A discovery-phase urine proteomics investigation in type 1 diabetes

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Acta Diabetol (2012) 49:453–464 DOI 10.1007/s00592-012-0407-0

ORIGINAL ARTICLE

A discovery-phase urine proteomics investigation in type 1 diabetes A. Soggiu • C. Piras • L. Bonizzi • H. A. Hussein S. Pisanu • P. Roncada



Received: 5 March 2012 / Accepted: 22 May 2012 / Published online: 8 June 2012 Ó Springer-Verlag 2012

Abstract Diabetes is a chronic metabolic disease which can lead to serious health problems particularly in and to the development of cardiovascular and renal complications. The aim of this study is to possibly identify distinctive molecular features in urine samples which might correlate to the progression and complications of type 1 diabetes. Diabetic patients with normo- and micro-albuminuria have been analyzed and compared to a group of control subjects. Urine proteins of control and type 1 diabetes subjects were investigated in their proteome profiles, using high-resolution two-dimensional gel electrophoresis separation and protein identifications by MALDI–TOF–MS and LC–MS/ MS analysis. Proteomics analysis highlighted differential expression of several proteins between control and type 1 diabetes subjects. In particular, five proteins were found to be down-regulated and four proteins up-regulated. Lower protein representations in diabetic subjects were associated with Tamm–Horsfall urinary glycoprotein, apolipoprotein A-I, apolipoprotein E, a2-thiol proteinase inhibitor, and

human complement regulatory protein CD59, while higher protein representations were found for a-1-microglobulin, zinc-a2 glycoprotein, a-1B glycoprotein, and retinol-binding protein 4. These differences were maintained comparing control subjects with type 1 diabetes normo-albuminuric and micro-albuminuric subjects. Furthermore, these proteins are correlated to glycosylated hemoglobin and microalbuminuria, confirming their role in diabetic pathology. This study gives new insights on potential molecular mechanisms associated with the complications of type 1 diabetic disease providing evidences of urine proteins potentially exploitable as putative prognostic biomarkers. Keywords Type 1 diabetes  Urine proteomics  HbA1c  Microalbuminuria  Biomarkers  Diabetic nephropathy Abbreviations THP Apo A-1 Apo E HMWK

Communicated by Antonio Secchi.

CD59 A. Soggiu  L. Bonizzi  H. A. Hussein Dipartimento di Patologia Animale, Igiene e Sanita` Pubblica Veterinaria, Facolta` di Medicina Veterinaria, Universita` Degli Studi di Milano, Milan, Italy C. Piras Dipartimento di Scienze Zootecniche, Facolta` di Agraria, Universita` Degli Studi di Sassari, Sassari, Italy S. Pisanu Porto Conte Ricerche Srl, Tramariglio, Alghero, SS, Italy P. Roncada (&) Istituto Sperimentale Italiano ‘‘L. Spallanzani’’, Milan, Italy e-mail: [email protected]

AMBP ZA2G A1BG RBP4 HbA1c IDDM NIDDM MALDI–TOF LC–MS/MS

Tamm–Horsfall urinary glycoprotein Apolipoprotein A-I Apolipoprotein E Kininogen-1 or a2-thiol proteinase inhibitor Human complement regulatory protein CD59 a-1-Microglobulin Zinc-a2 glycoprotein a-1B Glycoprotein Plasma retinol-binding protein Glycosylated hemoglobin Insulin-dependent diabetes mellitus Non-insulin-dependent diabetes mellitus Matrix-assisted laser desorption ionization–time of flight Liquid chromatography tandem mass spectrometry

123

454

DN T1D 2-DE T2D

Acta Diabetol (2012) 49:453–464

Diabetes nephropathy Type 1 diabetes Two-dimensional electrophoresis Type 2 diabetes

Introduction Diabetes mellitus is an autoimmune disease characterized by T cell-mediated response that causes a selective and irreversible destruction of the insulin-producing b-cells in the pancreatic islets of Langerhans [1]. Type 1 diabetes is due to a complex network between the b-cell, immune system, and environment in genetically susceptible individuals [1]. Diabetes mellitus is characterized by absolute or relative deficiencies in insulin secretion and/or insulin action associated with chronic hyperglycemia and carbohydrates, lipids and proteins metabolism disorders [2]. Typically, this type of diabetes develops early in life, usually during childhood or adolescence with an incidence of exponential increase worldwide. Blood glucose can be kept under control using several types of insulin therapy such as multiple daily insulin injections (MDI), continuous subcutaneous insulin infusion (CSII), or conventional treatment. In pediatric T1D patients, it is important to determine the proper dose adjustment when changing from MDI to CSII [3]. Insulin therapy, especially in T1D patients switching to CSII, may increase levels of anti-insulin antibodies (IAs), but augmented levels of IAs were not associated with impaired glycemic control or increased hypoglycemic events [4]. When treating T1D patients, the type and the delivery of insulin used in CSII are also important. Shortacting insulin analogs [5] and dual wave bolus tools in CSII can improve metabolic control in diabetic patients [6]. Diabetes requires long-term medical attention because of many serious complications. Observations suggest that additional factors may be involved in the acceleration of diabetic complications. Obesity, for example, is associated with diabetic nephropathy (DN), but not with the severity of this complication [7]. In diabetes mellitus, the disorders of metabolism play a predominant role in diabetic complications. The hyperglycemia and metabolic deregulation found in diabetes often gives rise to health problems, which essentially can be divided into micro- and macroangiopathy [2]. Microangiopathy includes diabetic complications in small blood vessels affecting the eyes (retinopathy), kidneys (nephropathy, DN), and nerves (neuropathy). Diabetic patients with microvascular complications also show a strong association with cardiomyopathy [8]. It is crucial to detect diabetic late complications early, when they are present. A disease like cardiovascular autonomic neuropathy (CAN) that implies an increased risk of premature death

