Skip to main content

Advertisement

  • Research Article
  • Open Access

PPAR Signaling Pathway and Cancer-Related Proteins Are Involved in Celiac Disease-Associated Tissue Damage

  • 1,
  • 2,
  • 3,
  • 1,
  • 2,
  • 1 and
  • 1Email author
Molecular Medicine201016:1605199

https://doi.org/10.2119/molmed.2009.00173

  • Received: 22 November 2009
  • Accepted: 2 March 2010
  • Published:

Abstract

Celiac disease (CD) is an immune-mediated disorder triggered by the ingestion of wheat gliadin and related proteins in genetically predisposed individuals. To find a proteomic CD diagnostic signature and to gain a better understanding of pathogenetic mechanisms associated with CD, we analyzed the intestinal mucosa proteome alterations using two dimensional difference gel electrophoresis (2D-DIGE) coupled with matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF ms) of CD patients with varying degrees of histological abnormalities defined by Marsh criteria and controls. Our results clearly evidenced the presence of two groups of patients: Group A, including controls and Marsh 0–I CD patients; and Group B, consisting of CD subjects with grade II–III Oberhuber-Marsh classification. Differentially expressed proteins were involved mainly in lipid, protein and sugar metabolism. Interestingly, in Group B, several downregulated proteins (FABP1, FABP2, APOC3, HMGCS2, ACADM and PEPCK) were implicated directly in the peroxisome proliferator-activated receptor (PPAR) signaling pathway. Moreover, Group B patients presented a deregulation of some proteins involved in apoptosis/survival pathways: phosphatidylethanolamine-binding protein 1 (PEBP1), Ras-related nuclear protein (Ran) and peroxiredoxin 4 (PRDX4). PEBP1 downregulation and RAN and PRDX4 upregulation were associated with more severe tissue damage. Likewise, IgMs were found strongly upregulated in Group B. In conclusion, our results indicate that a downregulation of proteins involved in PPAR signaling and the modulation of several cancer-related proteins are associated with the highest CD histological score according to Oberhuber-Marsh classification.

Introduction

Celiac disease (CD) is caused by an immune reaction to gliadin, a gluten protein found in wheat gluten and related derivatives, in genetically predisposed individuals. The main histological feature of CD is represented by the presence of a chronic inflammation of the small bowel’s mucosa and submucosa (1,2), which produces extremely polymorphic clinical manifestations ranging from severe chronic enteritis and malabsorption to diarrhea, constipation, flatulence, weight loss, vitamin and mineral deficiencies, iron deficiency and bone disease. However in some cases, there are no gastrointestinal symptoms (3), or they are less pronounced. A permanent gluten-free diet (GFD) is currently the only accepted therapy for CD. Although most individuals respond to treatment, a minority of them (≤5%) may have persistent symptoms and villous atrophy despite scrupulous adherence to a GFD: this disorder is called refractory CD (RF-CD). The prognosis of this subgroup of patients is poor, and they show a higher risk of developing an overt lymphoma and uncontrolled malabsorption. Moreover, overall CD patients present a higher risk of developing cancer (4,5). Cancers include T- and B-cell non-Hodgkin lymphoma, oropharyngeal, esophageal and intestinal adenocarcinomas and pancreas tumors (6).

In most patients, the CD diagnosis is easily established. Nevertheless, roughly 10% of cases are difficult to diagnose because of a lack of concordance among serologic, clinical and histologic findings. The diagnosis of latent CD is usually retrospective and it is difficult to interpret whether minor small bowel mucosal changes are due to early developed CD or whether the infiltrative lymphoid cells represent an unspecific finding (7). Thus, there are a substantial number of latent and undiagnosed cases (8).

In CD, immune responses to gliadin promote the inflammatory reaction, primarily in the upper small intestine, characterized by the infiltration of the lamina propria and the epithelium with chronic inflammatory cells and villous atrophy. It is known that acquired T cell-mediated and innate immune mechanisms have an important role in CD pathogenesis (9). T cell-mediated adaptive response is mediated by CD4 + TH1 lymphocytes in the lamina propria that recognize deamidated gliadin peptides, bound to DQ2 or DQ8 HLA-II molecules, on antigen-presenting cells; T cells subsequently produce proinflammatory cytokines (10), mainly interferon (IFN)-γ (11). Tissue transglutaminase (TG2) is the enzyme that deamidates gliadin peptides determining the immunostimulator effect of gluten (12). Additional functions of TG2 consist of cross-linking gluten peptides, thus forming supramolecular complexes contributing to the formation of a wide range of T cell-stimulatory epitopes that might be implicated in different stages of the disease formation; in this context, the α2-gliadin-33mer fragment is the most immunogenic because it harbors six partly overlapping DQ2-restricted epitopes (13). The ensuing inflammatory cascade releases metalloproteinases and other tissue-damaging mediators that induce crypt hyperplasia and villous injury (14). Moreover, gliadin peptides can elicit innate immune responses that, in concert with adaptive immunity, induce mucosal damage via a T-independent pathway. In particular, it was shown that gluten peptides elicit an increased expression of interleukin-15 by macrophages, epithelial cells and dendritic cells in the lamina propria, that results in the activation of intraepithelial lymphocytes expressing the natural-killer (NK) activating receptors CD94 and NK-G2D (15,16). These activated cells become cytotoxic and kill enterocytes expressing the NK-G2D ligand, the major-histocompatibility-complex class I chain related A (MIC-A), a cell-surface antigen induced by cellular stress, thus contributing to enhanced enterocyte apoptosis (6,17,18). Upregulation of IL-15 expression by epithelial cells and dendritic cells in the lamina propria seems to also contribute to the altered signaling properties of the CD8+ T cell population (16). In addition, recent genome-wide association studies have provided convincing evidence that the IL-21 gene also is associated with CD (19,20). IL-21 has been shown to stimulate epithelial cells to secrete chemokines, to facilitate the recruitment of immune cells within the inflamed tissue and to modulate the proliferation and function of CD8 + T and NK cells (21). Moreover, in its role in the control of B-cell and plasma cell function, IL-21 may also contribute to the production of CD-associated antitransglutaminase autoantibodies (22). These observations collectively underline the complexity of the CD pathogenesis and suggest the activation of multiple cellular pathways.

Proteomic analysis is a promising tool to enhance our knowledge of fundamental aspects of how the biological systems operate and to provide practical insights that will impact medical practice (23,24). To gain a better understanding of pathogenetic mechanisms associated with CD, we used a combination of proteomic technologies: two dimensional difference gel electrophoresis (2D-DIGE) coupled with matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF ms), to investigate the intestinal mucosa tissue proteome alterations of CD patients, with respect to controls.

Our results highlight the downregulation of proteins involved in the PPAR pathway and the modulation of some cancer-related proteins associated with the highest histological grade of CD.

