A functional proteomics approach to the comprehension of sarcoidosis

Journal of Proteomics 128 (2015) 375–387

Contents lists available at ScienceDirect

Journal of Proteomics journal homepage: www.elsevier.com/locate/jprot

A functional proteomics approach to the comprehension of sarcoidosis C. Landi a,⁎,1, E. Bargagli b,1, A. Carleo b, L. Bianchi a, A. Gagliardi a, G. Cillis b, M.G. Perari b, R.M. Refini b, A. Prasse c, L. Bini a, P. Rottoli b a b c

Functional Proteomic Section, Department of Life Sciences, University of Siena, Siena,Italy UOC Respiratory Diseases and Lung Transplantation, Department Internal and Specialist Medicine,University of Siena, Siena,Italy Department of Pneumology, Medical School Hannover, Carl-Neubergstrasse 1, 30625 Hannover, Germany

a r t i c l e

i n f o

Article history: Received 5 February 2015 Received in revised form 30 July 2015 Accepted 19 August 2015 Available online 2 September 2015 Keywords: Functional proteomics Sarcoidosis Tuberculosis Bronchoalveolar lavage Pathway analysis Mass spectrometry

a b s t r a c t Pulmonary sarcoidosis (Sar) is an idiopathic disease histologically typified by non-caseating epitheliod cell sarcoid granulomas. A cohort of 37 Sar patients with chronic persistent pulmonary disease was described in this study. BAL protein profiles from 9 of these Sar patients were compared with those from 8 smoker (SC) and 10 no-smoker controls (NSC) by proteomic approach. Principal Component Analysis was performed to clusterize the samples in the corresponding conditions highlighting a differential pattern profiles primarily in Sar than SC. Spot identification reveals thirty-four unique proteins involved in lipid, mineral, and vitamin Dmetabolism, and immuneregulation of macrophage function. Enrichment analysis has been elaborated by MetaCore, revealing 14-3-3ε, α1-antitrypsin, GSTP1, and ApoA1 as “central hubs”. Process Network as well as Pathway Maps underline proteins involved in immune response and inflammation induced by complement system, innate inflammatory response and IL-6signalling. Disease Biomarker Network highlights Tuberculosis and COPD as pathologies that share biomarkers with sarcoidosis. In conclusion, Sar protein expression profile seems more similar to that of NSC than SC, conversely to other ILDs. Moreover, Disease Biomarker Network revealed several common features between Sar and TB, exhorting to orientate the future proteomics investigations also in comparative BALF analysis of Sar and TB. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Sarcoidosis (Sar) is a multisystem disorder of unknown aetiology characterized by the formation of noncaseating epithelioid cell granulomas. This disease normally involves the lungs, lymph nodes and eyes but any organ or system can be affected. The pathogenesis of sarcoidosis is not completely understood. In patients with genetic susceptibility persistent exposure to an unknown inhaled antigen may induce an exaggerated immune response mediated by antigen-presenting cells (APC) such as macrophages and dendritic cells. Heterogeneous presentation, from benign to severe disease, has been described and an acute variant can be distinguished from a subacute/persistent one that can lead to irreversible fibrotic alterations of the organs involved. Clinical manifestations at onset can be different.

⁎ Corresponding author. E-mail addresses: [email protected], [email protected] (C. Landi). 1 Both the authors contributed equally to the study.

http://dx.doi.org/10.1016/j.jprot.2015.08.012 1874-3919/© 2015 Elsevier B.V. All rights reserved.

Löfgren's syndrome is a specific form of sarcoidosis with generally good prognosis. The criteria to define disease activity and severity (number of organ involved, radiological stages, etc.) are not yet clearly defined. The prognosis of sarcoidosis is unpredictable: some patients experience spontaneous remission and others develop chronic persistent disease with relapses in some cases and different responses to treatment. Inthis context, no specific biomarkers with sufficient sensitivity and specificity to predict clinical outcome or to guide therapy are available. Interaction between antigens and APC polarizes T lymphocytes to T helper 1 (Th1) phenotype and it is followed by cell recruitment, proliferation and differentiation, forming sarcoid granulomas, that are constituted by lymphocytes, macrophages, epithelioid and giant cells [1,2]. Evolution of sarcoidosis may vary: remission occurs in the majority of cases with good long-term outcomes, while persistent granuloma inflammation may lead to fibrotic lung disease [3,4]. Although diagnosis still requires histological analysis [5], bronchoalveolar lavage (BAL) has a role in the diagnostic algorithm of the disease. Bronchoscopy is a less invasive technique than biopsy, enabling BAL which provides information on lung cell populations, lymphocyte phenotypes and protein expression. BAL is useful as it reflects the lung

376

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

environment. BAL analysis can reveal major biological activities related to inflammation, oxidation–reduction, tissue matrix turnover and immunity [6]. In the majority of sarcoidosis patients, BAL cellular pattern is characterized by an increase in lymphocytes, mostly CD4+T cells with a CD4/CD8 ratio N 3.5. Proteomic analysis of human body fluids has become an important way to discover biomarkers. Comparison of BAL proteomes of different diseases is a systematic differential investigation of changes in pattern of BAL proteins useful to evaluate pathogenetic mechanisms and potential therapeutic targets. Previous studies have shown that the protein composition of BAL varies in different interstitial lung diseases [7,8]. The aim of this study was to investigate the protein profile and network of BAL, resolved by 2D electrophoresis, from Sar patients with chronic persistent disease compared with smoker (SC) and no-smoker controls (NSC) in order to study the pathogenesis of chronic disease in active phase and to identify proteins of interest to validate as potential prognostic biomarkers. Smoking is a known risk factor for the development of various interstitial lung diseases [9], but is commonly believed to be negatively associated with the occurrence of Sar [10]. A further aim of this study was to evaluate the influence of cigarette smoking on the BAL proteome of sarcoidosis patients and the pathogenetic mechanisms of the disease.

2. Materials and methods

Control subjects were matched for age and gender, had no history of asthma or allergy and were not subjected to any kind of therapy. They had normal lung function parameters and chest X-ray. The controls underwent bronchoscopy for diagnostic reason (i.e. after a single episode of haemoptysis, the bronchoscopy did not reveal any alteration, the haemoptysis was due to a physiological fragility of the upper respiratory tract capillaries). BAL samples were collected in the middle lobe of the right lung as commonly accepted to facilitate the recovery of the instilled fluid. International guidelines for BAL procedure clearly stated that the traditional BAL site for healthy controls is lingual orright middle lobe. The same operator, the same staff members performed this procedure in all patients and controls and BAL samples were processed in the same lab at Siena University. Patients and controls enrolled in this study gave their written informed consent to participation in the study that was approved by the ethical committee of Siena University Hospital (AOUS). The validation was performed by Western blot in order to confirm some of the differences obtained by proteomics; Western blots were performed on independent BAL samples from a population of Sar patients and controls different from that used for proteomic analysis. Population was composed of 5 Sar patients (4 female, 1 male, mean age 64.4 ± 12, 2 ex-smokers, FVC: 92.2 ± 19.3, FEV1: 91.2 ± 29), 5 smoker healthy controls (3 female, 2 male, mean age 59 ± 5.1, 3.8 pack/years, FVC: 99.3 ± 21, FEV1: 93.6 ± 22.9) and 5 no smoker healthy controls (4 female, 1 male, mean age 62 ± 8.1, FVC: 101 ± 33.2, FEV1: 95.1 ± 22.8).