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is often asymptomatic. Simple function tests, in particular the Valsalva ratio, that are easy to carry out and not time consuming, showed a good predictive value [9]. Diabetic retinopathy (DR), which is a cause of blindness, is largely preventable when lesions such as microaneurysms (MA) are correctly recognized. A large population-based study in T2D showed that MA number and distribution is also directly correlated with levels of HbA1c, and the control of both events could be a valid tool for predicting progression and severity of DR [10]. Macroangiopathy includes severe complications like atherosclerosis, which appear as cardiovascular problems as well as autonomic disfunction pioglitazone, a glucose-lowering drug, provided, in a recent study, an improvement of cardiac sympathovagal balance in T2DM [11]. Also, oculomotor neuropathies like ophthalmoplegia showed an association with cardiovascular risk factors and metabolic syndrome [12]. Diabetes is also the major cause of end-stage kidney failure throughout the world [13]. DN accounts approximately for one-third of all cases of end-stage renal disease in the world [14]. Recently, proliferative DR has been shown as a predictive factor in DN development [15]. DN is clinically characterized by elevated blood pressure, increased urinary albumin excretion (UAE), and a relentless decline in renal function. In DN, microalbuminuria, which is the first clinical evidence of this complication, could predict future cardiovascular disease in diabetic patients [16]. Established DN is associated with well-characterized renal structural and functional changes, such as extracellular matrix accumulation, reduced filtration surface area, thickening of both glomerular and tubular basement membranes, and changes in arterioles and in the renal interstitium [17]. Compared to diabetic subjects without complications, DN patients showed a more severe oxidative stress with increased levels of the products of oxidative stress and lower levels of antioxidant enzymes [18]. The complexity of diabetes and its complications require broad-based, unbiased, and scientific approaches, such as genomics and proteomics, in order to understand the causes, prove the diagnosis, and to monitor disease progression. In recent years, transcriptomics approaches allow to initially identify important genes, which are either upregulated or down-regulated in diabetes disease [19]. However, a problem with gene products regulation at the mRNA and protein levels through several post-translational events is not easily accessible by transcriptomics. As a matter of fact, the changes in the mRNA levels also need to be evaluated in terms of changes to functional protein levels. The recent developments in the field of proteomics, and in particular urinary proteomics, could explain which proteins are implicated in the pathophysiology of the diseases [20, 21]. In recent years, proteomics has proved to be a powerful tool in biomedical experimental investigation

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and in laboratory medicine and will likely eventually provide us with a comprehensive knowledge of several proteins that can be found in the urine under many conditions. Urinary protein repertoire is considered to reflect both normal kidney filtration activity and specific kidney produced proteins [22–24]. It has been shown that around 70 % of the urinary proteome originates from the kidney and the urinary tract, whereas the other 30 % of the proteins detected in the urine are plasma proteins. Urinary proteins include glomerular filtrated plasma proteins, soluble proteins secreted by epithelial cells, solid phase elements of epithelial cells and other cell sources, and exosomes [25]. As normal urinary proteins generally reflect normal tubular physiology, reliable information regarding changes in the excretion of urinary proteins is essential to better understand tubular and glomerular responses to physiological stimuli. In this study, urinary proteome investigation has been pursued in order to evaluate the expression of proteins potentially involved in type 1 diabetes normo- and microalbuminuria. Several proteomic studies reported how renal and tubular damage in type 2 diabetes patients could affect the urine proteome [26, 27]. The aim of this investigation is to discover possible markers of diabetic disease using a proteomic approach to compare healthy urine samples and urine from patients with type 1 diabetes in the presence and absence of microalbuminuria. Proteomics is a relatively emerging science in the post-genomics era, offering the possibility to investigate and analyze simultaneously all the proteins that are in particular samples [20, 21]. Gel-based [28] and gel-free methods are the main tools for urine proteomics [29]. Currently, urine samples are one of the biological fluids used in clinical proteomics as they can be obtained in a non-invasive mode and in large quantities [30, 31]. Most of these studies apply to kidney disease and urinary tract, but recent data indicate that the analysis of