Materials and Methods

Patients

For proteomic analysis, duodenal biopsies were obtained from 19 suspected adult CD patients attending the Centro di Riferimento Oncologico in Aviano, Italy. Biopsies were fixed in Bouin solution and a portion of unfixed tissue was snap frozen in liquid nitrogen and stored at −80°C. Histological evaluation was performed according to modified Oberhuber-Marsh classification (2). HLA DQB1 typing was carried out as reported previously (25).

Molecular, immunohistochemical and serologic analyses excluded a CD diagnosis for 7/19 patients who were used as controls (Table 1); 10/19 subjects were confirmed to be positive for CD and the remaining 2/19 were suspected CD patients. Among confirmed CD patients, two have a Marsh 0, two a Marsh I, one has a Marsh II and five have a Marsh III histological classification. HLA DQ2/8 variants were present in all CD patients, in the suspected CD patients and in two patients with excluded CD.
Table 1

Patients characteristics.

Patient

Age/Sex

DQ2/8 variant

Modified MARSH grade

Celiac disease

Group

Diagnosis/diet (months)

Anti-Transglutaminasea

CD familiarity

    

CD patients

   

Patient 1

26/M

-b

0

Excluded

Group A

0/0

−/−

 

Patient 2

42/F

-

0

Excluded

Group A

0/0

−/−

 

Patient 3

16/F

DQ2

0

Excluded

Group A

0/0

−/−

 

Patient 4

19/F

-

0

Excluded

Group A

0/0

31/−

yes

Patient 5

59/F

DQ2

0

Excluded

Group A

0/0

31/−

 

Patient 6

25/M

-

0

Excluded

Group A

0/0

37/−

 

Patient 7

34/F

-

0

Excluded

Group A

0/0

52/−

 

Patient 8

44/M

DQ2

0

Suspectedc

Group A

36/36

−/−

 

Patient 9

46/M

DQ2

0

Suspectedd

Group A

0/0

−/7.5

 

Patient 10

39/F

DQ2

0

Confirmed

Group A

0/0

−/16

 

Patient 11

22/F

DQ2

0

Confirmed

Group A

0/0

−/73

yes

Patient 12

47/F

DQ2

I

Confirmed

Group A

60/58

−/−

 

Patient 13

34/M

DQ2

I

Confirmed

Group A

25/24

−/08

 

Patient 14

39/F

DQ2

II

Confirmed

Group B

0/0

26/16

 

Patient 15

52/M

DQ2

III A

Confirmed

Group B

0/0

−/15

 

Patient 16

36/M

DQ2

III B

Confirmed

Group B

0/0

26/20

 

Patient 17

41/M

DQ2

III B

Confirmed

Group B

0/0

−/21

 

Patient 18

38/F

DQ2

III A

Confirmed

Group B

0/0

130/NEe

 

Patient 19

33/F

DQ2

III A

Confirmed

Group B

0/0

336/NE

 
    

Refractory CD patients

   

Patient 20

32/F

DQ2

III B

Confirmed

 

96/72

−/−

yes

Patient 21

29/F

DQ8

III A

Confirmed

 

12/12

−/−

 

Patient 22

43/F

DQ2

III A

Confirmed

 

120/118

−/−

 

Patient 23

41/M

DQ2

III A

Confirmed

 

240/238

−/−

 

Patient 24

32/F

DQ2 and DQ8

III A

Confirmed

 

60/57

400/400

 

aDashes indicate values lower than the cutoff.

bDashes indicate patient does not carry either variant.

cPatient with ulcerative jejunoileitis.

dPatient with pancreatic cysts and intraductal tumor.

eNE: not evaluated.

Among CD patients, two were under GFD while the remaining cases were CD at first diagnosis (Table 1). Five patients with refractory CD (RF-CD) who no longer respond to a GFD (Table 1) have been analyzed by immunoblotting. All patients have been notified of the purpose of the study and an informed consent has been obtained for all participants.

Sample Preparation and 2D-DIGE Analysis

2D-DIGE analysis was carried out as reported previously (26,27). Proteins were extracted from gut biopsies with a sample grinding kit (GE Healthcare, Uppsala, Sweden) and 200 µL of lysis buffer (7 mol/L urea, 2 mol/L thiourea, 4% CHAPS and 30 mmol/L Tris-HCl pH 8.5). The cell lysates were precipitated before 2D-DIGE using a 2-D Clean-Up kit (GE Healthcare) and then resuspended in 7 mol/L urea, 2 mol/L thiourea and 4% CHAPS. Protein concentration was determined with Bio-Rad protein assay (Bio-Rad, Milan, IT). For DIGE minimal labeling, 25 µg of protein sample were mixed with 100 pmol CyDye (GE Healthcare). Then the sample pairs were mixed with the internal standard following the experimental design reported in Table 2. The internal standard, including equal amounts of all the samples within the experiment, has been labeled with Cy2 dye. We also adopted a dye swapping strategy to avoid a dye labeling bias, therefore Cy3 and Cy5 dyes were interchangeable.
Table 2

2D-DIGE experimental design.

Gel number

Cy2

Cy3

Cy5

1

Pooled standard

Control

CD Marsh I

2

Pooled standard

CD Marsh III B

Control

3

Pooled standard

Control

-

4

Pooled standard

Suspected CD Marsh 0

Suspected CD Marsh 0

5

Pooled standard

CD Marsh I

CD Marsh III A

6

Pooled standard

CD Marsh III A

Control

7

Pooled standard

Control

CD Marsh 0

8

Pooled standard

CD Marsh III A

CD Marsh II

9

Pooled standard

CD Marsh 0

Control

10

Pooled standard

Control

CD Marsh III B

Eleven-cm immobilized pH gradient strips (pH 3–10 NL) (Bio-Rad) were rehydrated passively overnight and then run on a Bio-Rad Protean IEF Cell. The following voltage program was used for first dimension separation: 250 V for 15 min, a slow voltage ramp to 8,000 V over 2.5 h and a final focusing step for a total of 35,000 Vh. Focused IPG strips were stored at −80°C before equilibration and application to SDS-PAGE. For the second dimension, IPG strips were equilibrated in 7 mol/L urea, 2 mol/L thiourea, 2% SDS, 30% glycerol and 50 mmol/L Tris-HCl pH 8.8, reduced with 65 mmol/L DTT and alkylated with 135 mmol/L iodoacetamide. The second dimension was run on Criterion IPG + 1 Comb 8–16% precast gels (Bio-Rad). Gels were scanned on a Typhoon TRIO scanner (GE Healthcare) at 100 mm resolution (emission filters centered at 520 nm, 580 nm and 670 nm for Cy2, Cy3 and Cy5 respectively). Images were subjected to Biological Variation Analysis (BVA), allowed matching of spots from multiple gels; then, the Difference In-gel Analysis (DIA) (using DeCyder software version 6.5 [GE Healthcare]) normalized, and statistically analyzed spot abundance to identify and quantify differentially expressed proteins. The Extended Data Analysis (EDA) module was used for multivariate analysis of protein expression data, derived from the BVA and DIA modules through principal component analysis (PCA), pattern analysis and discriminant analysis.