2.1. Population

2.2. Bronchoscopy and BAL

A cohort of 37 sarcoidosis patients (24 (64.9%) female, 13 (35.1%) male; 8 (21.62%) smokers, 18 (32.43%) ex-smokers and 11 (45.95%) non-smokers, mean age 51.3 ± 10.6) regularly followed at Siena Regional ILD Referral Centre for sarcoidosis, was enrolled in the study. Diagnosis was conducted according to international criteria (WASOG task force) [11]. Patients underwent medical examination, lung function tests with DLCO determination, chest X-ray and HRCT to evaluate radiological stage and lung involvement, ultrasonography of organs potentially involved, echocardiography to exclude heart involvement and to evaluate PAPs values, and their medical histories were recorded. At onset of the disease or in the case of relapse, while not under pharmacological treatment, all patients had undergone bronchoscopy with BAL. BAL cell analysis and lymphocyte phenotype were performed. At onset and in the case of relapse, patients were also analysed for serological biomarkers, including ACE,lysozyme and chitotriosidase, to evaluate disease activity/severity. Nine out of 37 sarcoidosis patients were selected for BAL proteome analysis: all of them had histologically-demonstrated sarcoidosis with lung involvement and they represent a homogenous group of chronic persistent well documented sarcoidosis. Patients with Löfgren's syndrome were excluded. These nine patients were all symptomatic with dry cough, asthenia and dyspnoea. They had radiological evidence of stage 2 or 3 sarcoidosis (3/9 were in stage 2, 6/9 in stage 3)and elevated serum levels of ACE and chitotriosidase with respect to normal referral values at the moment of bronchoscopy. All had BAL lymphocytosis with increased CD4/CD8 ratio. All started systemic steroid treatment for sarcoidosis lung involvement after bronchoscopy. Selection criteria for the inclusion and exclusion of these 9 patients were:none had previously been treated with steroids or other immunosuppressant at the time of the bronchoscopy; they were monitored regularly at our Regional ILD Referral Centre in Sienafrom onset for at least 12 months; and they had no history of concomitant pathology (e.g. patients with pulmonary hypertension associated with ILD were excluded). For all of them wehad detailed medical history, including occupational exposure and pharmacological therapy. BAL proteomic findings from these nine patients with Sar were compared with BAL features of 10 non-smoker (NSC, mean age of 65.3 ± 8.5, 4 male) and eight smoker controls (SC, mean age 64 ± 5.4, 4 male, 4 packs/year).

Bronchoscopy and BAL were performed as previously reported [8]. BAL samples were obtained by a standard method, instilling 180 ml of normal saline solution by fibroscope (Olympus IT-10; Olympus Italia, Milan, Italy). The first aliquot obtained was not used for immunological testing. The other aliquots were centrifuged at 800 × g for 5 min to separate the cells from the fluid component of BAL. Cell differentiation was performed on cytocentrifuge preparations. BAL lymphocyte phenotype was characterized using flow cytometry (FACSCalibur, Becton & Dickinson, Milan, Italy) and monoclonal antibodies (antiCD3, -CD4, -CD8, and -CD69; Dickinson and Company, San Jose, California, USA). 2.3. Angiotensin converting enzyme (ACE) Serum concentrations of ACE were determined by a commercial assay (Buhlmann ACE colorometric, Buhlmann Laboratories AG, Schonenbuch, Switzerland) widely used to determine ACE activity in serum, urine and tissues, as previously described [12]. Serum concentrations of angiotensin converting enzyme (ACE) were determined measuring absorbance (at 382 nm) of cyanuric chloride– hippuric acid complex derived from a synthetic substrate of ACE,Nhippuryl- L -histidyl- L -leucine. The upper limit of normal ACEconcentrations was 68 U/l. In vivo, ACE catalyses conversion of angiotensin I to angiotensin II and inactivates bradykinin during regulation of blood pressure by the renin–angiotensin system. Elevated levels of serum ACE have been measured in patients suffering from various disorders. ACE is released by epithelioid cells and its serum levels are significantly correlated with the granuloma burden of the patient. The positive predictive value of elevated ACE activity is estimated between 75 and 90%, and the negative predicted value between 70 and 80%. An initial low ACE activity indicates a good prognosis. 2.4. Chitotriosidase assay Chitotriosidase levels were established in blood samples by a fluorimetric method, incubating 22 μM 4-methylumbelliferryl β D-NNNtriacetylchitotriosidase (Sigma Chemical Co.) in citrate phosphate buffer

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

(pH 5.2) for 1 h at 37 °C and then stopping the reaction by adding 0.1 M glycine–NaOH buffer, pH 10.8 [13,14] Fluorescence was read at 450 nm with a Perkin Elmer LS40 fluorimeter using an excitation wavelength of 365 nm. Serum levels of chitotriosidase were expressed in nmol/h/ml. 2.5. Lysozyme activity assay Serum lysozyme concentrations were evaluated through commercial turbidimetric assay (FAR Diagnostics srl, Verona, Italy) in which lysozyme hydrolytic activity of β-glucosidic bonds was assessed giving a suspension of Micrococcus lysodeikticus cell membranes in PBS buffer as substrate. The turbidity decrease of suspension after 2 min is proportional to lysozyme activity and is related to its concentration by a calibration curve [15].

377

matching all gels of each group (9 for Sar, 10 for NSC and 8 for SC) with a reference gel for the same condition, having the best resolution and the greatest number of spot, chosen by the user and named “Master” by the software (intra-class analysis). The three Master reference gels were then matched with each other (inter-class analysis). By this procedure, the Image Master Platinum algorithm matched all the gels to find quantitative differences. Quantitative differences were considered when the ratio of mean percentage relative volume (%V) was at least ± 2 fold and satisfied the non-parametric Kruskal–Wallis test (p b 0.05) in comparisons of mean ranks by Dunn's test (Graphpad Prism 5 for Windows). Moreover, we performed False Discovery Rate (FDR) to correct for multiple comparisons.

2.9. MALDI-ToF-MS — protein identification 2.6. 2D-Electrophoresis BAL fluid sample preparation BAL samples obtained by bronchoscopy were centrifuged at 800 ×g for 5 min to separate BAL fluid from the cell component. BAL fluid was dialyzed for 12 h against distilled water at 4 °C to eliminate salts, lyophilized and dissolved in lysis buffer solution composed of 8 M urea, 4% w/ v3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate hydrate (CHAPS), 40 mM Tris base and 65 mM dithioerythritol (DTE). After determination of protein concentration by Bradford protein assay [16], lysis buffer and traces of bromophenol blue were added to samples up to 60 μg proteins in 100 μl solution for the analytical run and 500 μg proteins in 200 μl solution for the preparative run. 2.7. 2D-Electrophoresis The Immobiline polyacrylamide system was used to perform 2DElectrophoresis (2DE). For the first dimension run, immobilized nonlinear pH 3–10 gradient on strips 18 cm in length (GE Healthcare, Uppsala, Sweden) were employed. Runs were carried out using the Ettan™ IPGphor™ system (Amersham Biosciences) at 16 °C. Electrical conditions were: 0 V for 1 h, 30 V for 8 h, 200 V for 1 h, from 300 to 3500 V in 30 min, 3500 V for 3 h, from 3500 to 8000 V in 30 min and 8000 V, for a total of 90,000 Vh. MS-preparative strips were prerehydrated with 350 μl urea 8 M, 4% w/vCHAPS, 1% w/vDTE and 2% v/v carrier ampholyte at room temperature for 12 h and samples were loaded at the cathodic and anodic ends. Preparative runs were carried out using a Multiphor™ II electrophoresis system at 16 °C with the following voltage steps: 200 V for 6 h, 600 V for 1 h, 1200 V for 1 h, 3500 V for 3 h and 5000 V for 14 h. At the end of the first dimension run, strips were washed with deionized water and equilibrated with two buffers, the first composed of 6 M urea, 2% w/v Sodium Dodecyl Sulphate (SDS), 2% w/vDTE, 30% v/v glycerol and 0.05 M Tris–HCl pH 6.8 for 12 min and the second of 6 M urea, 2% w/vSDS, 2.5% w/v iodoacetamide, 30% v/v glycerol, 0.05 M Tris–HCl pH 6.8 and a trace of bromophenol blue for a further 5 min. The second dimension was completed at 40 mA/gel constant current on 9–16% SDS polyacrylamide linear gradient gels (size: 18 × 20 cm × 1.5 mm) at 9 °C. When the dye front reached the bottom of the gel, the separation was stopped [17]. Analytical gels were stained with ammoniacal silver nitrate and digitized with a Molecular Dynamics 300S laser densitometer (4000 × 5000 pixels, 12 bits/pixel; Sunnyvale, CA, USA). MS-preparatory gels were attached to a glass surface using γ-methacryloxypropyltrimethoxysilane (also known as Bind-silane, LKBProdukter AB, Brommo, Sweden), stained with SYPRO Ruby (Bio-rad headquarters, Hercules, California) and digitized with a Typhoon 9400 laser densitometer (GE Healthcare) [18–20]. 2.8. 2D-image and statistical analysis 2D image analysis was performed using Image Master Platinum 7.0 software (GE Healthcare). The analysis process was performed by

