treated rats. c The percentage of amino acid sequence coverage and the number of matching peptides are given by MASCOT for the identified protein. Protein identification corresponded to p b 0.05 for the Mowse score and mass error ≤100 ppm. ⁎ : mass error ≤150 ppm. ⁎⁎ : mass error ≤200 ppm. ⁎⁎⁎ : identification performed by LC-MS/MS.

888

V. Malard et al. / Biochimica et Biophysica Acta 1794 (2009) 882–891

Fig. 3. 2-DE analysis: spots expressed differentially under uranium exposure close-up of details of modulated spots, selected by image analysis, corresponding to U-regulated proteins. 250 μg of concentrated urinary proteins were separated by 2-DE (first dimension, 18 cm 4–7 IPG strips, second dimension 12% SDS PAGE) and visualised by silver staining. Spot numbering refers to Table 2 which reports protein identification by MALDI-TOF analysis.

total peptide number). Protein fragmentation is detected by both techniques, although it is easier to detect using 2-DE. EGF fell steeply under uranium stress, with mean total peptide hits varying from 118 to 11. This protein variation was not detected on 2-DE gels, either because the MW (124 kDa) is too high to obtain good spot resolution, or because it is a membrane protein. Thus, we can say that even if SDSPAGE-LC-MS/MS allowed the identification of more proteins, these techniques are complementary. 3.2.5. Western blot analysis Western blots were performed to confirm the variation of some specific proteins. From the SDS-PAGE-LC-MS/MS data (Table 3), albumin and EGF were considerably modulated, while for ceruloplasmin, expressed at a lower level, there was less difference (33 peptide Table 3 Identification of differentially expressed proteins in urine from rats treated with uranium, determined by SDS-PAGE-LC-MS/MS Protein identity

Name

Acc. no.

p value

Ratio Peptide (U+ / U−) difference

Pro-epidermal growth factor Ceruloplasmin Dipeptidyl-peptidase 2 Serum albumin Alpha-1-antiproteinase Transthyretin Haptoglobin Contrapsin-like protease inhibitor 3 Serotransferrin Gamma-glutamyl hydrolase Nerve growth factor gamma chain Pancreatic alpha-amylase Epithelial-cadherin

EGF_RAT CERU_RAT DPP2_RAT ALBU_RAT A1AT_RAT TTHY_RAT HPT_RAT CPI3_RAT

P07522 P13635 Q9EPB1 P02770 P17475 P02767 P05544 P05544

b0,001 b0,001 0,002 0,005 0,007 0,007 0,008 0,019

0,10 11,50 0,04 2,30 3,00 21,97 5,26 0,51

−320 33 − 25 966 91 20 12 − 96

TRFE_RAT GGH_RAT KLK1_RAT

P12346 Q62867 P00758

0,020 0,024 0,032

2,87 0,09 0,58

209 − 12 − 17

AMYP_RAT P00689 CADH1_RAT Q9R0T4

0,048 0,050

0,28 0,25

− 58 − 27

U+ means uranium (5 mg/kg), U− means control.

counts) even if p b 0.001 was highly significant. We chose EGF and ceruloplasmin because they only appeared to be modulated in SDSPAGE-LC-MS/MS. We tested the level of these three proteins on 5 control rats, 5 rats exposed to 2.5 mg/kg of uranium and 5 rats exposed to 5 mg/kg, to evaluate the dose-effect relationship. Fig. 4 shows the corresponding results that fully supported the profiling results (p b 0.05) with a proportional dose effect for albumin and ceruloplasmin modulation. Their modulation is observed at a dose of 2.5 mg/kg, for which neither BUN nor creatinine was modified (Fig. 2). 4. Discussion Adachi et al. published the most complete proteome list of human urine ever reported [31]. We compared our list of 102 different proteins with their results. They observed proteins with a high molecular weight, as 375 of the 1543 reported proteins had MW higher than 70 kDa. Out of the 26 proteins with a high molecular weight observed in our rat proteomic data, 24 are shared with the human urinary proteome [31]. The human data set revealed the presence of extracellular proteins, lysosomal proteins, membrane transporters possibly excreted through the process of exosome formation; this may explain the presence of high molecular weight proteins. Strikingly, in this human urine data set, nearly half of the annotated proteins were membrane proteins according to Gene Ontology analysis. We used DAVID software (http://david.abcc. ncifcrf.gov/) to extract information about GO Cellular component classification, and found 23% of proteins described by the term “plasma membrane”. Although lower than that obtained by Adachi et al., this proportion corroborates their results. We also globally compared the two protein lists and found a total of 56 identical proteins out of 102. This overlap of 55% is quite high and gives us confidence concerning our identifications. The rat urinary proteome has already been described in a previous study based on a classical 2DE approach, describing 57 different proteins [20]. Only nine proteins