Protein Identification by MALDI-TOF Peptide Mass Fingerprinting

Preparative gel was obtained by the above described procedure, with a 300 µg total protein load. After 2-DE, the gel was fixed in 50% ethanol and 2% ortophosphoric acid followed by an exposure to a staining solution (17% [NH4]2SO4, 2% ortophosphoric acid, 34% methanol). Coomassie Colloidal G-250 was added to a final concentration of 0.065%. Destaining of the gel was performed with deionized water until the background was completely clear. Coomassie-stained gel was scanned with a GS-800 densitometer (Bio-Rad) at 63 µm resolution. Protein spots of interest were excised from the preparative gel and destained with 25 mmol/L ammonium bicarbonate in 50% acetonitrile. After overnight trypsin digestion, peptides were extracted with 1% TFA, subjected to Zip Tip cleanup (Millipore, Milan, IT), and directly eluted with an α-Cyano-4-hydroxycinnamic acid matrix (10 g/L α-Cyano-4-hydroxycinnamic acid in 50% acetonitrile and 0.3% TFA). Peptide mass fingerprinting (PMF) was performed on a Voyager-DE PRO Biospectrometry Workstation mass spectrometer (Applied Biosystems, Foster City, CA, USA). MALDI-TOF mass spectra were acquired in 700–4,000 Da molecular weight range, in reflector and in positive-ion mode, with 150 nanosecond delay time and an ion acceleration voltage of 20 kV. Spectra were calibrated externally using Peptide calibration Mix 4, 500–3500 Da (Laser Bio Labs, Nice, France). Mass spectra, obtained by collecting 1,000–2,000 laser shots, were processed using Data Explorer version 5.1 software (Applied Biosystems). Peak lists have been obtained from the raw data following advanced baseline correction (peak width 32, flexibility 0.5, degree 0.1), noise filtering (noise filter correlation factor 0.7) and monoisotopic peak selection. Database search was done with the online MASCOT search engine (https://doi.org/www.matrixscience.com), Aldente (https://doi.org/www.expasy.org/tools/aldente) and ProFound (https://doi.org/prowl.rockefeller.edu/prowl-cgi/profound.exe) PMF tools, against the NCBInr and Swiss Prot databases, limiting the search to human proteins, allowing for one trypsin missed cleavage and with a 100 ppm mass tolerance error. The fixed modification selected was cysteine carbamidomethylation, while the variable modification selected was the methionine oxidation. The protein list has been analyzed with the Pathway Express tool (https://doi.org/vortex.cs.wayne.edu/projects.htm) (28) to find all associated pathways.

Immunoblotting Analysis

For Western blot analysis, 25 mg of protein extracts, each comprising equal amounts of proteins from patient’s Group A and B, were run on 4% to 20% gradient precast gels (Bio-Rad) and transferred onto Protran Nitrocellulose Transfer membrane (Schleicher-Schuell, Dassel, DE, Germany) using Trans Blot Semi-dry Transfer cell (Bio-Rad) (29). The membranes were blocked with 2% nonfat milk. The primary antibodies used were: mouse antihuman RAN monoclonal antibody (1:3000) (clone ARAN1, Novus Biologicals, Littleton, CO, USA); polyclonal goat antihuman IgM HRP conjugated (1:1000) (Sigma, St. Louis, MO, USA); rabbit anti-PEBP1 polyclonal antibody (1:1000) (Cell Signaling, Danvers, MA, USA), goat polyclonal anti-FABP antibody (1:1000) (clone C-20, Santa Cruz, CA, USA); and goat antivinculin polyclonal antibody (1:1000) (clone N-19, Santa Cruz), that was used as control for equal loading. Secondary antibodies were goat antimouse IgG (1:1000) (GE Healthcare), goat antirabbit IgG (1:1000) (Cell Signaling) and donkey antigoat IgG (1:5000) (Santa Cruz), all HRP conjugated. Detection of immunoreactive proteins was accomplished with ECL Western blot detection reagent (GE Healthcare) followed by autoradiography with Hyperfilm ECL (GE Healthcare).

The same experimental procedure has been used to determine the expression levels of RAN, IgM, FABP and RKIP in patients with RF-CD. Twenty-five micrograms of protein extracts from patients 1, 2 and 3, representing the controls; 16, 17 and 19, belonging to CD patients Group B; and patients 20–24, representing the group of subjects with RF-CD, were individually loaded and run on 4% to 20% gradient precast gels and then processed as described above.

The bands were quantified by densitometry, with a GS-800 densitometer (BioRad) and Quantity One software v.4.5, to obtain an integral volume value (optical density, OD ×rea), which was then normalized with respect to the vinculin value.

Results

Analysis of CD Proteins by 2D-DIGE

PCA analysis and hierarchical clustering. The first analysis was performed following classification criteria based on histological Marsh grades of bioptic samples. Patients were, in turn, grouped as with pre-infiltrative (Marsh 0), infiltrative (Marsh I), infiltrative-hyperplastic (Marsh II) and destructive (Marsh III) lesions. Following the differential expression analysis, we created a set comprised of proteins present in more than 80% of spot maps and with a one-way analysis of variance (ANOVA) P < 0.01. Results from principal component analysis (PCA) evidenced the presence of only two groups with relevant biological correlation between proteins expression and Marsh classification: controls and CD patients with Marsh 0–I on one side (Group A) and CD patients with Marsh II–III on the other side (Group B). The same results were obtained when algorithm hierarchical clustering and pattern analysis were performed. All the subsequent calculations were then carried out following the above-described partition. PCA and hierarchical clustering were repeated, including proteins present in more than 80% of the spot maps and with a Student t test (P < 0.05) (Figure 1).
Figure 1
Figure 1

Principal component and pattern analyses of protein maps from controls and CD patients with Marsh 0–I (Group A) and CD patients with Marsh II–III (Group B). We performed PCA and hierarchical clustering on a set comprising proteins present in more than 80% of spot maps and with a t test P < 0.05.

Differentially expressed proteins. Differential expression analysis between Group A and B evidenced 234 significantly modulated proteins. The most significant ones (80 spots, P < 0.01) were excised from the Coomassie stained preparative gel and then identified by MALDI-TOF PMF. The protein identification was obtained successfully in 54 of the 80 excised spots. A comprehensive list of all identified proteins is reported in Table 3 (30). Differentially expressed proteins, marking Group A with respect to Group B, were involved mainly in several metabolic pathways: glycolysis/gluconeogenesis, lipid, glycerolipid and glycerophospholipid metabolism, urea cycle, metabolism of amino groups, metabolism of nitrogenous molecules, protein synthesis and degradation and purine metabolism (Table 3).
Table 3

Identified differentially expressed proteins.