Protein identification was performed by Peptide Mass Fingerprinting (PMF) using mass spectrometry. MS-preparative gels were mechanically cut with an Ettan Spot Picker (GE Healthcare) to excise electrophoretic spots stained with SYPRO Ruby. These were destained in 2.5 mM ammonium bicarbonate and 50% acetonitrile (ACN), dehydrated in acetonitrile, rehydrated and digested overnight at 37 °C in trypsin solution. Digested protein solution was placed on the MALDI target, dried, covered with matrix solution of α-cyano-4-hydroxycinnamic acid (CHCA) in 50% v/vACN and 0–5% v/vTFA, and allowed to dry again. An UltrafleXtreme™ MALDI-ToF/ToF (Brucher Corporation, Billerica, MA,United States) acquired the masses of the peptides and PMF search was performed using MASCOT (Matrix Science Ltd., London, UK,http://www.matrixscience.com), setting up the following research parameters: Mammalia as taxonomy, Swiss-Prot/TrEMBL and NCBInr as databases, 100 ppm as mass tolerance, one acceptable missed cleavage site, carbamidomethylation (iodoacetamide alkylation) of cysteine as fixed modification, and oxidation of methionine as a possible modification.

2.10. LC–ESI-IT-MS2— protein identification Additional peptide sequencing was performed on a nanoscale LC– ESI-IT-MS2 in cases of uncertain MALDI-ToF/ToF identification [21]. A PHOENIX 40 chromatograph (ThermoQuest Ltd., Hemel Hempstead, U.K.) and an LCQ DECA IonTrap mass spectrometer (Finnigan, San Jose, CA, USA) composed LC–MS system. Briefly, 5 μl of trypsin-digested solutions was injected into the chromatography system via a six-port valve and was trapped in a C18 trapping column (20 mm × 100 μm ID × 360 μm OD, Nanoseparations, Nieuwkoop, NL) using 100% HPLC grade water +0.1% v/v formic acid (solvent A)at a flow rate of 5 μl/min for 10 min. Flow rate was adapted to 100–125 nL/min by a precolumn splitter restrictor on a C18 analytical column (30 cm × 50 μm ID × 360 μm OD, Nanoseparations). Analytical separation was performed using a linear gradient up to 60% acetonitrile +0.1% v/v formic acid (solvent B)for 30 min. Trapping and analytical columns were washed for 10 min in 100% solvent B and were equilibrated for 10 min in 100% solvent A after separation. Stable spray was obtained supplied 195 °C and 2 kV to ESI needle (5 cm × 25 μm ID × 360 μm OD, Nanoseparations). Management of LC pump, mass spectrometer parameters and automatic spectral acquisitions was organized using Xcaliburtm 1.2 software (Thermo). MS/MS ion search was conducted in Swiss-Prot/UniprotKB databases using MASCOT. Research parameters were: Homo sapiens as taxonomy, peptide precursor charge at 2+ or 3+, mass tolerance at ±1.2 Da for precursor peptide and ±0.6 Da for fragment peptides, only one missed cleavage site acceptable, carbamidomethylation of cystein as fixed modification, and oxidation as possible modification. Peptides with individual ion scores −10*Log[P] were considered significant.

378

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

Table1 Table reporting total Sar population, Sar patient for 2DE, healthy no-smoker and smoker subjects characteristics such as age, sex, smokers (%), lung function tests with FVC, FEV1, TLC, DLCO, FEV1/FVC, KCO determination, BAL macrophage, lymphocytes, neutrophils, eosinophils percentages. Patients were also analysed for serological biomarkers, including Angiotensinogen Converting Enzyme (ACE) and lysozyme. CD4/CD8 ratio was also reported. General features

Age (years)

Female

Male

Smokers

No smokers

Ex-smokers

FVC

FEV1

TLC

Sarcoidosis patient Sarcoidosis patient (2DE) Healthy no-smokers subjects Healthy smokers subjects

51.3 ± 10.6 60.3 ± 9.4 65.3 ± 8.5 64 ± 5.7

24 (64.9%) 5 (55.5%) 6 (60%) 4 (50%)

13 (35.1%) 4 (44.5%) 4 (40%) 4 (50%)

8 (21.6%) 2 (22.2%)

11 (29.7%) 4 (44.4%) 10 (100%)

18 (48.7%) 3 (33.3%)

105 ± 18 97.8 ± 15.5 99.8 ± 23.5 98.5 ± 31.8

96.1 ± 18 95.6 ± 18.4 98.3 ± 19.4 94.1 ± 31.6

116 ± 15.8 97.3 ± 11.3 99.3 ± 18.3 95.3 ± 11.1

8 (4 pack/years)

2.11. Western blot analysis

3. Results

Western blot analysis was carried out using five different BAL fluid samples, with respect to that of proteomic analysis, for each condition, to validate the reliability of the electrophoresis results. Analysis was performed for 14-3-3 ε, annexin 3 and plastin 2. BAL fluid samples were dissolved in Laemmli buffer consisting of 100 mM Tris–HCl pH 6.8, 2%(w/v) SDS, 20% (v/v) glycerol, and 4% (v/v) β-mercaptoethanol, andheated at 95 °C for 5 min. For each sample, 25 μg of protein was loaded and separated on 12% polyacrylamide gel. As set out by Towbin, gels were transferred onto nitrocellulose membrane (Hybond ECL,GE Healthcare). To verify the success of protein transfer, they were transiently stained with Ponceau Red (0.2% w/v Ponceau S in 3% w/v trichloroacetic acid) [22,23]. Rabbit antiplastin 2, anti-14-3-3ε and mouse monoclonal anti-annexin A3 (Sigma Aldrich, St. Louis, USA) were primary antibodies for immunodetection, achieved with the appropriate dilutions indicated by the manufacturers. Goat-anti-mouse and goat-anti-rabbit were used as secondary antibodies (Sigma Aldrich). Hybridization with primary antibodies was performed overnight at room temperature, and goat-anti-mouse and goat-anti-rabbitHRP-conjugate secondary antibodies (Sigma Aldrich) were incubated for 2 h at room temperature. Image Quant LAS 4000 (GE Healthcare) visualized immuostained bands by cheminoluminescence and Image J software quantified the density values.