urinary proteome may also be a highly informative tool in the classification of non-urogenital diseases [31]. Despite these advances in the discovery of biomarkers, the contribution of urinary proteomics in understanding the pathophysiology of this disease is still limited, especially because of problems related to the lack of comprehensive characterization of biomarker protein sequences combining high-resolution intact protein separations with different mass spectrometry approaches such as MALDI–TOF–MS and LC–MS/MS. This study represents an attempt to overcome these issues [21–24, 32].

Methods Urine samples were collected from 10 healthy subjects and 20 patients with type 1 diabetes. Control subjects were chosen after a clinical check of renal function, blood pressure, microalbuminuria, urinary sediment, and absence of significant clinical events during last 12 months. The clinical characteristics of type 1 diabetes subjects are shown in Table 1, which includes sex, age, total cholesterol (mg/dl), LDL cholesterol level (mg/dl), HDL cholesterol level (mg/dl), triglyceride level (mg/dl), glycosylated hemoglobin (HbA1c, %), albuminuria (mg/24 h), and albumin–creatinine ratio. In the diabetic group, 16 subjects had normoalbuminuria (\30 mg/24 h) and 4 subjects had microalbuminuria (30–300 mg/24 h). Informed consent was obtained from patients according to the institutional review board guidelines for human subjects. Collection of urine The urine collection protocol used in this work is in agreement, with light modifications, to the ‘‘Collection of Urine Samples for Proteome Analysis’’ reported by the

Table 1 Clinical characteristics of control and type 1 diabetes subjects Control subjects

Type 1 diabetic subjects (total)

Type 1 diabetic subjects (normoalbuminuria)

Type 1 diabetic subjects (microalbuminuria)

n

10

20

16

4

Age (years)

35.62 ± 4.72

43.81 ± 10.07

44.85 ± 10.15

36.5 ± 7.78

Sex (% men)

50

60

54

50

Diabetes duration (years)



24.43 ± 8.65

25.28 ± 8.84

18.5 ± 4.95

Total cholesterol (mg/dl)

143.63 ± 18.71

178.86 ± 20.55

175.92 ± 19.85

198 ± 18.38

LDL cholesterol (mg/dl)

101.54 ± 22.62

111.14 ± 23.58

106.91 ± 21.72

136.5 ± 23.33

HDL cholesterol mg/dl)

42.67 ± 15.55

58.78 ± 18.58

59.5 ± 19.61

54.5 ± 14.84 48.5 ± 12.02

Triglyceride (mg/dl)

39.92 ± 12.67

47.06 ± 15.67

46.84 ± 16.55

HbA1c (%)

4.5 ± 1

8.09 ± 1.09

7.88 ± 0.98

9 ± 1.41

Albuminuria (mg/24 h)

8.5 ± 3

24.34 ± 39.68

9.06 ± 5.69

116 ± 22.63

Data are mean ± SD (standard deviation) unless otherwise indicated

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Human Urine and Kidney Proteome Project and European Kidney and Urine Proteomics COST Action (EuroKUP) networks (for more information, please visit: http://eurokup.org/node/129). Briefly, urine samples were collected in the morning into sterile polypropylene tubes from healthy individuals and insulin-dependent diabetic patients. Immediately after collection, urine samples were centrifuged at 1,000g for 10 min at 4 °C to remove cell debris and casts. A complete protease inhibitor tablet (Roche) was added to aliquots of urine samples (25 mL) to inhibit the activity of endogenous proteases present in the specimen, and then sodium azide (NaN3) was added to prevent bacterial growth. After this preliminary process, the samples were stored at -80 °C and thawed prior to use. Preparation of urine The ultrafiltration of urine (supernatant) was achieved by means of an Amicon Ultra-15 Centrifugal Device (cutoff 10,000 Da; Millipore) in order to reduce the initial volume of urine and to remove salts and low molecular weight contaminants by dialysis against ultrapure water. The ultrafiltration was conducted at 4,000g for 10 min at 4 °C. The Nuclease Mix (GE Healthcare) was added to concentrated supernatant for 1 h at room temperature to complete elimination of DNA and RNA. The sample was subjected to protein precipitation in a cold mixture of acetone and methanol (v/v ratio 8:1) for a final acetone concentration of 83.2 %, for removing lipids and salts and stored at -20 °C overnight. The precipitate was pelleted by centrifugation at 4,000g for 15 min at 4 °C, washed sequentially with 1 mL of acetone followed by methanol and then air-dried. The resulting pellet was solubilized in 2D sample buffer containing 7 M urea, 2 M thiourea, 4 % CHAPS, 1 % DTT, 15 mM Tris, 2 % ampholine 3.5-10 (1:10, w/v). Protein concentrations were determined using the 2D Quant Kit (GE Healthcare). Samples were stored at -80 °C prior to electrophoresis. 2-D gel electrophoresis Isoelectric focusing was carried out on 13-cm homemade IPG strips pH 4–8 (4 %T, 3 %C). The strip was rehydrated for 20 h with rehydration solution. Each sample was loaded with 300 lg of proteins with anodic cup loading. IEF then was performed at 20 °C by a series of increasing voltage ‘‘steps’’ from 50 to 3,500 V, until 80,000 Vhr in a Multiphor II Electrophoresis System (GE Healthcare). After the first dimension, immobilized pH gradient strips were equilibrated twice for 15 min under gentle stirring with a solution containing 6 M urea, 2 % SDS, 50 mM Tris–HCl pH 8.8 and 30 % glycerol. To the first equilibrium was added 1 % DTT and to the second 2.5 % iodoacetamide.