V. Malard et al. / Biochimica et Biophysica Acta 1794 (2009) 882–891

889

Fig. 4. Western blot confirmation of protein modulation EGF, ceruloplasmin and albumin were assayed by western blot using urine from five control rats, five rats exposed to 2.5 mg/kg uranium and five exposed to 5 mg/kg. The mean +/− SD band volumes for each group were determined using Image master. The results were respectively, for EGF: 1614 ± 449 for the control group, 493 ± 347 for the group exposed to 2.5 mg/kg and 927 ± 354 for the group of rats exposed to 5 mg/kg. The corresponding p values are shown on the graph. The results for albumin were: 427 ± 452, 1175 ± 277 and 1799 ± 432. The results for ceruloplasmin were: 0, 82 ± 37 and 507 ± 243. These results are representative of two or three experiments.

were found to be in common between our list and this study. This low redundancy can be explained by differences in urine collection protocols, different sample preparation, different fractionation techniques and also different analysis techniques. Lee et al. recently noticed temporal variations in the urinary proteome during rat kidney maturation. They identified 41 different proteins in adult rat urine, corresponding to 24 proteins with a Uniprot identification number. 14 of these proteins were on our list. Considering the high number of proteins in normal rat urine, these lists are complementary, since they each probably describe only part of the whole proteome. All the data obtained here, therefore, expand the rat urinary proteome, and form the basis for the following toxicoproteomic report and further studies using this biological fluid. Sun et al. [3,4] studied the kinetics of acute renal failure induced in rats following intravenous uranyl nitrate exposure (5 mg/kg). They noticed an increase in serum creatinine at day 3 with a peak at day 7. The initial lesions of necrosis were observed at day 2 with maximal tubular necrosis at days 4–5. More recently, Cheng et al. [29] used a mouse model of uranyl nitrate-induced acute renal failure to study the expression of calcium-binding proteins during injury. They observed an increase in both serum creatinine and BUN after 3 days exposure to 3.3 mg/kg uranyl nitrate in association with tubular necrosis. Tubular necrosis was also observed in rat kidneys 48 h after U (uranyl acetate dehydrate) exposure at 5 mg/mL associated with a rise in serum creatinine (fold 5) and urea (fold 4). According to previous studies, we exposed rats intravenously to 2.5 and 5 mg/kg uranium and collected blood and urine 24 h later. Serum creatinine (sCr) and Blood-Urea Nitrogen (BUN) were not modified in the 2.5 mg/kg group (Fig. 2) and increased slightly for the 5 mg/kg treatment (fold 1.5 and 1.4, p b 0.05). This does not indicate declared renal failure [32] but is consistent with previous work [3] where noticeable elevation was seen later. Urine is a suitable material for the diagnosis of several diseases, particularly in the event of kidney failure. One study involving an animal model used a gel-based proteomics approach with urine to improve understanding of the pathophysiology and define biomarker candidates for human membranous nephropathy in passive Heymann nephritis, a glomerular disease. A total of 37 differentially expressed proteins were found at 6 different time-points from 10 to 50 days [33]. Comparison with our study shows that we identified fewer modulations but we focused on the early response, 24 h instead of 10 days and more. Nevertheless 6 proteins from our study were found to be modulated in the same way (serum albumin, Alpha-1-antiproteinase, transthyretin, haptoglobin, serotransferrin and epithelial-cadherin). Albumin has already been shown to be modulated in urine in association with uranium injury. Mao et al. [34] investigated the association between uranium concentration in drinking water and microalbuminuria, a sensitive biological indicator of renal dysfunc-