Protein name

Average ratioa

DeCyder P valueb

Spotc

Database accession numberd

MW (kDa)

pI

Matching peptides

Coverage (%)

Identification P value

Ig mu chain C region

2.92

1.93e-4

224

P01871

50

6,4

11

19

1,00E-04

 

3.71

1.18e-4

227

   

10

22

1,20E-02

  

PPAR signaling pathway

     

Phosphoenolpyruvate carboxykinase

−2.92

1.96e-4

288

Q16822

71

7,5

17

22

3,20E-08

 

−3.3

3.55e-5

289

   

19

27

1,00E-08

Hydroxymethylglutaryl-CoA synthase

−3.33

4.49e-6

436

P54868

55

8,7

12

16

1,60E-05

Medium-chain specific acyl-CoA dehydrogenase

−1.52

4.01e-4

476

P11310

47

8,6

15

31

8,10E-07

Fatty acid binding protein

−4.23

2.91e-5

1094

P12104

15

6,6

7

31

1,90E-02

Fatty acid-binding protein

−3.1

3.16e-4

1123

P07148

14

6,6

6

38

3,30E-02

Apolipoprotein C3

−-4.22

1.66e-4

1189

P02656

9

4,7

4

46

9,20E-04

Carbonyl reductase (NADPH) 1

−1.71

4.65e-7

669

P16152

31

8,6

18

72

1,00E-20

 

−2.77

4.65e-7

683

   

13

43

1,00E-06

Retinol binding protein II

−2.99

1.12e-4

1054

P50120

16

5,3

7

42

2,60E-02

 

−2.72

1.93e-4

1057

   

5

35

1,40E-02

  

Protein metabolism

     

Carbamoyl-phosphate synthase

−1.92

7.56e-5

48

P31327

166

6,3

9

5

3,20E-02

 

−2.03

8.78e-5

49

   

16

9

5,10E-06

 

−1.76

1.93e-4

56

   

32

22

2,60E-19

Elongation factor 2

1.53

4.84e-4

134

P13639

96

6,4

16

16

1,00E-08

Tryptophanyl-tRNA synthetase

2.04

1.45e-4

377

P23381

53

5,8

16

28

1,00E-09

Ornithine aminotransferase

−1.84

8.78e-5

479

P04181

49

6,6

10

22

1,90E-04

Aminoacylase-1

−2.07

7.36e-6

487

Q03154

46

5,8

16

38

6,40E-10

Ornithine aminotransferase

−2.27

2.84e-5

488

P04181

49

6,6

16

33

1,00E-09

Ornithine carbamoyltransferase

−-2.08

1.14e-4

563

P00480

36

7,9

12

20

1,50E-04

Proteasome subunit α type-6

1.37

1.41e-3

797

P60900

28

6,3

9

32

5,90E-04

  

Sugar metabolism

     

Sucrase

−1.72

8.48e-5

62

P14410

210

5,4

22

11

6,40E-06

 

−3.43

7.31e-6

67

   

20

9

3,20E-07

Fructose bisphosphate aldolase B

−3.2

3.55e-5

554

P05062

40

8,3

13

29

6,70E-04

Aldose 1-epimerase

−1.57

1.92e-6

581

Q96C23

38

6,2

8

30

1,80E-04

Fructose-1.6-bisphosphatase

−2.96

2.15e-6

582

P09467

37

6,5

16

33

3,20E-13

Aflatoxin B1 aldehyde reductase member 3

−1.37

5.91e-4

620

O95154

37

6,7

14

41

3,20E-07

  

Lipid metabolism

     

Aldo-keto reductase family 1 member B10

−2.97

3.13e-3

605

O60218

36

7,1

7

20

2,50E-02

 

−2.31

2.34e-4

615

   

11

32

4,70E-04

Glycerol-3-phosphate dehydrogenase

−2.39

5.39e-6

623

P21695

38

5,8

11

22

8,00E-07

Hydroxyacyl-coenzyme A dehydrogenase

−1.62

1.04e-4

665

Q16836

33

8,4

14

30

3,20E-07

Dihydroxyacetone kinase

−2.03

2.20e-5

323

Q3LXA3

59

7,6

15

30

3,20E-09

  

Energy production

     

Aconitate hydratase

−1.74

8.48e-5

180

Q99798

86

7,6

22

28

8,10E-09

Cytochrome b5

−2.4

1.57e-5

1004

P00167

11

5

7

79

2,50E-08

  

Detoxification

     

Catalase

−1.39

1.00e-4

337

P04040

60

6,9

15

28

1,60E-08

Sulfotransferase 1A3/1A4

−1.92

1.50e-4

643

P50224

34

5,7

16

56

1,30E-11

Glutathione S-transferase A1

−1.96

3.16e-4

820

P08263

26

8,9

7

21

1,20E-02

  

Cell proliferation/apoptosis

     

GTP-binding nuclear protein Ran

1.91

2.67e-6

814

P62826

25

7,1

11

42

1,30E-08

Phosphatidylethanolamine-binding protein 1

−1.42

1.25e-3

882

P30086

21

7,4

12

56

1,30E-07

Hypotetical protein MGC29506

2.61

3.22e-5

964

Q8WU39

21

5,4

8

28

3,80E-02

Peroxiredoxin-4

1.78

1.50e-4

782

Q13162

31

5,9

8

27

1,10E-03

  

Structural function

     

Villin 1

−1.4

1.93e-4

144

P09327

93

5,9

30

29

3,20E-14

Lamin-A/C

2.25

1.93e-4

221

P02545

74

6,6

28

42

1,30E-12

 

2.3

5.12e-5

229

   

22

36

4,10E-08

Actin beta

−2.55

2.79e-3

522

P60709

40

5,5

17

43

4,10E-13

Actin-related protein 2/3 complex subunit 2

−1.64

2.06e-6

697

O15144

34

6,8

16

34

6,30E-08

  

Purine metabolism

     

Guanine deaminase

−1.27

4.58e-4

451

Q9Y2T3

51

5,4

12

28

9,10E-04

Adenosine deaminase

−1.9

1.25e-3

519

P00813

41

5,6

13

29

2,60E-07

Purine nucleoside phosphorylase

−2.07

2.26e-4

701

P00491

32

6,5

13

35

2,00E-06

  

Other proteins

     

Calcium-activated chloride channel regulator 1

−2.47

3.22e-4

169

A8K7I4

101

5,9

17

14

1,60E-08

Voltage-dependent anion-selective channel protein 1

−1.46

8.48e-5

666

P21796

31

8,6

8

31

1,10E-04

DnaJ homolog subfamily B member 11

1.31

7.56e-5

534

Q9UBS4

41

5,8

10

28

7,00E-05

aStandardized volume ratio of the protein spot, calculated by DeCyder BVA module, between Group B (CD patients with Marsh II–III) and Group A (controls and CD patients with Marsh 0–I) patients.

bt Test P value determined by DeCyder EDA analysis.

cSpot numbers referring to (30).

dProtein accession numbers were derived from UniProtKB/Swiss-Prot database.