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The second dimension was performed by 8–15 % gradient gels. Gels were run in a SE 600S electrophoresis unit (Hoefer) in running buffer containing 25 mM Tris–HCl pH 8.3, 192 mM glycine, 0.1 % SDS. Electrophoretic run was performed by setting a current of 10 mA/gel for 15 min, followed by 20 mA/gel until end. After the runs, gels were stained by colloidal Coomassie Blue G-250 and destained with ultrapure water. Molecular masses were determined by running standard protein markers (Precision Plus Protein All Blue Standards, Biorad Laboratories) covering the range 10–250 kDa. The gel was scanned with Molecular Imager PharosFX Plus System (Biorad Laboratories). Image analysis was performed using Progenesis SameSpots (Nonlinear Dynamics). All imported images were processed to check image quality (saturation, dimension, background). The aligned images were then automatically analyzed using 2D analysis module for spot detection, background subtraction, normalization, and spot matching, while all spots were manually reviewed and validated to ensure proper detection and matching. Differentially expressed spots were identified by matrix-assisted laser desorption ionization-time of flight (MALDI–TOF) and LC–MS/MS. In situ digestion Differentially expressed proteins were excised from Colloidal Coomassie-stained gel and destained by sequential washes with 0.1 M NH4HCO3 pH 7.5 and acetonitrile. Samples were reduced by incubation with 50 ll of 10 mM DTT in 0.1 M NH4HCO3 buffer pH 7.5 and carboxyamidomethylated with 50 ll of 55 mM iodoacetamide in the same buffer. Enzymatic digestion was carried out with trypsin (12.5 ng/ll) in 10 mM ammonium bicarbonate pH 7.8 with 1 h of incubation at 4 °C. Trypsin solution was then removed, and a new aliquot of the digestion solution was added; samples were incubated for 16 h at 37 °C. A minimum reaction volume was used as to obtain the complete rehydration of gel. Peptides were then extracted by washing gel particles with 10 mM ammonium bicarbonate and 1 % formic acid in 50 % acetonitrile at room temperature. The resulting peptide mixtures were desalted using ZipTip C18 pipettes (Millipore), following the recommended purification procedure. MALDI–TOF mass spectrometry Positive Reflectron MALDI spectra were recorded on a Voyager DE STR instrument (Applied Biosystems, Framingham, MA, USA). The MALDI matrix was prepared by dissolving 10 mg of a-Cyano-4-hydroxycinnamic acid in 1 mL of acetonitrile/water (90:10 v/v). Typically, 1 ll of matrix was applied to the metallic sample plate, and 1 ll of

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analyte was then added. Acceleration and reflector voltages were set up as follows: target voltage at 20 kV, first grid at 95 % of target voltage, delayed extraction at 600 ns to obtain the best signal-to-noise ratio and the best possible isotopic resolution with multipoint external calibration using peptide mixture purchased from Applied Biosystems. Each spectrum represents the sum of 1,500 laser pulses from randomly chosen spots 9 sample position. Raw data were analyzed using the computer software provided by the manufacturers and are reported as monoisotopic masses. NanoLC mass spectrometry A mixture of peptide solution was analyzed by LC–MS analysis using a 4000Q-Trap (Applied Biosystems) coupled to an 1100 nano HPLC system (Agilent Technologies, Wilmington, DE, USA). Data were acquired and processed using Analyst software (Applied Biosystems). MASCOT analysis Spectral data were analyzed using Analyst software (version 1.4.1), and MS/MS centroid peak lists were generated using the MASCOT.dll script (version 1.6b9). MS/MS centroid peaks were threshold at 0.1 % of the base peak. MS/MS spectra having less than 10 peaks were rejected. MS/MS spectra were searched against Swiss Prot database using the upgrade licensed version of Mascot 2.1 (Matrix Science), after converting the acquired MS/MS spectra to mascot generic file format. The Mascot search parameters were: taxonomy ‘‘human’’; ‘‘trypsin’’ as enzyme allowing up to 2 missed cleavages, carbamidomethyl as fixed modification, oxidation of M, pyroGlu N-term Q, as variable modifications, 600 ppm MS/MS tolerance and 0.6 Da peptide tolerance and top 20 protein entries. Spectra with a MASCOT score\25 having low quality were rejected. The score used to evaluate quality of matches for MS/MS data was higher than 30. Statistical analysis Statistical analysis was performed by the Progenesis Stats module on the log-normalized volumes for all spots. Mann–Whitney test and one-way ANOVA were used to confirm the p value between different groups, p values under 0.05 were considered statistically significant. FDR (false discovery rate) and power analysis were also calculated, and q values \0.05 and power values [0.8 respectively were considered to be significant. Correlations between variables of interest were performed by a Pearson’s test for correlation. Values of p \ 0.05 were considered to be significant.