tion. Linear regression analysis revealed a significant association between the uranium cumulative exposure index and albumin. Our results confirm these data. On the other hand, other authors have reported that Uranium exposure is not linked to creatinine clearance or urinary albumin, but in this study [35], uranium intake from drinking water was very low. Transthyretin, that we see induced, has recently been described as a biomarker for gentamicin-induced nephrotoxicity in rats, using imaging mass spectrometry [36]. Cadherins are type I membrane, calcium-dependent, cell adhesion molecules interacting with glomerular permeability key proteins. Down-regulation of cadherins is associated with glomerular permeability defects [33], and this also occurred in our rat model. Alpha-1antiproteinase (A1AT) is a member of the serine protease inhibitor family. It is one of the most abundant proteins in the urine of proteinuria patients, and has been suggested as a marker for glomerulopathy. We identified an increase in urinary A1AT excretion, which corroborates previous findings [37,38]. Our results show that 24 h after acute uranium exposure, several urinary proteins reflect glomerular damage. Several studies have demonstrated the important role of growth factors, particularly EGF and transforming growth factor alpha, in cellular growth following renal damage. EGF is mainly synthesized in mature kidneys. Many studies indicate that urinary EGF concentration falls significantly in patients with acute and chronic renal failure. Reduction in EGF has been reported previously in the distal nephron in cisplatin, gentamycin and ischemia-induced acute renal failure models [39–41]. To elucidate the pathophysiological roles of the changes in the distal nephron in uranium-induced acute renal failure, Sun et al. [3] investigated the relationship between changes in distal nephron constituent molecules and proximal tubule damage and repair in intravenous uranium-treated rats. Immunostaining for EGF, which was constitutively expressed in the tubule, diminished significantly as early as day 2, when proximal tubule regeneration became evident, and remained below normal levels until day 21. Given that EGF gene expression in the mature kidney is considered to be associated with functional differentiation, rather than proliferation, EGF reduction during acute renal failure could be an initiating factor in tubule regeneration. Our results confirm and complete previous results, showing, for the first time, a steep fall in urinary EGF as a result of heavy metal stress. Vitamin D-binding protein (DBP) binds, transports and activates vitamin D, which plays a major role in calcium homeostasis and bone turnover. As the complex of 25(OH)D(3) and vitamin D-binding protein is endocytosed via megalin into proximal tubule cells where activation of the vitamin D takes place, elevated urinary levels of vitamin D-binding protein may be linked to renal tubular dysfunction [42]. The presence of vitamin D-binding protein in urine following U