Of note, we found that, in Group B, several downregulated proteins (FABP1, FABP2, APOC3, HMGCS2, ACADM, PEPCK) were implicated directly in the PPAR signaling pathway as resulting from Pathway Express tool analysis (P = 1.62e-8; Figure 2) (28).
Figure 2
Figure 2

A simplified view of differentially expressed proteins according to PPAR signaling pathway resulting from Pathway Express tool analysis. All the illustrated proteins were downregulated in Group B (CD patients with Marsh II–III) with respect to Group A (Controls and CD patients with Marsh 0–I). Protein abbreviations correspond to Entrez Gene official symbols.

In addition, Group B patients presented a deregulation of some proteins involved in apoptosis/survival pathways: phosphatidylethanolamine-binding protein 1 (PEBP1), also known as Raf kinase inhibitory protein (RKIP), Ras-related nuclear protein Ran and peroxiredoxin 4 (PRDX4). Among them, PEPB1 and RAN proteins, respectively down- and upregulated in Group B, were particularly interesting for their involvement in cancer development.

Finally, we found that IgMs were upregulated strongly in patients with the highest histological grade of CD (Group B).

Marker selection. The EDA discriminant analysis tool can be used to find the smallest subset of proteins, called classifiers, that allows distinguishing between experimental groups and eventually classify a new set of spot maps.

Marker selection calculation was performed with the set including proteins present in more than 80% of spot maps and with a t test (P < 0.05). Analysis was performed with partial least squares method as search method, and with regularized discriminant analysis as evaluation method. As cross-validation options, we set five folds.

Results indicated that using two of the proteins indicated in Table 4, the IgM, selected by all the classifiers and fatty acid binding protein, chosen by two out of five classifiers, it is possible to discriminate between Group A and B with an accuracy score of 100% (data not shown). We created a classifier, identified as RDA1, comprised of IgM and fatty acid binding protein. This classifier was able to classify all the analyzed samples correctly.
Table 4

Marker selection results.a

Spot number

Rank

Appearance

Name

Accession number

227

1

5

Immunoglobulin M

P01871

222

2

1

NI

NI

436

2

1

Hydroxymethylglutaryl-CoA synthase

P54868

1123

2

2

Fatty acid binding protein

P07148

134

2

1

Elongation factor 2

P13639

aMarker selection calculation was performed with the set, including proteins present in more than 80% of spot maps and with a t test (P < 0.05). Analysis was performed with partial least squares method as the search method, and with regularized discriminant analysis as the evaluation method. As cross-validation options, we set five folds. In this table, we indicated the spot number, the rank value (the mean of the classifier’s ranking of the protein), the appearance value (indicating the number of classifiers that have selected the corresponding protein), the protein name and the accession number from Swiss-Prot database. Owing to the low protein amount, we were not able to identify spot 222.

Immunoblotting Analysis

We have used the protein immunoblotting assay to confirm the differential expression of IgM, FABP, RAN and PEBP1 in the two groups of patients. Vinculin has been used as control for equal loading. Figure 3 shows the immunoblotting results. Normalizing the Western blot results against vinculin, we confirmed that IgM and RAN were upregulated, while PEBP1 and FABP were downregulated in Group B, in accordance with 2D-DIGE analysis.
Figure 3
Figure 3

Validation by immunoblotting of several proteins differentially expressed in Group A compared with Group B and analysis of IGM, FABP, RAN and PEBP1 expression levels in RF-CD patients. Immunoblotting assay evidences the differential expression of IGM, FABP, RAN and PEBP1 in Group A (lane 1), Group B (lane 2) and in RF-CD patients (lane 3). Vinculin has been used as control for equal loading. Immunoblotting demonstrates the upregulation of IgM and RAN and the downregulation of the PEBP1 and FABP, in Group B compared with Group A, in accordance with the results obtained by 2D-DIGE analysis. In patients with RF-CD, IgMs remained elevated, FABP was reversed toward normalization, RAN was increased while PEBP1 was decreased with respect to both Group A and Group B CD patients. The Figure shows the results of three representative samples, one from each analyzed Group (A, B and RF-CD). Experimental details are described in the materials and method section.

We therefore examined the expression level (mean ± SD) of these proteins, normalized with respect to vinculin, on patients with RF-CD. As a preliminary result from RF-CD patients, tissue IgM level remained elevated with respect to controls (2.3 ± 1,01 versus 1.4 ± 0.1), FABP decreased in Group B patients, but reversed toward normalization in RF-CD patients (0.9 ± 0.6 versus 0.8 ± 0.1), RAN was increased further (1.9 ± 0.6 versus 1.2 ± 0.3) while PEBP1 was decreased (1.6 ± 0.9 versus 2.7 ± 0.7) (Figure 3).

Discussion

CD occurs in adults and children at rates approaching 1% of the population (6,31,32). Its diagnosis requires abnormalities on laboratory tests that might be caused by malabsorption (for example, folate deficiency and iron-deficiency anemia), the presence of high antigliadin titers, antitissue transglutaminase and antiendomysial antibodies, the presence of an HLA DQ2/8 variant, a duodenal biopsy that shows the characteristic findings of intraepithelial lymphocytosis, crypt hyperplasia, villous atrophy and, above all, a positive response to a gluten free diet (GFD). The spectrum of pathologic changes in CD ranges from near-normal villous architecture, with a prominent intraepithelial lymphocytosis, to total villous atrophy (33).

The histologic findings in CD are characteristic but not specific (34); their presence, and particularly the observation of a Marsh-Oberhuber type III lesion, permits a presumptive diagnosis of CD and initiation of a GFD. CD is not the only cause of lymphocytosis and villous atrophy (35) and the diagnosis is confirmed only when there is a favorable response to the diet, evaluated after at least a 6-month follow up. Moreover, the pathogenesis of CD-associated tissue damage is still poorly understood (36).

On these grounds, the aim of this study was to investigate for the first time the intestinal mucosa tissue proteome alterations between CD patients with different histological Marsh grade and a control group of patients in whom a CD diagnosis was excluded after clinical, laboratory and histological examinations.

The comparison of the protein expression profile of CD patients grouped according to their histological classification and controls, by both PCA and pattern analyses, failed in discriminating between latent or non-specific forms (Marsh 0–I) and controls. By converse, it clearly emerged that a common protein profile characterized the patient with an infiltrative-hyperplastic (Marsh II) and the patients with a destructive (Marsh III) lesion. Following a two-group classification scheme, Group A (controls and CD patients with Marsh 0–I) and Group B (CD patients with Marsh II–III), the combination of 2D-DIGE, DeCyder EDA and MALDI-TOF PMF, led us to achieve a classifier (Table 4), composed by two proteins (IgM and FABP1) that was able to distinguish between these two groups with 100% accuracy.

Among the proteins included in the classifiers, the IgM upregulation represents the best hallmark of CD patients with Marsh II–III. An enhanced local IgM secretion had already been demonstrated in CD patients (37). Since IgM antibodies can activate complement, it has been suggested that they might contribute to the damage following the encounter with antigens (for example, gluten) (38,39). Thus, our data confirm the concept of an IgM segregation in the gut, and indicate that B cells, responsible for IgM production, are associated with CD and are involved in tissue damage.