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Results Expression of urinary proteins from control and type 1 diabetes subjects has been compared using a proteomics approach. Table 1 shows clinical characteristics of 10 control subjects and 20 type 1 diabetic patients grouped in 16 type 1 diabetic subjects with normoalbuminuria and 4 type 1 diabetic subjects with microalbuminuria. A total of 30 samples were analyzed with 2D gel electrophoresis. Figure 1 shows a typical example of 2D gel profile. All gels showed approximately 160 protein spots. After Coomassie Blue staining, the differentially expressed spots were processed by MALDI–TOF–MS and LC–MS/MS analysis for identification. Table 2 lists the identified proteins with Uniprot accession numbers, theoretical molecular weights (Mr), isoelectric points (pI), and Mascot scores. Image analysis of 2D maps gives a protein profile associated with T1D and its complications. Image analysis highlights nine differentially expressed proteins (p \ 0.05) (FDR \ 0.05) between control and type 1 diabetic subjects, as reported in Figs. 2a and 3a. These differences were maintained when comparing control group with type 1 diabetes group in the presence of normoalbuminuria or microalbuminuria (Figs. 2b, 3b). Five proteins were down-regulated in a diabetic grouping in comparison to control group (Fig. 2a). These proteins were Tamm–Horsfall urinary glycoprotein (THP, 2.72-fold), apolipoprotein A-I (Apo A-1, 1.79-fold), apolipoprotein E (Apo E, 1.82-fold), a2-thiol proteinase inhibitor (HMWK, 1.77-fold), and human complement regulatory protein CD59 (CD59, 1.4-fold), as indicated in Table 3. Among the diabetic group, it has been observed that a major down-regulation of these proteins in the presence of microalbuminuria proves their positive correlation with the progression of disease (Fig. 2b). Four proteins were up-regulated in diabetic patients in comparison with the control group (Fig. 3a). Proteins up-regulated were a-1-microglobulin (AMBP, 1.62-fold), zinc-a2 glycoprotein (ZA2G, 3.04-fold), a-1B glycoprotein (A1BG, 3.92-fold), and plasma retinol-binding 4 (RBP4, 1.20-fold), as reported in Table 3. Also, albumin is overexpressed during microalbuminuria. However, FDR correction for this protein is not statistically significative (data not shown). Expression of these proteins increases in patients with microalbuminuria in comparison with subjects with normoalbuminuria (Fig. 3b), and the same trend has been observed for protein down-regulated in the presence of microalbuminuria. Tables 4 and 5 display the correlation between normalized expression levels of upand down-regulated proteins with two biochemical parameters: glycosylated hemoglobin (HbA1c, %) and albuminuria (mg/24 h). Among down-regulated proteins, THP and CD59 are inversely correlated with HbA1c, as

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Acta Diabetol (2012) 49:453–464 pI 4

8 8

KDa 200

2 116

1

97

4

3 6 5

66

7

9 55

11 12

45

13

13

15

29

AA

10 12

24

36

8

14

23

24

16

17 20

18

19 20

19

21

22

14.2 15 6.5

Fig. 1 Representative 2D PAGE map of human urine proteins after Coomassie Blue staining, as indicated in the experimental section. The numbers indicated on the gel correspond to proteins identities in Table 2

reported in Table 4. Similarly, THP, Apo A-I, and CD59 have an inverse correlation with albuminuria. As reported in Table 5, the levels of expression of ZA2G were positively correlated with HbA1c and albuminuria levels, while AMBP and RBP showed a positive correlation with excreted albumin levels. Correlation of proteins down- and up-regulated with glycosylated hemoglobin and albuminuria confirmed that these proteins may be implicated in diabetic pathology especially with the state and progress of diabetic complications (microalbuminuria).