890

V. Malard et al. / Biochimica et Biophysica Acta 1794 (2009) 882–891

stress seems to indicate tubular dysfunction. Similar results have been observed in urine from the inhabitants of a Cd-polluted area in Japan in association with tubular dysfunction [42]. These results show that 24 h after acute uranium exposure, several urinary proteins reflect tubular damage. Nerve growth factor gamma chain or kallikrein-1 is a serine proteinase present in many tissues, including the kidney connecting tubule and cortical collecting tubule and is released in the urine and the peritubular interstitium. The renal kallikrein–kinin system is believed to operate in concert with the renin–angiotensin system to regulate physiologically the distribution of renal blood flow and the metabolism of water and electrolytes [43]. Reduced renal kallikrein excretion has been documented in patients with mild renal disease and more markedly reduced in patients with severe renal failure [44]. The reduction of the urinary Nerve growth factor gamma chain that we observed after uranium contamination seems to be associated with renal failure. Some other proteins, which are found with decreased levels, have never been described as associated with renal failure. Pancreatic alpha-amylase is a secreted protein belonging to the glycosyl hydrolase family which binds calcium. Dipeptidyl-peptidase II is predominantly expressed in the kidney and plays an important role in the degradation of some oligopeptides, but its definite function in peptide and protein breakdown in the proximal tubule is still unknown [45]. These proteins are both found in normal human urine [31]. These proteins are not known to be modulated either by metal exposure or in urine after nephrotoxic stress and constitute a part of the urinary signature of uranium toxicity. Although it has never been described as modulated in urine in association with acute renal failure, another protein is found at an increased level. Ceruloplasmin is a plasma protein which binds up to 95% of circulating copper, and has several possible functions in copper transport, organic amine oxidation, iron (II) oxidation and regulation of cellular iron levels, radical scavenging and other antioxidant processes. A relationship between ceruloplasmin and uranium has recently been demonstrated. A sensitive in vitro procedure, involving a combination of bidimensional chromatography with time-resolved fluorescence, coupled with proteomic analysis, identified uraniumbinding proteins in human serum fractions [46]. Ten novel targets were validated using purified proteins. Of these, ceruloplasmin displayed the ability to bind two uraniums per polypeptide. In another study, the potential role of changes in metal-binding proteins was examined in cisplatin-induced nephrotoxicity [47]. Increased serum blood urea, nitrogen and creatinine were observed, but no change was seen in the distribution of metal-binding proteins (transferrin, ferritin, ceruloplasmin, and metallothionein) evaluated by immunohistochemical staining. Ceruloplasmin has not been described as associated with metal stress nor found modulated in urine after such stress. Interestingly it binds to uranium in human serum and we found it increased in a dose-dependent manner in urine from rats exposed to uranium. Recently, ceruloplasmin mRNA levels were found dramatically augmented in rat kidneys exposed for 9 months to 40 mg/L of depleted uranium in drinking water [48], indicating a role of ceruloplasmin in case of uranium contamination. Since our results showed a strong increase in ceruloplasmin in urine, we can hypothesize that ceruloplasmin could be linked to uranium excretion and could be a biomarker of uranium exposure. Given that a key issue in monitoring occupational exposure to uranium is the availability of adequate biomarkers, ceruloplasmin is a worthy candidate for further study. In this study, we obtained a proteome data set of normal rat urine, identifying a total of 102 different proteins. Our analysis provides the largest set of proteins present in normal rat urine. We observed consistent differences in urinary protein profiles of rats treated with an acute dose of uranium, using 2-DE and SDS-PAGE-LC-based proteomic strategies. To our knowledge, this is the first study using

