Elsevier

Journal of Proteomics

Volume 91, 8 October 2013, Pages 259-269
Journal of Proteomics

Comparative proteomic analysis of neutrophils from patients with microscopic polyangiitis and granulomatosis with polyangiitis

https://doi.org/10.1016/j.jprot.2013.07.021Get rights and content

Highlights

  • We examine protein profiles of neutrophils to distinguish between MPA and GPA.

  • Intensity of 55 spots is significantly different between the 2 disease groups.

  • The profiles of 13 protein spots completely distinguish the 2 disease groups.

Abstract

Both microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA) belong to ANCA-associated vasculitis (AAV), in which neutrophils play a key role in their pathology. In this study, in order to discriminate between MPA and GPA, protein profiles of peripheral blood polymorphonuclear cells (PMNs) of 11 MPA patients and 9 GPA patients and 10 healthy controls (HC) were analyzed by 2D-DIGE. In all the 864 spots detected, intensity of 55 spots was significantly different (p < 0.05) among the three groups by ANOVA. 31 out of the 55 spots were identified by mass spectrometry. Orthogonal partial-least-squares-discriminate analysis revealed that the abundance profile of the protein spots discriminated the AAV group from the HC group, and the MPA group from the GPA group completely. 13 protein spots were considered as biomarker candidates to distinguish between MPA and GPA. In those, spots whose intensity was higher in MPA than in GPA included actin with various pI values, while a considerable part of spots whose intensity was higher in GPA were proteins related with the activity of neutrophils. Among the candidate proteins, ROC analysis showed that a combination of neutrophil gelatinase-associated lipocalin and a-kinase anchor protein 7 isoforms beta had a high diagnostic potential.

Biological significance

In this study, protein profiles of polymorphonuclear cells (PMNs) of microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA) patients and healthy controls (HC) were investigated by 2D-DIGE, and MS analysis. As a result, we found that the protein profiles of PMNs were useful for distinguishing between patients (MPA and GPA) and HC, and between patients with MPA and patients with GPA. Especially, we found that the 13 protein spots that consisted of 10 proteins considerably contributed to the discrimination between MPA and GPA. This is the first to demonstrate that protein profiles of PMNs are different among MPA, GPA and healthy control. The 10 proteins we identified in this study would be new biomarkers for the diagnosis of the diseases, and may be reflect the pathology difference between MPA and GPA.

Introduction

Microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA) are systemic small vessel vasculitides affecting multiple organs [1], [2], [3], [4]. In patients with MPA and GPA, anti-neutrophil cytoplasmic antibody (ANCA) is frequently observed [1], [5]. Thereby, these diseases are classified into ANCA-associated vasculitides similarly as Churg–Strauss syndrome (CSS) and renal limited vasculitis, and ANCA-based pathophysiology involving neutrophils is hypothesized [6], [7], [8]. Even though GPA, MPA, and CSS are systemic vasculitides, patients with CSS present clinical features of eosinophilia and allergic disorders, which are not observed in patients with GPA and MPA [2], [4]. In contrast, GPA and MPA present similar clinical features to each other, instantly, involvement of kidney and lung. Sometimes it is difficult to distinguish GPA from MPA, in particular, in the early phases of the diseases.

Histologically, GPA presents granuloma in active lesions, but MPA does not [4], [9], [10]. Thereby, tissue biopsy of lung, kidney, and/or skin is usually performed to determine whether granuloma exists or not. When granuloma is evidenced, it is highly diagnostic for GPA [11], however, granuloma may not be contained in examined small samples even in the cases of GPA. Considering that tissue biopsy is an invasive examination, simpler but more useful examinations should be established.

Serologically, 82 to 94% of patients with GPA and MPA have been reported to be positive for ANCA [12], [13]. Target antigens are different between GPA and MPA, that is, 70 to 80% of patients with GPA possess ANCA to proteinase 3 (PR3-ANCA) and 10% of them possess ANCA to myeloperoxidase (MPO-ANCA) [14], [15]. On the other hand, 50 to 75% of patients with MPA possess MPO-ANCA and 30% of them possess PR3-ANCA [4], [13], [16], [17], [18]. Further, MPO-ANCA is detected not only in MPA but also in liver diseases, inflammatory bowel diseases, and infection [19]. Thus the profile of ANCA is only partially useful for the differential diagnosis between MPA and GPA. Additional useful biomarkers should be established for the differential diagnosis.