Discussion This study highlighted a set of proteins differentially expressed in urine from control subjects and type 1 diabetic

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patients. These differences were also maintained by comparing control subjects to diabetes individuals for the presence of normoalbuminuria and microalbuminuria. Five urine proteins exhibited low relative abundance in diabetic disease with respect to the control subjects. These proteins down-regulated were Tamm–Horsfall urinary glycoprotein (THP), apolipoprotein A-I (Apo A-1), apolipoprotein E (Apo E), a2-thiol proteinase inhibitor (HMWK), and human complement regulatory protein CD59 (CD59). THP is a glycoprotein and is the most abundant protein in the urine under normal physiological conditions. THP has been suggested as a useful marker of renal damage and has been reported to be decreased in patients with type 1 diabetes [33] also in the presence of microalbuminuria [34]. THP downregulation as a biomarker of early development of renal disease in T1D has recently been reported.

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Table 2 Protein identifications by MALDI/MS and LC–MS/MS analysis 2-DE spot number

Protein name

Accession number

Exp. Mw (kDa)

Exp. pI

Mascot score

1

Tamm–Horsfall urinary glycoprotein

P07911

92

4.2

225

2

Ceruloplasmin

P00450

141

5.3

99

3

a-1B-Glycoprotein

P04217

77

5.25

235

4

Serotransfrerrin

P02787

80

6.75

187

5

Serum albumin

P02768

70

6

357

6

a2-Thiol proteinase inhibitor

P01042

65

4.25

69

7 8

Alpha-1-antitrypsin a-Amilase

P20848 P04746

63 60

5.1 6.75

373 358

9

Alpha-2-HS-glycoprotein

P02765

58

4.3

125

10

Gelsolin

P06396

50

5.25

99

11

Leucin-rich a-2-glycoprotein

P02750

52

4.4

208 114

12

Zinc-a-2 glycoprotein

P25311

42

5.1

13

Inter-a- trypsin inhibitor heavy chain H4

Q14624

35

5.3

200

14

a-Arrestin 1

P49407

32

5.4

137

15

a-1-Microglobulin

P02760

32

4.75

818

16

Apolipoprotein A

P02647

30

5.15

248

17

Immunoglobulin kappa light

P01834

29

6

203

18

Basement membrane-specific heparan sulfate proteoglycan

P98160

22

5.8

85

19

Human complement regulatory protein CD59

Q6FHM9

20

4.5

126

20

Plasma retinol-binding protein 4

P02753

22

5.2

190

21

Human lithostathine

P05451

20

5.2

85

22 23

Apolipoprotein E Complex-forming glycoprotein HC

P02649 P02649

21 33

5.5 5.1

150 168

24

Fibrinogen, alpha polypeptide isoform

P02649

38

4.75

170

Experimental data showed the decreased expression of THP in patients with T1D who developed either ERFD (early renal function decline) or MA (micro- or macroalbuminuria) over 6 years of follow-up [35]. This is the first study, to our knowledge, that shows, using a 2-DE/MS approach, a sensible decrease of THP in T1D diabetic patients, also in the presence of microalbuminuria. In particular, results showed that decrease of THP is negatively correlated with two important clinical parameters for diabetes disease: HbA1c and albuminuria. These correlations suggest that there is a dependence of THP levels with glycemic control and albumin excretion, as reported by other authors [36]. These evidences indicate also that the excretion of THP can be correlated with renal function as showed by other studies [36, 37]. Currently, THP seems to be a potential biomarker for early kidney dysfunctions in diabetes [35]. CD59 is a powerful inhibitor of the complement membrane attack complex (MAC). MAC is a circular polymer 12–18 of complement C9 monomers with the capacity to insert into the membranes and form a transmembrane pore [38]. In particular, CD59 restricts MAC assembly by interacting with the terminal complement proteins C8 and

C9, thus preventing C9 polymerization. CD59 prevents the flow of water and salts through the MAC preventing cell lysis (erythrocytes). This work shows a significant decrease of CD59 in T1D normoalbumuric patients compared to control, and his down-regulation is positively correlated with HbA1c and microalbuminuria, as shown for THP. Positive correlation of CD59 with HbA1c could indicate that prolonged hyperglycemia can drive both decreased expression of CD59 and CD59 glycation via the Amadori rearrangement causing inhibition of CD59 and increased MAC deposition, as reported [38–41]. These events can lead to an increase in MAC deposition that in diabetic pathology could cause a higher risk of cardio-vascular diseases [39]. Recently, a diminished expression of CD59 has been reported in peripheral blood leukocytes from T2DM patients with macrovascular diseases [42]. Apolipoprotein A-I (Apo A-I) and Apolipoprotein E (Apo E) have an important role in regulating lipid metabolism. Apo A-I, by acting as a cofactor for lecithin: cholesterol aciltransferase, participates in reverse cholesterol transport from tissues to the liver. Indeed, the role of Apo A-I is to promote the efflux of cellular cholesterol, to bind lipids, and to activate lecithin cholesterol aciltransferase to