3.1. Population

2.12. Principal Component Analysis In order to perform Principal Component Analysis, our data were organized in a matrix, where the columns represented gel maps and the rows the differentially expressed spots, reporting their %V in every gel for the three conditions. Principal Component Analysis (PCA) was performed using MultiExperiment Viewer (MeV) v.4.9 software (TM4, Microarray Software Suite, Dana-Faber Cancer Institute, Boston, MA, USA, http://www.tm4.org); samples were projected in a multidimensional vector space according to the variance of each variable. After a linear transformation and reduction of complexity, the samples were plotted in a 3-dimensional Cartesian axes system, limiting analysis to the three principal new variables.

2.13. Network and pathway analyses Network and pathway analyses were performed submitting the identified protein list to the MetaCore 6.8 network building tool (GeneGo, St., Joseph, MI, USA, http://portal.genego.com). This software used scientific literature data and annotated databases of protein interactions and metabolic reactions to process the entry proteins list by the “shortest-path” algorithm and showed the networks graphically through “nodes” representing proteins and “arches” representing protein interactions. The “shortest-path” algorithm built a hypothetical network connecting two experimental proteins directly or using only one MetaCore database protein.

The characteristics of the Sar population are reported in Table1. As expected, there was a prevalence of females over males and the population was composed mainly of young adult non-smokers or ex-smokers. Lung function test showed normal results including DLCO, while BAL clearly showed an elevated lymphocyte percentage with increased CD4/ CD8 ratio. The concentrations of ACE, chitotriosidase and BAL lymphocytes in these patients were highly increased than normal referral values (p b 0.01). In the subgroup of sarcoidosis patients undergoing proteomic analysis, the main symptoms reported were dyspnoea, dry cough and asthenia. BAL showed lymphocytosis and increased CD4/CD8 ratio. In this subgroup of patients, radiological stages 2–3 were observed, HRCT documented lung involvement in all patients and eye involvement and heart involvement were evaluated and excluded. In these nine patients, chitotriosidase, lysozyme and ACE levels were 138.73 ± 87.2, 8.3 ± 3.1and 58.8 ± 23.2 nmol/h/ml respectively. BAL cell findings in total Sar patients include macrophages 54.2 ± 22.7, lymphocytes 41.2 ± 23.1, neutrophils 4.3 ± 4.6, and eosinophils 2.8 ± 3.2 percentage [24].

3.2. 2D-Electrophoresis and mass spectrometry In this study, BAL fluid proteomic profiles from nine sarcoidosis patients against ten non-smokers and eight smoker controls were resolved using 2D-Electrophoresis. Image Master 2D Platinum 7.0 software detected about 1000 spots in each gel. The effectiveness of average-matching between each gel and the corresponding “Master gel” was 80% of total number of spots. Pairwise comparison among the three “Master gels” revealed quantitative protein differences, taking as significant an at least two fold change in percentage relative volume (%V) and passing the Kruskal–Wallis and Dunn tests (p ≤ 0.05). In order to validate statistical significance among three groups, the False Discovery Rate (FDR) test was also performed. Fifty-three spots passed the tests.The results are reported in Table 2. These spots were identified using MALDI-ToF-MS and LC–ESI-ITMS2 (if required). Thirty-four unique proteins were identified and listed in Table 2 with the corresponding MS results (such as score, number ofmatched peptides, sequence coverage and peptide sequence) and statistical values (such as False Discovery Rate, Kruskal–Wallis value, Kruskal–Wallis statistic and Dunn's Multiple Comparison). Protein numbers in Table 2 corresponded to the circled spots in the reference gel map (Fig. 1). Sar up-regulated proteins compared to NSC or SCincluded 14-3-3 protein epsilon, annexin A2, pulmonary surfactant A2, complement factors C3, H,B and I,protein S100A8, vitamin-Dbinding protein, retinol-binding protein 4, cystatin B,alpha-1antitrypsin, alpha-2-HS-glycoprotein, antithrombin III, apolipoprotein AI, haptoglobin, serotransferrin, transthyretin, zinc-alpha-2-glycoprotein, beta 2 microglobulin, ceruloplasmin and albumin. In particular, despite several proteins, such as complement C3, complement factor H,retinolbinding protein 4, alpha-2-HS-glycoprotein, fatty-acid-binding protein and ceruloplasmin are observed to have similar changes in expression

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

379

Table 1 Table reporting total Sar population, Sar patient for 2DE, healthy no-smoker and smoker subjects characteristics such as age, sex, smokers (%), lung function tests with FVC, FEV1, TLC, DLCO, FEV1/FVC, KCO determination, BAL macrophage, lymphocytes, neutrophils, eosinophils percentages. Patients were also analysed for serological biomarkers, including Angiotensinogen Converting Enzyme (ACE) and lysozyme. CD4/CD8 ratio was also reported. DLCO

FEV1/FVC

KCO

ACE

Lysozyme

Macrophages

Neutrophils

Eosinophils

Lymphocytes

CD4/CD8

79.9 ± 14 78.7 ± 21.5 98.5 ± 21.6 93.8 ± 25.9

96.1 ± 6.5 95.8 ± 10.3 98.2 ± 9.6 92.2 ± 7.3

92.7 ± 10.9 97.1 ± 9.3 99.5 ± 8.3 94.8 ± 7.9

59.7 ± 30.3 58.8 ± 23.2

7.5 ± 7.1 8.3 ± 3.1

54.2 ± 22.7 61.3 ± 19.9 88.53 ± 19.2 92.25 ± 10

4.3 ± 4.6 1.1 ± 0.4 1.5 ± 9.4 1.9 ± 5.1

2.8 ± 3.2 1.2 ± 1.1 0.8 ± 3.1 0.8 ± 3.1

41.2 ± 23.1 35.3 ± 20.8 8.3 ± 10.8 7.7 ± 11.5

2.53 + 1.3 2.51 + 1.8 4.7 ± 3.2 4.1 ± 4.8

levels between Sar and SC in comparison to NSC, we also found that complement factor I,vitamin-D-binding protein, glutathione S transferase P,alpha-1-antitrypsin, Ig alpha-2 chain C region, antithrombin III, haptoglobin, selenium-bindingprotein and albumin presented an opposite expression pattern in these two cohorts compared to NSC.

On the other hand, Pathway Map analysis revealed that the differentially expressed proteins are implicated in immunological mechanisms related to immunological responses (alternative and classical complement and lectin-induced pathways), blood coagulation, heme metabolism, IL-1 signalling pathway and transport ACM3 signalling in lacrimal glands.

3.3. Principal Component Analysis (PCA)

3.5. Western blot analysis

PCA is a non-parametric method used to distil relevant information from a huge set of data. When we entered the matrix containing %V of the differentially-expressed spots of the three conditions, the software highlighted the spatial disposition of samples, the reproducibility of biological replicates for each condition and the variance of the groups of proteins analysed. Fig. 2A shows the PCA distribution (PC1: 31.76%, PC2: 22.56%, and PC3: 6.60%) in which all samples from the three cohorts corresponded to their respective groups (Fig. 2A). SC and Sar cohorts had lower intra-group variance than NSCs. The Sar group was clearly separated from NSC and SC. The first component PC1 separated Sar from SC while PC2 separated Sar and SC from the NSC group. The eigenvalue scree plot (Fig.2B) shows the graph of correlation matrix eigenvalues for significant principal components, revealing high significance of the first three principal components.