Two other proteins included in the classifiers, FABP1 and HMGCS2, are both integrated in the PPAR signaling pathway (40,41). Downregulation of PPAR signaling pathway in the highest grade of CD also is corroborated by the modulation of other proteins involved in PPAR signaling as FABP2, PEPCK, APOC3 and ACADM (see Figure 2) (Table 3) (42). Moreover, in the intestine, the PPAR expression correlates with the abundance of the cellular retinol binding protein (42), that we found downregulated accordantly in Group B. Also carbonyl reductase (CBR1), known to be involved in the conversion of prostaglandin E2 (PGE2) into PGF, and whose expression has been found to be induced as a consequence of PPARα activation (43), was downregulated in Group B.

The PPARα, −γ and −β/δ are ligand-activated nuclear receptors with a wide range of effects on metabolism, cellular proliferation, differentiation and immune response (44). They form heterodimers with the retinoid X receptor (RXR) and activate transcription by binding to specific DNA elements (see Figure 2) (4446). PPARs are the major regulators of lipid and fatty acid metabolism and regulate transport, oxidation, storage and synthesis of fatty acids (47). Moreover PPARy activation increases glucose uptake and synthesis by skeletal muscle, and, in addition, reduces hepatic glucose production and its subsequent release by a decreased gluconeogenesis (4850). Thus, the downregulation of PPAR pathway we found in the Marsh II and III CD could contribute to the downregulation of proteins involved in fatty acid and sugar metabolism observed in our and other series of CD patients (Table 3) (51).

Interestingly, a recent study has evidenced the correlation among gliadin, tissue transglutaminase (TG2) and PPAR (52). The authors demonstrated that peptide p 31–43, one of the most “toxic” gliadin peptides for predisposed individuals (53), determined an increased production of reactive oxygen species and a TG2 overexpression in epithelial cells, which then induced PPARγ ubiquitination and degradation (52). These findings are in line with our results evidencing that the proteins related to PPAR-signaling are downregulated. Moreover these data are particularly suggestive if we consider our preliminary results on RF-CD: in refractory. In RF-CD, where IgA-tTg mostly return to normal values, immunoblotting findings highlight a normalization of FABP level that is directly linked to PPAR level.

PPARs also are important regulators of the immune system. Recent data reported that a depressed expression or activity of these receptors might lead to an inability to mount an effective immune response and, possibly, to an exacerbation of autoimmune disease (54). Moreover, ligands for PPAR have therapeutic activity in several rodent models of inflammatory and autoimmune disease (45,5557), thus suggesting that they might have similar effects in human disease as well. Besides, it has been shown that PPARγ can inhibit the production of several T-cell cytokines, including the classical TH1-cell cytokine IFN-γ (58), known to be important in the maintenance of inflammation and in CD pathogenesis (11). These functions are mediated largely through the abilities of the PPARα and −γ to transrepress the activities of many inflammation-associated transcription factors, including NF-κB (59). In fact, PPARα can directly bind NF-κB p65, thus interfering with NF-κB transcriptional activation (45,60), while PPARγ haploinsufficiency results in dysregulation of NF-κB and hyperreactivity of B cells (54). Since NF-κB is constitutively active in intestinal mucosa of patients with untreated CD (61), and autoantibodies are implicated in CD pathogenesis, we hypothesize that the downregulation of PPAR signaling pathway may contribute to the NF-κB activation and play a pivotal role in the CD-associated inflammation. A further contribution to NF-κB activation also is determined by the upregulation of RAN protein (62) and of PRDX4 (63), and by the downregulation of PEBP1, also known as RKIP (64,65), all of which we found modulated in our series (Table 3).

It is known that adult CD patients present a higher risk of developing cancer than the general population (4,5). In our series, we found the upregulation of known cancer-related proteins (RAN and PRDX4) (62,63) and the downregulation of PEBP1 (65). The Ran-GTP signaling pathway was found to be exploited preferentially in cancer (66), as becoming essential for cell division in transformed cells (but not in normal cells). In this regard, we reported a parallel increase in the RAN abundance and the Marsh index in CD previously (67). PRDX4 is an immediate regulator of H2O2-mediated NF-κB activation (63); moreover it has been recently demonstrated that TNF-related apoptosis-inducing ligand (TRAIL), by suppressing the PRDX4 level, might facilitate cell death (68). PEBP1, also known as RKIP, is a modulator of the RAF/MAPK signaling cascade and acts as a suppressor of metastasis and apoptosis, and it was found downregulated in Group B (64).

Interestingly, if we consider our preliminary results on RF-CD, the proteins associated with tumor development (RAN and PEBP1) (see Figure 3) continue to vary with respect to both Group A and Group B patients in a trend suggestive of an increased risk of malignant evolution.

As a whole, these results give an insight into the molecular mechanisms determining the perpetuation of the severe inflammatory response in CD and of the proteins possibly involved in cell deregulation associated with a risk to develop cancer.

Finally, our data suggest the potential role of PPAR as a therapeutic target for the modulation of inflammation in CD. The role of PPAR and the regulators of the inflammatory response in CD deserves further research focus.

Disclosure

The authors declare that they have no competing interests as defined by Molecular Medicine, or other interests that might be perceived to influence the results and discussion reported in this paper.

Declarations

Acknowledgments

The authors wish to thank the “Centro di Biomedicina Molecolare” (CBM), which provided the Proteomic Instruments, and Anna Vallerugo for her writing assistance. This study was supported by Programma Integrato Oncologia, Tematica 2 and “Projetto celiachia complicata” Associazione Italiana Celiaci (AIC).

Authors’ Affiliations

(1)
Experimental and Clinical Pharmacology Unit, CRO Centro di Riferimento Oncologico, IRCCS National Cancer Institute, via F. Gallini 2, 33081 Aviano, PN, Italy
(2)
Department of Gastroenterology, CRO Centro di Riferimento Oncologico, IRCCS National Cancer Institute, Aviano, PN, Italy
(3)
Department of Pathology, CRO Centro di Riferimento Oncologico, IRCCS National Cancer Institute, Aviano, PN, Italy