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Acta Diabetol (2012) 49:453–464

A ***

A

Type 1 diabetes

Control

9

Control

8

8 7

**

6 5

* ***

4 3 2

***

1 THP

Apo A-I

Type 1 diabetes

7 6

*

5 4 3 2

***

*

1

0 Apo E

HMWK

0

CD59

B

AMBP

ZA2G

A1BG

RBP

B 14

* ** ** *

**

*



** †

** THP

Control

Apo A-I

Apo E

Type 1 diabetes normoalbuminuria

*

Control

12 10

HMWK

***

Type 1 diabetes microalbuminuria

8 6



4



**

**

0

CD59

AMBP

Type 1 diabetes microalbuminuria

form mature HDL [43]. In literature, strong down-regulation of Apo A-1 was reported for T2D with DN and macroalbuminuria, and this protein was progressively decreased with increasing disease severity [20]. In this study, Apo A-I showed significantly decreased levels in T1D patients with microalbuminuria compared to controls. Also down-regulation of Apo A1 seems to be a potential biomarker of kidney early failure in T1D. Apo E plays a key role in cholesterol transport, circulating plasma lipoproteins concentration, and regulatory function of inflammation. It is also directly involved in the sorting and distribution of lipids in plasma [44]. Decreased expression of Apo E can represent a marker of kidney damage. Apo E has a protective function in kidney regulation both in mesangial cell proliferation and in matrix expansion. Decrease and/or complete absence of Apo E seems to cause an acceleration of processes that induct a reduction in mesangial cell proliferation and an increase of

Type 1 diabetes normoalbuminuria

**

2

Fig. 2 Down-regulated protein in control group compared with type 1 diabetes group (a) and in control group compared with type 1 diabetes group subdivided in normoalbuminuric and microalbuminuric patients (b). Each bar represents the mean (±standard deviation, SD) of each protein. Significant differences were determined by Mann–Whitney test (p \ 0.05). ( p [ 0.05; *p \ 0.05; **p \ 0.01; ***p \ 0.001)

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Normalized Volume (%)

Normalized Volume (%)

*

9

Normalized volume (%)

Normalized volume (%)

10

ZA2G

A1BG

RBP

Fig. 3 Up-regulated protein in control group compared with type 1 diabetes group (a) and in control group compared with type 1 diabetes group subdivided in the normoalbuminuric and microalbuminuric patients (b). Each bar represents mean (±standard deviation, SD) of each protein. Significant differences were determined by Mann–Whitney test (p \ 0.05). ( p [ 0.05; *p \ 0.05; **p \ 0.01; ***p \ 0.001)

Table 3 Proteins differentially expressed in type 1 diabetes subjects compared with control subjects Protein

Fold change

Down-regulated THP

-2.72

Apo A-I

-1.79

Apo E

-1.82

HMWK

-1.77

CD59

-1.41

Up-regulated AMBP

1.62

ZA2G A1BG

3.04 3.92

RBP4

1.20

accumulation of extracellular matrix [44, 45]. As demonstrated by our data, Apo E is down-regulated in urine from T1D patients without MA but does not show a further

Acta Diabetol (2012) 49:453–464

461

Table 4 Pearson correlation of down-regulated urinary proteins in type 1 diabetes subjects

THP

HbA1c Correlation coefficient

p

Albuminuria Correlation coefficient

p

-0.81

\0.001

-0.446

\0.05

NS

-0.298

\0.05

Apo E

-0.428

NS

-0.206

NS

HMWK

-0.428

NS

-0.476

NS

CD59

-0.705

\0.01

-0.634

\0.05

Apo A-I

0.0426

THP Tamm–Horsfall urinary glycoprotein, Apo A-I apolipoprotein A-1, Apo E apolipoprotein E, HMWK a2-thiol proteinase inhibitor, CD59 Human complement regulatory protein CD59 NS not significant (p [ 0.05)

Table 5 Pearson correlation of up-regulated urinary proteins in type 1 diabetes subjects HbA1c Correlation coefficient

p

Albuminuria Correlation coefficient

p

AMBP

-0.176

NS

0.588

\0.05

ZA2G

0.678

\0.01

0.771

\0.0001

A1BG

0.483

NS

0.104

NS

RBP

0.365

NS

0.842

\0.0001

AMBP a-1-microglobulin, ZA2G zinc-a-2 glycoprotein, A1BG a-1Bglycoprotein, RBP plasma retinol-binding protein NS not significant (p [ 0.05)