this latter technique to analyse a toxicological signature in urine, giving a descriptive and semi-quantitative analysis of urinary proteome modification. We have highlighted protein modulation before the appearance of biochemical signs of renal failure. Through our analysis, we observed the signature of both glomerular and tubular damage. We also identified proteins never associated with renal damage or metal stress that could be interesting biomarker candidates. Urinary proteomics is thus a valuable tool to profile noninvasively uranium toxicity and could be very useful in follow-up in case of accidental exposure to uranium. We are currently evaluating the sensitivity of this technique in case of lower level contamination. Acknowledgements We are grateful to Béatrice LE GALL (CEA/DSV/iRCM/LRT) for help in designing and performing animal experiments. We thank Jérome GARIN and Christophe BRULEY (CEA/DSV/iRTSV/EDyP, Grenoble, France) for providing us with IRMA software, Gilles IMBERT and Olivier PIBLE for bioinformatic support, Jean ARMENGAUD and Elisabeth DARROUZET for critical review of this manuscript and for helpful discussions. This work was supported by the CEA (Commissariat à l'Energie Atomique) a French government-funded technological research organisation. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.bbapap.2009.01.010. References [1] R.W. Leggett, The behavior and chemical toxicity of U in the kidney: a reassessment, Health Phys. 57 (1989) 365–383. [2] D.J. Sanchez, M. Belles, M.L. Albina, J.J. Sirvent, J.L. Domingo, Nephrotoxicity of simultaneous exposure to mercury and uranium in comparison to individual effects of these metals in rats, Biol. Trace Elem. Res. 84 (2001) 139–154. [3] D.F. Sun, Y. Fujigaki, T. Fujimoto, T. Goto, K. Yonemura, A. Hishida, Relation of distal nephron changes to proximal tubular damage in uranyl acetate-induced acute renal failure in rats, Am. J. Nephrol. 22 (2002) 405–416. [4] D.F. Sun, Y. Fujigaki, T. Fujimoto, K. Yonemura, A. Hishida, Possible involvement of myofibroblasts in cellular recovery of uranyl acetate-induced acute renal failure in rats, Am. J. Pathol. 157 (2000) 1321–1335. [5] M. Taulan, F. Paquet, A. Argiles, J. Demaille, M.C. Romey, Comprehensive analysis of the renal transcriptional response to acute uranyl nitrate exposure, BMC Genomics 7 (2006) 2. [6] E. Craft, A. Abu-Qare, M. Flaherty, M. Garofolo, H. Rincavage, M. Abou-Donia, Depleted and natural uranium: chemistry and toxicological effects, J. Toxicol. Environ. Health, B. Crit. Rev. 7 (2004) 297–317. [7] P.H.S. ATSDR - U.S. Department of Health and Human Services, Toxicological profile for uranium - http://www.atsdr.cdc.gov/toxprofiles/tp150.html, Atlanta, GA, 1999. [8] K.S. Squibb, R.W. Leggett, M.A. McDiarmid, Prediction of renal concentrations of depleted uranium and radiation dose in Gulf War veterans with embedded shrapnel, Health Phys. 89 (2005) 267–273. [9] C.G. Duarte, H.G. Preuss, Assessment of renal function — glomerular and tubular, Clin. Lab. Med. 13 (1993) 33–52. [10] M.L. Zamora, B.L. Tracy, J.M. Zielinski, D.P. Meyerhof, M.A. Moss, Chronic ingestion of uranium in drinking water: a study of kidney bioeffects in humans, Toxicol. Sci. 43 (1998) 68–77. [11] I. Rehman, A.R. Azzouzi, J.W. Catto, S. Allen, S.S. Cross, K. Feeley, M. Meuth, F.C. Hamdy, Proteomic analysis of voided urine after prostatic massage from patients with prostate cancer: a pilot study, Urology 64 (2004) 1238–1243. [12] J.E. Celis, H. Wolf, M. Ostergaard, Bladder squamous cell carcinoma biomarkers derived from proteomics, Electrophoresis 21 (2000) 2115–2121. [13] D. Theodorescu, S. Wittke, M.M. Ross, M. Walden, M. Conaway, I. Just, H. Mischak, H.F. Frierson, Discovery and validation of new protein biomarkers for urothelial cancer: a prospective analysis, Lancet Oncol. 7 (2006) 230–240. [14] P.R. Cutillas, R.J. Chalkley, K.C. Hansen, R. Cramer, A.G. Norden, M.D. Waterfield, A.L. Burlingame, R.J. Unwin, The urinary proteome in Fanconi syndrome implies specificity in the reabsorption of proteins by renal proximal tubule cells, Am. J. Physiol., Renal. Physiol. 287 (2004) F353–364. [15] Y.F. Zhang, D.L. Wu, M. Guan, W.W. Liu, Z. Wu, Y.M. Chen, W.Z. Zhang, Y. Lu, Tree analysis of mass spectral urine profiles discriminates transitional cell carcinoma of the bladder from noncancer patient, Clin. Biochem. 37 (2004) 772–779. [16] M. Meier, T. Kaiser, A. Herrmann, S. Knueppel, M. Hillmann, P. Koester, T. Danne, H. Haller, D. Fliser, H. Mischak, Identification of urinary protein pattern in type 1

V. Malard et al. / Biochimica et Biophysica Acta 1794 (2009) 882–891

[17]

[18]

[19]

[20] [21]

[22]

[23]

[24]

[25] [26] [27]

[28]

[29]

[30]

[31]