References

  1. Marsh MN, Crowe PT. (1995) Morphology of the mucosal lesion in gluten sensitivity. Baillieres Clin. Gastroenterol. 9:273–93.View ArticleGoogle Scholar
  2. Oberhuber G, Granditsch G, Vogelsang H. (1999) The histopathology of coeliac disease: Time for a standardized report scheme for pathologists. Eur. J. Gastroenterol. Hepatol. 11:1185–94.View ArticleGoogle Scholar
  3. Catassi C, Fabiani E. (1997) The spectrum of coeliac disease in children. Baillieres Clin. Gastroenterol. 11:485–507.View ArticleGoogle Scholar
  4. Cellier C, et al. (2000) Refractory sprue, coeliac disease, and enteropathy-associated T-cell lymphoma. French coeliac disease study group. Lancet. 356:203–8.View ArticleGoogle Scholar
  5. Rampertab SD, Forde KA, Green PH. (2003) Small bowel neoplasia in coeliac disease. Gut. 52:1211–61.View ArticlePubMed CentralGoogle Scholar
  6. Green PH, Cellier C. (2007) Celiac disease. N. Engl. J. Med. 357:1731–43.View ArticleGoogle Scholar
  7. Carmack SW, Lash RH, Gulizia JM, Genta RM. (2009) Lymphocytic disorders of the gastrointestinal tract: A review for the practicing pathologist. Adv. Anat. Pathol. 16:290–306.View ArticleGoogle Scholar
  8. Catassi C, et al. (1994) Coeliac disease in the year 2000: Exploring the iceberg. Lancet. 343:200–3.View ArticleGoogle Scholar
  9. Di Sabatino A, Corazza GR. (2009) Coeliac disease. Lancet. 373:1480–93.View ArticleGoogle Scholar
  10. Sollid LM. (2002) Coeliac disease: Dissecting a complex inflammatory disorder. Nat. Rev. Immunol. 2:647–55.View ArticleGoogle Scholar
  11. Nilsen EM, et al. (1998) Gluten induces an intestinal cytokine response strongly dominated by interferon gamma in patients with celiac disease. Gastroenterology. 115:551–63.View ArticleGoogle Scholar
  12. Molberg O, et al. (1998) Tissue transglutaminase selectively modifies gliadin peptides that are recognized by gut-derived T cells in celiac disease. Nat. Med. 4:713–7.View ArticleGoogle Scholar
  13. Camarca A, et al. (2009) Intestinal T cell responses to gluten peptides are largely heterogeneous: Implications for a peptide-based therapy in celiac disease. J. Immunol. 182:4158–66.View ArticlePubMed CentralGoogle Scholar
  14. Mohamed BM, et al. (2006) Increased protein expression of matrix metalloproteinases −1, −3, and −9 and TIMP-1 in patients with gluten-sensitive enteropathy. Dig. Dis. Sci. 51:1862–8.View ArticleGoogle Scholar
  15. Mention JJ, et al. (2003) Interleukin 15: A key to disrupted intraepithelial lymphocyte homeostasis and lymphomagenesis in celiac disease. Gastroenterology. 125:730–45.View ArticleGoogle Scholar
  16. Zhang X, Sun S, Hwang I, Tough DF, Sprent J. (1998) Potent and selective stimulation of memoryphenotype CD8+ T cells in vivo by IL-15. Immunity. 8:591–9.View ArticleGoogle Scholar
  17. Meresse B, et al. (2004) Coordinated induction by IL15 of a TCR-independent NKG2D signaling pathway converts CTL into lymphokine-activated killer cells in celiac disease. Immunity. 21:357–66.View ArticleGoogle Scholar
  18. Hue S, et al. (2004) A direct role for NKG2D/MICA interaction in villous atrophy during celiac disease. Immunity. 21:367–77.View ArticleGoogle Scholar
  19. van Heel DA, et al. (2007) A genome-wide association study for celiac disease identifies risk variants in the region harboring IL2 and IL21. Nat. Genet. 39:827–9.View ArticlePubMed CentralGoogle Scholar
  20. Glas J, et al. (2009) Novel genetic risk markers for ulcerative colitis in the IL2/IL21 region are in epistasis with IL23R and suggest a common genetic background for ulcerative colitis and celiac disease. Am. J. Gastroenterol. 104:1737–44.View ArticleGoogle Scholar
  21. Parrish-Novak J, et al. (2000) Interleukin 21 and its receptor are involved in NK cell expansion and regulation of lymphocyte function. Nature. 408:57–63.View ArticleGoogle Scholar
  22. Dienz O, et al. (2009) The induction of antibody production by IL-6 is indirectly mediated by IL-21 produced by CD4+ T cells. J. Exp. Med. 206:69–78.View ArticlePubMed CentralGoogle Scholar
  23. Kuramitsu Y, Nakamura K. (2006) Proteomic analysis of cancer tissues: Shedding light on carcinogenesis and possible biomarkers. Proteomics. 6:5650–61.View ArticleGoogle Scholar
  24. Jiang YJ, Sun Q, Fang XS, Wang X. (2009) Comparative mitochondrial proteomic analysis of raji cells exposed to adriamycin. Mol. Med. 15:173–82.View ArticlePubMed CentralGoogle Scholar
  25. De Re V, et al. (2007) Genetic insights into the disease mechanisms of type II mixed cryoglobulinemia induced by hepatitis C virus. Dig. Liver Dis. 39 Suppl 1:S65–71.View ArticleGoogle Scholar
  26. De Re V, et al. (2007) Proteins specifically hyperexpressed in a coeliac disease patient with aberrant T cells. Clin. Exp. Immunol. 148:402–9.View ArticlePubMed CentralGoogle Scholar
  27. De Re V, et al. (2008) HCV inhibits antigen processing and presentation and induces oxidative stress response in gastric mucosa. Proteomics Clin. Appl. 2:1290–9.View ArticleGoogle Scholar
  28. Draghici S, et al. (2007) A systems biology approach for pathway level analysis. Genome Res. 17:1537–45.View ArticlePubMed CentralGoogle Scholar
  29. De Re V, et al. (2006) HCV-NS3 and IgG-fc cross-reactive IgM in patients with type II mixed cryoglobulinemia and B-cell clonal proliferations. Leukemia. 20:1145–54.View ArticleGoogle Scholar
  30. Simula MP, et al. (2009) Two-dimensional gel proteome reference map of human small intestine. Proteome Sci. 7:10.View ArticlePubMed CentralGoogle Scholar
  31. West J, et al. (2003) Seroprevalence, correlates, and characteristics of undetected coeliac disease in England. Gut. 52:960–5.View ArticlePubMed CentralGoogle Scholar
  32. Fasano A, et al. (2003) Prevalence of celiac disease in at-risk and not-at-risk groups in the United States: A large multicenter study. Arch. Intern. Med. 163:286–92.View ArticleGoogle Scholar
  33. Marsh MN. (1992) Gluten, major histocompatibility complex, and the small intestine. A molecular and immunobiologic approach to the spectrum of gluten sensitivity (‘celiac sprue’). Gastroenterology. 102:330–54.View ArticleGoogle Scholar
  34. Memeo L, et al. (2005) Duodenal intraepithelial lymphocytosis with normal villous architecture: Common occurrence in H. pylori gastritis. Mod. Pathol. 18:1134–44.View ArticleGoogle Scholar