downregulation during microalbuminuria. Further experiments are necessary to evaluate the potential role of specific Apo E isoforms as protein biomarkers of early kidney damage/failure. Kininogen-1 or alpha-2-thiol proteinase inhibitor is known for its anti-angiogenic properties and inhibitory action on the proliferation of endothelial cells [46]. In this study, kininogen-1 showed significantly (p \ 0.05) decreased levels in normoalbuminuric T1D patients in comparison with controls and a further downregulation in microalbuminuric patients. Its decreased expression in urine has previously been reported in patients with IgA nephropathy [47], in hypertensives [48], in patients with different glomerular diseases [21], and in several forms of cancer [49]. a-1-Microglobulin (AMBP), zinc-a-2 glycoprotein (ZA2G), a-1B-glycoprotein (A1BG), and plasma retinolbinding protein 4 (RBP4) were found to be over-represented within the diabetic group in comparison with the control group. AMBP and ZA2G showed a positive correlation with HbA1c and albuminuria. AMBP is an inhibitor of trypsin, plasmin, and lysosomal granulocytic elastase. This plasma protein could be found

free or in complexes with albumin or IgA. The free form is filtered by the glomerular membrane, while approximately 95 % of it is absorbed in the proximal tubules cells. Increased excretion of this protein is linked to uropathy [50] and dysfunction of proximal tubules cells [51]. This increased excretion also correlates to microalbuminuria and insufficient glycemic control in non-insulin-dependent diabetes mellitus [52]. Our results show a significative excretion of alpha-1-Microglobulin in normoalbuminuric T1D and an increased excretion in the presence of microalbuminuria. These findings potentially support the theory that the augmented excretion of AMBP could be useful as an index of the initial and progressive renal dysfunction, as previously showed by other studies [51]. ZA2G is a protein localized within the epithelium of various human tissues including the epithelial cells of the proximal and distal tubules in kidney. ZA2G is considered a protein without a precise function [53]. Recently, it has been classified as a new adipokine [54] probably involved in the pathogenesis of obesity by regulating lipolysis [55]. Interestingly, some adipocytokines have been found to be associated with susceptibility to renal dysfunction [56]. Moreover, ZAG was also reported to be associated with glomerular lesions [57], endemic nephropathy [58], and in urine from T2D patients [59, 60] also with microalbuminuria [61]. Our results show a significative excretion of ZA2G in normoalbuminuric T1D and a massive excretion in the presence of microalbuminuria. This protein was also positively associated with HbA1c and microalbuminuria. Differential expression of this protein can be evaluated as a potential biomarker for specific and accurate clinical analysis of early kidney failure in T1D as also reported for DN [57, 60, 61]. Although the human A1BG protein was sequenced in 1986, its physiological role is unknown. From experimental data, A1BG protein shows homology to the immunoglobulin supergene family [62], histocompatibility antigens [63], and a potential activity as tissue inhibitor of metalloproteinases as showed in its opossum homologue, Oprin [64]. Recently, this protein has been proposed as biomarker in bladder cancer [65] and interstitial cystitis [66]. Our data showed a significant excretion exclusively in normoalbuminuric patients. Moreover, this protein was not correlated to albuminuria and HbA1c. Differential expression of this protein as a potential biomarker of early kidney failure without albuminuria in T1D should be evaluated. Plasma retinol-binding protein 4 is an adipokine that belongs to the superfamily of lipocalins [67] and is the principal transport protein for retinol (vitamin A) in blood [68]. Increased serum RBP4 levels have been reported in subjects with obesity, insulin resistance, and type 2 diabetes [69–71]. Augmented urine excretion of RBP4 was reported in IDDM [72–75] and NIDDM

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[27, 57, 59, 74, 76–82]. This study evidences an increase of RBP4 especially in microalbuminuric patients. Results also showed a positive correlation between increase of RBP4 and increase of albuminuria. Therefore, augmented excretion of RBP4, which also correlates with microalbuminuria, could be a predictive biomarker of early-stage nephropathy in IDDM as previously reported [83]. It was also shown the presence, in urine proteome, of human lithostatine 1 alpha but no differential expression for this protein has been found in our samples. Human lithostatine, also known as Reg1A, is a potentially important protein expressed only in regenerating pancreatic islets and seems to be a target for production of autoantibodies in T1D [84].

Conclusions Type 1 diabetes represents a chronic disease associated with chronic hyperglycemia and metabolism disorders. In conclusion, this preliminary work provides evidence of several urine proteins potentially involved in diabetic pathology, such as THP, CD59, AMBP, ZA2G, and RBP4. Excretion levels of several proteins correlate with HbA1c and microalbuminuria that represent main clinical parameters for the evaluation of the diabetic status. In the detection of early DN, these proteomic data could be an important starting point to supervise and/or predict the state and progress of diabetic complications in addition to the evaluation of albumin excretion. Acknowledgments We wish to thank Prof. Francesco Cucca, Prof. Mario Maioli, for help in collecting the Sardinian type 1 diabetes families and for clinical information. This work was supported by project ‘‘ICT SIAI 101 SARDEGEN’’ (Proteotech srl).

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