Validation by Western blot analysis was performed for three interesting proteins found differently expressed by proteomic analysis in the three conditions (Sar, NSC, SC): annexin A3, plastin 2 and 14-3-3ε. We performed Western blot analysis with a different cohort of 5 patients, 5 smoker and 5 non-smoker controls in order to improve the clinical records. In proteomic analysis annexin A3 was found up-regulated in NSCwith respect to Sar and its expression was validated by 1D Western blot on the basis of the normalized relative integrated density values of the detected bands (Fig.4A). The bands were normalized to the integrated density of a protein band visualized on nitrocellulose membrane stained with Ponceau S reagent. These reference bands were not identified but were constantly and equally expressed in all three groups, as shown in Fig.4 (Ponceau S). The immunoblot histogram shows mean (± standard deviation) normalized relative integrated density. Statistical significance was analysed by the Mann–Whitney test:*p ≤ 0.05and **p ≤ 0.01. Proteomic trends of annexin A3, plastin 2 and 14–3-3ε are also shown in Fig.4 (silver) histograms with spot relative volume percentage on the y-axis and the groups (Sar, SC and NSC) on the x-axis. Dunn's test p-values are reported: *p ≤ 0.05; **p ≤ 0.01; and ***p ≤ 0.001. 1D Western blot of Plastin 2 confirmed its up-regulation in BAL of smoker controls with respect to Sar patients (Fig.4B). Validation of 14-3-3ε confirmed its over-expression in Sar with respect to NSC but without any significant difference of expression between Sar and SC(Fig.4C).

3.4. Network and pathway analyses Metacore software analysis produced a network of differently expressed proteins highlighted by proteomic and interaction data from the MetaCore database. In the network map (Fig.3) it is possible to distinguish relevant proteins having a large number of edges called “central hubs”, such as 14-3-3ε, α1-antitrypsin, glutathioneS-transferaseP,albumin and APOA1. Transcriptional factors c-Myc, AP1, PPARγ and α, NF-kB, androgen receptor and other interesting proteins, such MIF or proteases directly inhibited by A1AT, were network proteins predicted by the software and included from literature databases. In addition, MetaCore network highlighted four well-characterized or “canonical” pathways (bold blue lines in the figure) that included PI3K/Akt/mTor signalling, MAP kinase pathway, hypoxia response and pluripotency-associated transcriptional factors. The results of enrichment analysis obtained using the system biology platform GeneGo by MetaCore are reported in Fig.S1 as Disease Biomarker Networks, Process Networks and Pathway Maps. Disease Biomarker Networks produce a histogram with other Th1 pathologies related to our differentially expressed proteins, such as tuberculosis (TB) and chronic obstructive pulmonary disease (COPD). Protein networks involved in TB and pulmonary disease development are highlighted in Fig.S1 where proteins identified by us are shown with red circles. In our sarcoidosis research, Process Network pointed out information related to disease pathogenesis; in particular, many proteins identified by us seem to be involved in inflammation due to the complement system, innate inflammatory response and IL-6 signalling. Iron transport, immunological response and blood coagulation may also be involved in Process Networks.

4. Discussion In this study BAL features from a group of active chronic pulmonary sarcoidosis patients and SC and NSC were investigated and specific protein profiles and pathogenetic mechanisms characteristic of sarcoidosis were described. Novel potential biomarkers of active chronic sarcoidosis were evaluated as well as the effect of cigarette smoking on BAL protein composition in patients and controls. 4.1. Population — BAL cells and serological biomarkers The results of lung function tests, serum ACE concentrations, chitotriosidase levels in blood, lysozyme activity, electrophoretic and 2D-image analysis of BAL samples from our Sar patients corresponded to those usually reported for chronic persistent sarcoidosis such as increased lymphocyte percentages in the BAL fluid, a CD4/CD8 ratio N2.5 in patients and a slight increase in BAL alveolar macrophages in healthy smoker controls [1,13,14]. As expected, chitotriosidase and ACE levels were higher than normal in the selected subgroup of Sar patients selected for the proteomic study [13,14].

380

MASCOT search results No spot

Protein name

MetaCore name/gene name

AC

1 2 3

4

Experimental pI/MW (KDa)

Theoretical pI/MW (KDa)

14-3-3 protein epsilon Annexin 2 Annexin 3

YWHAE ANXA2 ANXA3

P62258 P07355 P12429

4.59 29,726 6.86 35,441 5.58 32,123

4.63 29,326 7.57 38,808 5.63 36,524

Plastin2

LCP1

P13796

7.76 65,936

5.20 70,815

SFTPA2

Q8IWL1 4.66 32,509

SFTPA2

No. matched peptide

Sequence coverage (%)

Kruskal–Wallis FDR

Kruskal–Wallis statistic Sar-NSC (H-value)

Sar-SC

Nsc-SC

0.0051 0.0007 0.0063

0.009 0.009 0.06

10.57 14.52 10.13

8.617* 9.033* −11.6**

8.354* 13.46*** −5.75

−0.2625 4.425 5.85

0.0002

0.0003

16.91

−8.067

−15.79*** −7.725

Score

7 34 14 44 12 37 GDEEGVPAVVIDMSGLR IGNFSTDIK TENLNDDEK AECMLQQAER GSVSDEEMMELR AYYHLLEQVAPK FSLVGIGGQDLNEGNR

103 209 143

5.07 26,622

8

39

124

0.0026

0.019

11.92

3.667

−9.083

−12.75**

Q8IWL1 4.70 30,263

5.07 26,622

7

39

125

0.0002

0.0013

17.46

7.444

15.82***

8.375

SFTPD

P35247

6.52 43,251

6.97 35,498

TAGFVKPFTEAQLLCTQAGGQLASPR

0.0014

0.009

13.2

−9.344*

−13.44**

−4.1

8 9 10 11 12 13 14 15 16 17 18

Pulmonary surfactant-associated proteinA2 Pulmonary surfactant-associated proteinA2 Pulmonary urfactant-associated protein D Complement C3 alpha chain Complement Factor H Complement Factor H Complement factor B Complement factor I Protein S100-A8 Vitamin D binding protein Retinol-binding protein 4 Peroxiredoxin-1 Glutathione S-transferase P Glutathione S-transferase P

C3 CFH CFH CFB CFI S100A8 GC RBP4 PRDX1 GSTP1 GSTP1

P01024 P08603 P08603 P00751 P05156 P05109 P02774 P02753 Q06830 P09211 P09211

6.65 65,936 5.46 192,008 5.55 189,304 6.44 58,474 5.59 53,118 6.73 10,558 5.10 55,140 5.25 19,479 4.83 40,871 5.24 22,660 5.53 22,492

6.02 188,569 6.21 143,680 6.21 143,680 6.67 86,847 7.38 62,487 6.51 10,885 5.40 54,526 5.76 23,337 8.27 22,324 5.43 23,569 5.43 23,569

0.0007 0.0079 0.0007 p b 0.0001 0.0119 0.0137 0.0036 p b 0.0001 0.0004 0.0002 0.0004

0.003 0.034 0.002 3.89E−05 0.044 0.048 0.007 1.9E−05 0.0006 0.0003 0.0006

14.63 9.683 14.4 21.92 8.863 8.584 11.28 21.74 15.79 17.34 15.5

9.2* 9.028* 12.96*** 13.6*** 0.5222 8.522 1.089 16.5*** 4.572 3.167 −13.44***

−4.75 −0.2222 4.424 13.38*** 10.22* 10.35* 11.76** 6.375 −10.03* 15.17*** −11.69**