[32]

diabetic adolescents with early diabetic nephropathy by a novel combined proteome analysis, J. Diabetes Complicat. 19 (2005) 223–232. S. Wittke, M. Haubitz, M. Walden, F. Rohde, A. Schwarz, M. Mengel, H. Mischak, H. Haller, W. Gwinner, Detection of acute tubulointerstitial rejection by proteomic analysis of urinary samples in renal transplant recipients, Am. J. Transplant. 5 (2005) 2479–2488. S. Decramer, S. Wittke, H. Mischak, P. Zurbig, M. Walden, F. Bouissou, J.L. Bascands, J.P. Schanstra, Predicting the clinical outcome of congenital unilateral ureteropelvic junction obstruction in newborn by urinary proteome analysis, Nat. Med. 12 (2006) 398–400. A. Vlahou, J. Schanstra, J. Frokiaer, M. El Nahas, G. Spasovski, H. Mischak, B. Domon, G. Allmaier, E. Bongcam-Rudloff, T. Attwood, Establishment of a European Network for Urine and Kidney Proteomics, J. Proteomics 71 (2008) 490–492. V. Thongboonkerd, J.B. Klein, J.M. Arthur, Proteomic identification of a large complement of rat urinary proteins, Nephron, Exp. Nephrol. 95 (2003) e69–e78. R.S. Lee, F. Monigatti, M. Lutchman, T. Patterson, B. Budnik, J.A. Steen, M.R. Freeman, H. Steen, Temporal variations of the postnatal rat urinary proteome as a reflection of systemic maturation, Proteomics 8 (2008) 1097–1112. L.N. Mueller, M.Y. Brusniak, D.R. Mani, R. Aebersold, An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data, J. Proteome Res. 7 (2008) 51–61. D.N. Perkins, D.J. Pappin, D.M. Creasy, J.S. Cottrell, Probability-based protein identification by searching sequence databases using mass spectrometry data, Electrophoresis 20 (1999) 3551–3567. Y. Wang, Y. Chen, Y. Zhang, S. Wu, S. Ma, S. Hu, L. Zhang, C. Shao, M. Li, Y. Gao, Differential ConA-enriched urinary proteome in rat experimental glomerular diseases, Biochem. Biophys. Res. Commun. 371 (2008) 385–390. J.X. Pang, N. Ginanni, A.R. Dongre, S.A. Hefta, G.J. Opitek, Biomarker discovery in urine by proteomics, J. Proteome Res. 1 (2002) 161–169. J. Gao, G.J. Opiteck, M.S. Friedrichs, A.R. Dongre, S.A. Hefta, Changes in the protein expression of yeast as a function of carbon source, J. Proteome Res. 2 (2003) 643–649. J. Gao, M.S. Friedrichs, A.R. Dongre, G.J. Opiteck, Guidelines for the routine application of the peptide hits technique, J. Am. Soc. Mass Spectrom. 16 (2005) 1231–1238. E.M. Fach, L.A. Garulacan, J. Gao, Q. Xiao, S.M. Storm, Y.P. Dubaquie, S.A. Hefta, G.J. Opiteck, In vitro biomarker discovery for atherosclerosis by proteomics, Mol. Cell Proteomics 3 (2004) 1200–1210. C.W. Cheng, A. Rifai, S.M. Ka, H.A. Shui, Y.F. Lin, W.H. Lee, A. Chen, Calcium-binding proteins annexin A2 and S100A6 are sensors of tubular injury and recovery in acute renal failure, Kidney Int. 68 (2005) 2694–2703. M. Belles, V. Linares, M. Luisa Albina, J. Sirvent, D.J. Sanchez, J.L. Domingo, Melatonin reduces uranium-induced nephrotoxicity in rats, J. Pineal Res. 43 (2007) 87–95. J. Adachi, C. Kumar, Y. Zhang, J.V. Olsen, M. Mann, The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins, Genome Biol. 7 (2006) R80. R. Bellomo, C. Ronco, J.A. Kellum, R.L. Mehta, P. Palevsky, Acute renal failure — definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group, Crit. Care 8 (2004) R204–R212.