  35. Dickson BC, Streutker CJ, Chetty R. (2006) Coeliac disease: An update for pathologists. J. Clin. Pathol. 59:1008–16.View ArticlePubMed CentralGoogle Scholar
  36. Briani C, Samaroo D, Alaedini A. (2008) Celiac disease: From gluten to autoimmunity. Autoimmun. Rev. 7:644–50.View ArticleGoogle Scholar
  37. Crabtree JE, Heatley RV, Losowsky ML. (1989) Immunoglobulin secretion by isolated intestinal lymphocytes: Spontaneous production and T-cell regulation in normal small intestine and in patients with coeliac disease. Gut. 30:347–54.View ArticlePubMed CentralGoogle Scholar
  38. Scott BB, Scott DG, Losowsky MS. (1977) Jejunal mucosal immunoglobulins and complement in untreated coeliac disease. J. Pathol. 121:219–23.View ArticleGoogle Scholar
  39. Halstensen TS, Hvatum M, Scott H, Fausa O, Brandtzaeg P. (1992) Association of subepithelial deposition of activated complement and immunoglobulin G and M response to gluten in celiac disease. Gastroenterology. 102:751–9.View ArticleGoogle Scholar
  40. Kaikaus RM, Chan WK, Ortiz de Montellano PR, Bass NM. (1993) Mechanisms of regulation of liver fatty acid-binding protein. Mol. Cell. Biochem. 123:93–100.View ArticleGoogle Scholar
  41. Juge-Aubry C, et al. (1997) DNA binding properties of peroxisome proliferator-activated receptor subtypes on various natural peroxisome proliferator response elements. importance of the 5′-flanking region. J. Biol. Chem. 272:25252–9.View ArticleGoogle Scholar
  42. Desvergne B, Wahli W. (1999) Peroxisome proliferator-activated receptors: Nuclear control of metabolism. Endocr. Rev. 20:649–88.PubMedGoogle Scholar
  43. Yokoyama Y, et al. (2007) Clofibric acid, a peroxisome proliferator-activated receptor alpha ligand, inhibits growth of human ovarian cancer. Mol. Cancer. Ther. 6:1379–86.View ArticleGoogle Scholar
  44. Kota BP, Huang TH, Roufogalis BD. (2005) An overview on biological mechanisms of PPARs. Pharmacol. Res. 51:85–94.View ArticleGoogle Scholar
  45. Straus DS, Glass CK. (2007) Anti-inflammatory actions of PPAR ligands: New insights on cellular and molecular mechanisms. Trends Immunol. 28:551–8.View ArticleGoogle Scholar
  46. Delerive P, Fruchart JC, Staels B. (2001) Peroxisome proliferator-activated receptors in inflammation control. J. Endocrinol. 169:453–9.View ArticleGoogle Scholar
  47. Kersten S, Desvergne B, Wahli W. (2000) Roles of PPARs in health and disease. Nature. 405:421–4.View ArticleGoogle Scholar
  48. Zierath JR, et al. (1998) Role of skeletal muscle in thiazolidinedione insulin sensitizer (PPARgamma agonist) action. Endocrinology. 139:5034–41.View ArticleGoogle Scholar
  49. Raman P, Judd RL. (2000) Role of glucose and insulin in thiazolidinedione-induced alterations in hepatic gluconeogenesis. Eur. J. Pharmacol. 409:19–29.View ArticleGoogle Scholar
  50. Bragt MC, Popeijus HE. (2008) Peroxisome proliferator-activated receptors and the metabolic syndrome. Physiol. Behav. 94:187–97.View ArticleGoogle Scholar
  51. Bertini I, et al. (2009) The metabonomic signature of celiac disease. J. Proteome Res. 8:170–7.View ArticleGoogle Scholar
  52. Maiuri L, et al. (2010) Lysosomal accumulation of gliadin p31–43 peptide induces oxidative stress and tissue transglutaminase-mediated PPARgamma downregulation in intestinal epithelial cells and coeliac mucosa. Gut. 59:311–9.View ArticleGoogle Scholar
  53. Maiuri L, et al. (2003) Association between innate response to gliadin and activation of pathogenic T cells in coeliac disease. Lancet. 362:30–7.View ArticleGoogle Scholar
  54. Setoguchi K, et al. (2001) Peroxisome proliferator-activated receptor-gamma haploinsufficiency enhances B cell proliferative responses and exacerbates experimentally induced arthritis. J. Clin. Invest. 108:1667–75.View ArticlePubMed CentralGoogle Scholar
  55. Kielian T, Drew PD. (2003) Effects of peroxisome proliferator-activated receptor-gamma agonists on central nervous system inflammation. J. Neurosci. Res. 71:315–25.View ArticleGoogle Scholar
  56. Lovett-Racke AE, et al. (2004) Peroxisome proliferator-activated receptor alpha agonists as therapy for autoimmune disease. J. Immunol. 172:5790–8.View ArticleGoogle Scholar
  57. Dubuquoy L, et al. (2006) PPARgamma as a new therapeutic target in inflammatory bowel diseases. Gut. 55:1341–9.View ArticlePubMed CentralGoogle Scholar
  58. Cunard R, et al. (2002) Regulation of cytokine expression by ligands of peroxisome proliferator activated receptors. J. Immunol. 168:2795–802.View ArticleGoogle Scholar
  59. Daynes RA, Jones DC. (2002) Emerging roles of PPARs in inflammation and immunity. Nat. Rev. Immunol. 2:748–59.View ArticleGoogle Scholar
  60. Delerive P, et al. (1999) Peroxisome proliferator-activated receptor alpha negatively regulates the vascular inflammatory gene response by negative cross-talk with transcription factors NF-kappaB and AP-1. J. Biol. Chem. 274:32048–54.View ArticleGoogle Scholar
  61. Maiuri MC, et al. (2003) Nuclear factor kappa B is activated in small intestinal mucosa of celiac patients. J. Mol. Med. 81:373–9.View ArticleGoogle Scholar
  62. Jiang X, et al. (2003) NF-kappa B p65 transactivation domain is involved in the NF-kappa B-inducing kinase pathway. Biochem. Biophys. Res. Commun. 301:583–90.View ArticleGoogle Scholar
  63. Jin DY, Chae HZ, Rhee SG, Jeang KT. (1997) Regulatory role for a novel human thioredoxin peroxidase in NF-kappaB activation. J. Biol. Chem. 272:30952–61.View ArticleGoogle Scholar
  64. Odabaei G, et al. (2004) Raf-1 kinase inhibitor protein: Structure, function, regulation of cell signaling, and pivotal role in apoptosis. Adv. Cancer Res. 91:169–200.View ArticleGoogle Scholar
  65. Yeung KC, et al. (2001) Raf kinase inhibitor protein interacts with NF-kappaB-inducing kinase and TAK1 and inhibits NF-kappaB activation. Mol. Cell. Biol. 21:7207–17.View ArticlePubMed CentralGoogle Scholar
  66. Xia F, Lee CW, Altieri DC. (2008) Tumor cell dependence on ran-GTP-directed mitosis. Cancer Res. 68:1826–33.View ArticleGoogle Scholar
  67. Simula MP, et al. (2009) Comment re: Ran-GTP control of tumor cell mitosis. Cancer Res. 69:1240; author reply 1240.View ArticleGoogle Scholar
  68. Wang H, et al. (2009) TNF-related apoptosis-inducing ligand suppresses PRDX4 expression. FEBS Lett. 583:1511–5.View ArticleGoogle Scholar

Copyright

© The Feinstein Institute for Medical Research 2010

Advertisement