−13.95*** −9.25* −8.538 −0.225 9.7* 1.825 10.68* −10.13* −14.6*** 12** 1.75

19

Cystatin-B

CSTB

P04080

7.44 16,173

6.96 11,139

22 21 15 16 11 11 14 21 HGNTDSEGIVEVK 7 68 9 25 9 45 6 30 6 39 6 48 VHVGDEDFVHLR SQLEEKENK SQVVAGTNYFIK VFQSLPHENKPLTLSNYQTNK

0.0051

0.008

10.57

8.617*

8.354*

−0.2625

5

6

7

176 119 82 135 123 119 114 100 101 100

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

Table2 Table reporting quantitative protein differences. Protein numbers correspond to the circled spots in the reference gel map (Fig. 1). Protein names, MetaCore/gene names, accession numbers, experimental and theoretical pI and MW are reported. Spots were identified using MALDI-ToF-MS and LC–ESI-IT-MS2 (if required). MASCOT search results (score, number of matched peptides, sequence coverage) are showed. Peptide sequence is reported if required LC–MS/MS identification. Moreover, Kruskal–Wallis, Kruskal–Wallis statistic and False Discovery Rate (FDR) results are showed. Dunn's test p-values are reported (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001) together with the significant fold change (values in bold) in each comparison (Sar-NSC, Sar-SC, NSC-SC).

P22897

5.47 194,750

6.08 164,120

LYZ SERPINA1 SERPINA1 IGHA2

P61626 P01009 P01009 P01877

9.22 11,572 4.85 49,029 4.78 53,950 5.20 62,676

9.38 16,982 5.37 46,878 5.37 46,878 5.71 37,301

27

Immunoglobulin J chain

IGJ

P01591

5.38 196,136

4.62 15,594

28

Alpha-2-HS-glycoprotein

AHSG

P02765

4.60 54,700

5.43 40,098

29

Alpha-2-HS-glycoprotein

AHSG

P02765

4.57 55,140

5.43 40,098

30

Alpha-2-HS-glycoprotein

AHSG

P02765

5.14 62,176

5.43 40,098

32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Antithrombin-III Apolipoprotein AI Apolipoprotein AI Apolipoprotein AI Apolipoprotein AI Apolipoprotein AI Apolipoprotein AI Haptoglobin Haptoglobin Haptoglobin Serotransferrin Transthyretin Fatty acid-binding protein Zinc-alpha-2-glycoprotein Zinc-alpha-2-glycoprotein Selenium-binding protein

SERPINC1 APOA1 APOA1 APOA1 APOA1 APOA1 APOA1 HP HP HP TF TTR FABP4 AZGP1 AZGP1 SELENBP1

P01008 P02647 P02647 P02647 P02647 P02647 P02647 P00738 P00738 P00738 P02787 P02766 P15090 P25311 P25311 Q13228

5.20 56,783 5.18 21,941 5.03 22,270 5.08 21,941 5.11 24,633 5.21 23,069 5.10 22,886 5.31 41,129 5.67 16,457 4.49 48,510 6.28 61,352 5.52 13,766 6.40 11,833 4.89 41,562 4.83 41,129 6.04 56,783

6.32 53,025 5.56 30,760 5.56 30,760 5.56 30,760 5.56 30,760 5.56 30,760 5.56 30,760 6.13 45,861 6.13 45,861 6.13 45,861 6.81 79,296 5.52 15,991 6.59 14,824 5.71 34,465 5.71 34,465 5.93 52,928

49

Pancreatic alpha-amylase

AMY2A

P04746

6.26 37,426

6.45 55,887

50

Beta-2 microglobulin Polymeric immunoglobulin receptor Ceruloplasmin Albumin

B2M

P61769

6.03 11,124

6.06 13,820

GEPSHENNR EKETMDNAR 8 39 13 38 8 22 6 23 SSEDPNEDIVER FVYHLSDLCK IVLVDNK 5 18 HTLNQIDEVK FSVVYAK CNLLAEK EHAVEGDCDFQLLK HTFMGVVSLGSPSGEVSHPR HTLNQIDEVK EHAVEGDCDFQLLK 13 38 14 45 8 29 10 36 9 32 19 53 14 46 6 16 6 19 9 21 23 35 7 64 6 59 8 39 9 39 10 27 TGSGDIENYNDATQVR SGNEDEFR SNFLNCYVSGFHPSDIEVDLLK

PIGR

P01833

5.05 88,185

5.58 84,429

14

CP ALB

P00450 P02768

5.05 132,119 5.50 40,871

5.44 122,983 5.92 71,317

7 8 VPQVSTPTLVEVSR

51 52 53

23

6.761

−1.326

−8.088*

6.003 14.69 11.7 15.57

−2.994 −0.6556 0.5667 1.067

−7.882* 12.44** 11.67** −12.58**

−4.888 13.1** 11.1** −13.65***

11.67

6.922

12.35**

5.425

9.956*

9.681*

−0.275

7.156

13.18**

6.025

4.93E−05 21.54

16.39***

6.139

−10.25*

0.0002 0.0002 0.0086 p b 0.0001 0.0043 0.0033 0.001 0.002 0.0004 0.0026 0.0048 0.0005 0.0033 0.0233 0.0017 0.0023

0.0006 0.0006 0.05 2.85E−05 0.01 0.009 0.003 0.005 0.0009 0.006 0.01 0.001 0.007 0.05 0.003 0.004

17.15 16.92 9.52 18.87 10.9 11.45 13.91 12.39 15.53 11.91 10.7 15.34 11.43 7.515 12.71 12.12

−3.378 14.08*** −3.122 15.29*** 12.04** 12.28** 12.2*** −6.867 3.089 −2.178 7.611 12.24** −9.039** 9.289* 12.07** −0.04444

11.72** 8.653 8.215 7.514 6.514 7.528 8.375 6.333 14.33*** 10.22* 11.11** 13.19** −0.1389 8.264 10.79* −11.57**

15.1*** −5.425 11.34** −7.775 −5.525 −4.75 −3.825 13.2** 11.24** 12.4** 3.5 0.95 8.9* −1.025 −1.275 −11.53**

0.0021

0.007

12.35

−11.03**

−10.96**

0.075

0.0001

0.0001

18.41

15.62***

8.222

−7.4

151

0.0144

0.02

7.189

−2.611

−9.8*

92

p b 0.0001 0.0051

1.61E−05 21.27 0.008 10.54

16.06*** −0.3

5.431 10.5*

−10.63* 10.8*

120 184 111 78

73

155 167 112 137 103 232 178 76 94 104 263 141 110 114 124 110

0.0298

0.04

7.028

0.0497 0.0006 0.0029 0.0004

0.06 0.01 0.04 0.004

0.0029

0.02

0.0097

0.05

0.0025

0.01

p b 0.0001

9.271

11.97

8.482

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

MRC1

21 24 25 26

Macrophage mannose receptor 1 Lysozyme C Alpha 1 antitrypsin Alpha 1 antitrypsin Ig alpha-2 chain C region

20

381

382

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

Fig.1. BAL Master gel reporting differentially expressed spots (circles) among sarcoidosis (Sar), non-smoker (NSC) and smoker controls (SC).

Fig.2. Principal Component Analysis (PCA). (A)Graph showing samples distribution by spot expression profile. (B)Graph of correlation matrix eigenvalues for significant principal components.

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

383

Fig.3. Network obtained by MetaCore analysis. 14-3-3 epsilon, alpha 1-antitrypsin, GSTP1, albumin, and APOA1 are central hubs. Legend of network symbols is also reported.