891

[33] H.H. Ngai, W.H. Sit, P.P. Jiang, R.J. Xu, J.M. Wan, V. Thongboonkerd, Serial changes in urinary proteome profile of membranous nephropathy: implications for pathophysiology and biomarker discovery, J. Proteome Res. 5 (2006) 3038–3047. [34] Y. Mao, M. Desmeules, D. Schaubel, D. Berube, R. Dyck, D. Brule, B. Thomas, Inorganic components of drinking water and microalbuminuria, Environ. Res. 71 (1995) 135–140. [35] P. Kurttio, A. Auvinen, L. Salonen, H. Saha, J. Pekkanen, I. Makelainen, S.B. Vaisanen, I.M. Penttila, H. Komulainen, Renal effects of uranium in drinking water, Environ. Health Perspect. 110 (2002) 337–342. [36] H. Meistermann, J.L. Norris, H.R. Aerni, D.S. Cornett, A. Friedlein, A.R. Erskine, A. Augustin, M.C. De Vera Mudry, S. Ruepp, L. Suter, H. Langen, R.M. Caprioli, A. Ducret, Biomarker discovery by imaging mass spectrometry: transthyretin is a biomarker for gentamicin-induced nephrotoxicity in rat, Mol. Cell Proteomics. 5 (2006) 1876–1886. [37] K. Sharma, S. Lee, S. Han, S. Lee, B. Francos, P. McCue, R. Wassell, M.A. Shaw, S.P. RamachandraRao, Two-dimensional fluorescence difference gel electrophoresis analysis of the urine proteome in human diabetic nephropathy, Proteomics 5 (2005) 2648–2655. [38] V. Thongboonkerd, J.B. Klein, A.W. Jevans, K.R. McLeish, Urinary proteomics and biomarker discovery for glomerular diseases, Contrib. Nephrol. 141 (2004) 292–307. [39] E.J. Nouwen, W.A. Verstrepen, N. Buyssens, M.Q. Zhu, M.E. De Broe, Hyperplasia, hypertrophy, and phenotypic alterations in the distal nephron after acute proximal tubular injury in the rat, Lab. Invest. 70 (1994) 479–493. [40] I. Leonard, J. Zanen, D. Nonclercq, G. Toubeau, J.A. Heuson-Stiennon, J.F. Beckers, P. Falmagne, R.P. Schaudies, G. Laurent, Modification of immunoreactive EGF and EGF receptor after acute tubular necrosis induced by tobramycin or cisplatin, Ren. Fail. 16 (1994) 583–608. [41] G. Toubeau, D. Nonclercq, J. Zanen, G. Laurent, P.R. Schaudies, J.A. HeusonStiennon, Renal tissue expression of EGF and EGF receptor after ischaemic tubular injury: an immunohistochemical study, Exp. Nephrol. 2 (1994) 229–239. [42] M. Uchida, H. Teranishi, K. Aoshima, T. Katoh, M. Kasuya, H. Inadera, Elevated urinary levels of vitamin D-binding protein in the inhabitants of a cadmium polluted area, Jinzu River basin, Japan, Tohoku J. Exp. Med. 211 (2007) 269–274. [43] O.A. Carretero, A.G. Scicli, in: J.H. Laragh, B.M. Brenner (Eds.), The kallikrein-kinin system as a regulator of cardiovascular and renal function., vol. Raven Press, New York, 1995, pp. 983–999. [44] S. Naicker, S. Naidoo, R. Ramsaroop, D. Moodley, K. Bhoola, Tissue kallikrein and kinins in renal disease, Immunopharmacology 44 (1999) 183–192. [45] M.B. Maes, S. Scharpe, I. De Meester, Dipeptidyl peptidase II (DPPII), a review, Clin. Chim. Acta 380 (2007) 31–49. [46] C. Vidaud, A. Dedieu, C. Basset, S. Plantevin, I. Dany, O. Pible, E. Quemeneur, Screening of human serum proteins for uranium binding, Chem. Res. Toxicol. 18 (2005) 946–953. [47] J. Goudie, M. Chandra, G.D. Lawrence, P. Williams, Assessment of the possible role of iron and copper in cisplatin-induced nephrotoxicity in the rat, Res. Commun. Chem. Pathol. Pharmacol. 83 (1994) 33–49. [48] H. Berradi, J.M. Bertho, N. Dudoignon, A. Mazur, L. Grandcolas, C. Baudelin, S. Grison, P. Voisin, P. Gourmelon, I. Dublineau, Renal anemia induced by chronic ingestion of depleted uranium in rats, Toxicol. Sci. 103 (2008) 397–408.