Proteomics of a rather new technique provided us with comprehensive analysis of proteins in clinical samples, i.e., cells, tissues, and body fluid [20], [21], [22], [23], [24]. Using proteomic approaches, we have reported various biomarkers for diseases [25], [26], [27]. Instantly, we have reported that patients with Crohn's disease and those with ulcerative colitis were able to be distinguished using 2D-DIGE and the subsequent multivariate analysis of the protein profiles of peripheral blood mononuclear cells [27]. This technique would be useful for the differential diagnosis of GPA and MPA, even though such data have not been reported yet, to our knowledge. Thereby, we here attempted to distinguish between GPA and MPA by the proteomic approaches. Specifically, we analyzed protein profiles of polymorphonuclear cells (PMNs), consisting of neutrophils largely in patients with GPA and MPA, since a series of reports supported critical roles of activated neutrophils in the pathogenesis of GPA and MPA [6], [7], [8]. Then we established models that distinguish GPA from MPA by principal component analysis (PCA) and orthogonal partial-least-squares-discriminate analysis (OPLS-DA) [28], [29] of the obtained protein profile data. As a result, one of the models using abundance profiles of 13 protein spots that consisted of 10 proteins successfully distinguished GPA from MPA. Our model would be useful for the differential diagnosis of GPA and MPA.

Also, the 10 proteins that contributed to the discrimination may be involved in the pathophysiology of MPA and GPA.

Section snippets

Clinical samples

Eleven patients with MPA (mean age 65.9, 3 men and 8 women), 9 patients with GPA (mean age 60.8, 5 men and 4 women), and 10 healthy people (mean age 72.9, 5 men and 5 women) were enrolled in this study (Table 1). The patients fulfilled the ACR and the Chapel Hill criteria [1]. Disease activity in the patients with MPA, GPA was estimated with the Birmingham Vasculitis Activity Score version 3 (BVAS v.3) shown in Table 1. Four patients were not taking corticosteroids at the time of blood

Results

To obtain protein profiles of PMNs in AAV, we first applied 2D-DIGE to protein samples extracted from PMNs of 11 patients with MPA, 9 patients with GPA, and 10 healthy controls (HC) (Table 1). Specifically, each of the protein samples labeled with Cy5 and the standard sample labeled with Cy3 were separated simultaneously in the same gel. Then the separated proteins of each sample were detected by Cy5, as representative results from the MPA patients, the GPA patients, and the healthy controls

Discussion

In this study, we analyzed protein profiles of peripheral blood PMNs to identify molecules useful for distinguishing patients with AAV (MPA and GPA) from HC subjects, and for discriminating patients with MPA from those with GPA. Such molecules would be useful for the diagnosis of the diseases and for the investigation of the pathology of AAV. In particular, as mentioned above, GPA is characterized histologically by the formation of granuloma, which is critical for the outcome of the patients,

Conclusions

In the study, we compared the protein profile of PMNs using proteomics to find biomarkers that discriminate between MPA and GPA. The protein profiles were found clearly different between AAV and HC and between MPA and GPA. In particular, the protein profile of GPA was characterized by the increased abundance of inflammation-related proteins.

Abbreviations

    AAV

    ANCA-associated vasculitis

    AKA7A

    a-kinase anchor protein 7 isoforms beta

    ANCA

    anti-neutrophil cytoplasmic antibody

    AUROC

    area under the ROC curve

    AZP

    azathioprine

    CRP

    c-reactive protein

    CSS

    Churg–Strauss syndrome

    CY

    cyclophosphamide

    Cy3

    cyanine dye 3

    Cy5

    cyanine dye 5

    FBA

    fructose-bisphosphate aldolase A

    GPA

    granulomatosis with polyangiitis

    HC

    healthy controls

    IEF

    isoelectric focusing

    IIF

    indirect immunofluorescence assay

    IVCY

    intravenous cyclophosphamide

    LEI

    leukocyte elastase inhibitor

    MMF

    mycophemolate mofetil

    MPA

Competing interest

The authors declare that they have no competing interests.

Author contributions

TU, KN, KT conceived the study, and participated in its design and coordination. TU, KN, ST participated in the laboratory work and wrote the manuscript. TU, OS, NH, TY participated in the acquisition of clinical samples. KN, ST performed the experiments data analysis. OS, NH, TY, AM, IN, OK, SY, KM, OS, KT contributed to the conception of the study and interpretation of the data, and gave technical advice. All authors read and approved the final manuscript.

The following are the supplementary

Acknowledgements

We are grateful to Ms. Michiyo Katano-Yokoyama, Ms. Atsuko Nozawa, and Ms. Junko Asano for their technical assistance.

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