4.2. 2DE and MetaCore Proteomic analysis of BAL fluid allowed a characterization of proteins in the airway environment. Although most differentially expressed proteins have major immune-regulatory roles, others seem to have variegated functions ranging from lipid and vitamin metabolism, to mineral homeostasis, oxidative stress defence, cell motility, vesicle transport and lipid raft arrangement. Among proteins differently expressed in Sar compared to controls, ANXA3 and plastin 2 were also validated by Western blot analysis in a different population of patients and controls. ANXA3 is a member of the annexin family, down-regulated in Sar with respect to NSC. Interacting with phospholipid membranes in a Ca2+-dependent manner, ANXA3 promotes membrane trafficking, removal of damaged cells by phagocytic cells and intracellular membrane and granule fusion, as well as regulating membrane stabilization/permeabilization, exocytosis, leukocyte migration and inflammatory response [25–28]. Plastin 2 proved to be down-regulated in Sar patients compared to smoker controls in 2DE as well as in Western blot analysis. Interestingly, Landi etal. (2013) [8] identified plastin 2 in the BAL proteome and found its down-regulation in another interstitial lung disease: Pulmonary Langerhans Cell's Histiocytosis. Plastin-2 is a cytoplasmic globular actin-bundling protein that links two actin filaments in parallel and is mechanically involved in F-actin structural organization in immunological synapses, lamellipodia and filopodia (i.e. it is functionally related to leukocytes maturation, adhesion, polarity, motility, chemotaxis and activation) [29]. As cells expressing plastin 2 gene have a

postulated role against TNF-α cytotoxicity, the low plastin-2BAL fluid level may contribute to a TNF-α sensitive environment in the Saraffected lung [30]. Moreover, being involved in cytoskeletal regulation and lipid transport, plastin-2 contributes with membrane receptors (such as CCR6) and structural proteins to mechanical support of lipid raft domains. Alteration of lipid metabolism in the pathogenetic mechanisms occurring in Sar is an interesting research topic emerging from the present study, since several proteins involved in lipid metabolism were found to be differently expressed in patients and controls. 4.3. Proteins involved in lipid metabolism Fatty acid binding protein 4 (FABP4), a protein related to immunity and metabolism of fatty acids, retinoic acid and glucose was found to be down-regulated in Sar and SC compared to NSC. This cytoplasmic transport protein involved in regulation of cholesterol trafficking and inhibition of inflammatory activity, binding several hydrophobic molecules related to macrophage activation, is expressed by adipocytes, macrophages and endothelial cells. Macrophages deficient in FABP4 show enhanced PPARγ (peroxisome proliferator-activated receptor γ) activity, LDL uptake and cholesterol efflux, as well as reduced release of inflammatory cytokines (such as TNFα, MCP1, and IL-1) [31,32]. Our results showed two spots up-regulated in Sar patients compared to controls, namely, the Zinc-alpha-2 glycoprotein (ZAG). ZAG is another adipokine involved in lipid mobilization and lipolysisin adipocytes [33,34]. Indeed, high levels of ZAG are in line with the abovementioned

384

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

Fig.4. Western blot validation of annexin A3 (A), plastin 2 (B)and 14-3-3 epsilon (C). For each protein are reported the immune-detection image and the bands visualized on nitrocellulose membrane stained with Ponceau S reagent used for normalization step.1D WB histograms show mean (± standard deviation) of the relative integrated densities of immune-stained bands normalized to the Ponceau S bands. Statistical significance was analysed by the Mann–Whitney test:*p ≤ 0.05; **p ≤ 0.01. Moreover, proteomic trends are also shown (silver). Histograms report spot relative volume percentage on the y-axis and the groups (Sar, SC and NSC) on the x-axis. Dunn's test p-values are reported: *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001.

increase in Sar of serum concentrations of triglycerides [35,36]. ZAG is thought to have additional functions, such as anti-inflammatory and anti-viral effects [37] and a possible involvement in immune responses. ZAG could be considered a non-conventionalMHC class I molecule, because it has high sequence and structural similarity to MHC-I and the ability to bind and present lipid molecules to T lymphocytes [38].

There is no data on the potential involvement of this interesting protein in Sar. RBP4 is considered an adipocytokine since RBP4-vitaminA complexes inhibit insulin responses by interacting with STRA6 (stimulated by Retinoic Acid 6)on the skeletal muscle cell surface and by promoting gluconeogenesis in liver cells [39,40]. RBP4 is involved in fibrotic

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

mechanisms and angiogenesis and resulted over-expressed in Sar compared to NSC. It has been demonstrated invitro that RBP4 can induce the production of vasodilator NO, can inhibit the endothelin secretion in endothelial cells and that its macrophage expression can be suppressed by activation of Toll-like receptor 4 [41,42]. Alpha-2-HS-glycoprotein(AHSG) is an extracellular glycoprotein of the cystatin superfamily, secreted by hepatocytes in blood circulation. It is involved in lipid metabolism and regulation of mineralization, insulin-resistance, immune response and cell adhesion [43,44]. The most interesting function of AHSG is the regulation of mineral homeostasis by segregation in soluble mineral–protein complexes of calcium and phosphate molecules, inhibiting mineralization of connective tissues [45]. Degradation of these complexes involves two proteins that we found to be increased in Sar patients (ANXA2 and APOA1) [46–48]. No data is available on the potential involvement of ASGH in Sar. 4.4. Proteins involved in calcium metabolism A protein greatly over-expressed in Sar compared to controls is ANXA2, a membrane-associated protein expressed by endothelial cells, alveolar type II cells, mononuclear leukocytes and macrophages. It is directly associated with the transmembrane CD44 receptor on the sub-membrane zone in lipid rafts, where it acts as connector with the cytoskeleton and as organizer of lipid domains [49]. Functions of ANXA2 extend to induction of membrane fusion, promotion of exocytosis, surfactant secretion, and involvement in inflammatory events (exogenous administration of ANXA2 has been demonstrated to activate macrophages via NF-kB and MAPK), triggering fibrinolysis, wound repair, cell proliferation, cytoskeletal rearrangement, differentiation and apoptosis [50,51]. Several stimuli (including INFγ, IL-6, or oxidative stress) are able to regulate ANXA2 function and cell localization, probably through modifications of phosphorylation state [52]. The main regulatory mechanism of ANXA2 activation is calcium-dependent, although there is evidence of regulation based on cholesterol levels [53]. Sarcoidosis is a systemic disease that modifies vitamin D, calcium and calcitriol metabolism [54]. In active disease patients, low serum levels of 25OH-D and increased calciuria have been described [55–58]. Interestingly, ANXA2 seems to be the direct membrane receptor for calcitriol (or vitamin D3) and evidences suggest that calcitriol may transcriptionally regulate ANXA2 (the Anxa2 promoter region contains a vitamin-D binding site). ANXA2 participates in the calcitriol transduction pathway, enhancing effects of calcitriol [58–63]. Vitamin D is related to Sar pathogenesis because through INFγ induction, granuloma macrophages express CYP27B1 that converts vitamin D to calcitriol. Calcitriol induces transformation of circulating monocytes into multinucleated giant cells and epithelioid cells involved in granuloma formation. The expression of vitamin D receptors in macrophages suggests a possible autocrine/paracrine loop [64]. Oddly, calcitriol suppresses Apo A-I production at transcriptional level in cells expressing vitamin D receptors [65]. Even so, serum levels of calcitriol do not change between Sar and healthy subjects and this molecule is currently not detectable in BAL fluid [66]. In this study, we identified vitamin D binding protein (DBP), the major carrier of several vitamin D metabolites. Although the ANXA2 expression did not significantly change in Sar patients compared to the NSC group, our results confirm that cigarette smoke induces its down-regulation[67]. VTDB binds the CD44 receptor and ANXA2 on neutrophil and leukocyte surfaces, promoting chemotaxis in the presence of complement fragments [68,69]. 4.5. PCA and smoke related BAL proteins Proteomic analysis identified several differently expressed spots among the three groups of subjects and through mass spectrometry, 34 unique proteins were unambiguously identified. The PCA analysis showed that Sar, smoker and non-smoker control gel maps were clustered into three distinct groups, highlighting the consistent reproducibility of biological samples (Fig.2A). SC and Sar cohorts had lower

385

intra-group variance than NSCs. The first component PC1 separated Sar from SC protein profile. The smoking attitude resulted in differently expressed plastin 2, surfactant A2, complement factor I, protein S100A8, peroxiredoxin 1, VTDB, lysozyme C, alpha 1 antitrypsin, Ig alpha 2 chain C region, immunoglobulin J chain, antithrombin III, haptoglobin, serotransferrin, selenium binding protein and albumin. On contributing to a better knowledge of the effects of tobacco smoke, our results indicate that the protein profile in Sar patients shares common features with healthy NSCs. The effects of cigarette smoke in Sar pathogenesis are largely unexplored. Epidemiological data of a higher incidence of pathology in NSC patients [7,10] could lead to believe in a protective role of the cigarette smoke in the disease onset. Smoking exposure may induce the development of different lung diseases including ILD and high similarities between BAL protein profiles and PCA from SC and IPF with respect to NSC groups have been reported [7,70]. Anyhow, the exposure to cigarette smoke induces a change in the pattern of protein expression in BAL such as a downregulation of α1-antitrypsin, consequent to oxidative stress damage and to consumption related to the elastase hyperactivity or proteins involved in oxidative stress (GSTP1, PRDX1, ALBU, APOA1) [71]. 4.6. Pathway analysis The differential protein pattern was analysed to extrapolate the singular protein function, Process Networks, Pathway's Maps and possible diseases where these proteins may act as possible biomarker. The network built among the identified proteins allowed to visualize possible protein interactions (Fig. 3) and the action of other interesting molecules such as transcriptional factors (not always identifiable by proteomic analysis). The network analysis highlighted five “central hubs”, proteins with a higher number of edges with other proteins of the net (14-3-3ε, α1-antitrypsin, glutathione-S-transferaseP, albumin, APOA1). The pleiotropic protein 14-3-3ε was up-regulated in Sar compared to both control groups (also validated by WB analysis). It is a multifunctional adapter protein that is able to recognize phosphorylated motifs with modulating activity. MetaCore net instantaneously visualized interactions among 14-3-3ε and multiple transcriptional factors (NF-AT4, HSF1, FOXO3A, E2F1, p53, AP1 and PPAR β and γ and phosphatase such as AKT, histone deacetylase class II, protein kinase C), playing a crucial role in the regulation of cellular localization of several proteins potentially involved in ILD pathogenesis including MAPK pathway members (as Raf-1), cytoskeletal proteins (as actin, tropomyosin and tubulin), and heat shock proteins [72]. Another “central hub” is glutathione S transferase P (GSTP), a protein involved in oxidative stress defence[73] that results down-regulated in BAL of Sar patients compared to both controls, in line with previous findings [74]. Members of the GSTP family are known to play a role in the pathogenesis of different inflammatory diseases such as asthma, cancer, rheumatoid arthritis. Malli etal.(2013) suggested that the downregulation of GSTP1 in BAL from Sar patients may be due to an extra consumption to counterbalance the high ROS levels in Sar pathology [75]. Apolipoprotein AI is involved in lipid transport and five isoforms resulted significantly up-regulated in Sar compared to NSC. Apo AI has an anti-inflammatory role preventing collagen deposition, stopping neutrophil and macrophage activation. It can also remove LDL particles from foamy macrophages reversing the cell phenotype and thus inhibiting release of inflammatory cytokines [76,77]. The increase of Apo A-I concentration in Sar BALF may be an effort to contain the foamy macrophage accumulation in sarcoid granuloma zones. Another possible explanation of enhanced levels of Apo-AI should be related to the presence of anomalous lipid droplets that are described in the alveoli and in bronchi of Sar. These droplets seem to be originated from undigested lysosomes granules [78,79]. Process Network as well as Pathway Maps underline a crucial role of proteins involved in immune response and in inflammation induced by complement system, innate inflammatory response and IL-6signalling. IL-6 has been recently studied in a population of

386

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387

pulmonary sarcoidosis in association with TNF-alpha and Toll like receptor 2/6 [80]. Two interesting pathways that emerge in our study are Fetransport and blood coagulation. These potential pathogenetic mechanisms are unexplored for sarcoidosis while they are all hot research topics for studying different ILDs such as IPF[81]. Regarding the enrichment analysis, it was possible to extrapolate new information about the “Disease Biomarker Network”. This analysis perceives diseaserelevant proteins and their frequency across human diseases and in our study pointed out biomarker characteristic of Sar and COPD in BAL of Sar patients such as pulmonary surfactant associated protein A2 and D, alpha 1 antitrypsin, and vitamin D binding protein. Other potential biomarkers were able to distinguish Sar from other chronic granulomatous diseases, such as TB [79]. These two lung diseases are oftenin differential diagnosis and share several common pathogenetic features, including a suggested potential etiogenic role of Mycobacterium tuberculosis antigens in the development of sarcoidosis [83]. Considering the common pathways between Sar and TB, Moller (2007) detected in sarcoidosis tissues mKatG, a mycobacterial protein that was proposed as a novel pathogenic antigen candidate [84]. More recently Dubaniewicz and co-workers (2013) suggested that different genetic predispositions to mycobacterial host diverge the two similar pathologies; the high risk of developing the one or the other disease is probably based on the HLA or non-HLA alleles [85]. Proteins evidenced by enrichment analysis, characteristic of pulmonary TB, include haptoglobin, pulmonary surfactant associated protein A2, transferrin, macrophage mannose receptor I and complement factor C3. These intriguing results suggest to extend our research to comparative proteomic analysis of BAL from sarcoidosis and TB patients to evidence differences and similarities of these two granulomatous lung diseases. Potential protein biomarkers, characteristic of Sar and COPD in BAL of Sar patients, are pulmonary surfactant associated protein A2 and D, alpha 1 antitrypsin, and vitamin D binding protein. In this study the combination of proteomic results with a systems biology platform enabled us to amplify the information obtained, processing the results and identifying the principal pathways involved in the disease. MetaCore analysis was performed to extract new insights, hypotheses and emerging properties from the proteomic data suggesting that proteins of interest may represent potential target of novel therapies for sarcoidosis. PCA analysis highlighted high reproducibility of biological samples from the three groups. The MetaCore and PCAresults suggested that no-smoker controls and sarcoidosis protein profiles had several similarities with respect to the SC group, excluding an etiological role of smoking in the development of sarcoidosis.

5. Conclusion Proteomic analysis of BAL from patients with chronic active sarcoidosis and smoker and no-smoker controls allowed us to identify a large number of differential expressed proteins involved in relevant biological functions including regulation of lipid and calcium metabolisms, inflammation, immune response and iron transport. The PCA confirmed the high reproducibility of biological samples and differential pattern profiles primarily in Sar than SC (sarcoidosis shared common features with NSC). The MetaCore and PCA results highlighted that sarcoidosis shares common protein profiles with NSC. MetaCore analysis was done to extract novel information and emerging hypotheses from the proteomic data. Interesting central functional hubs, worthy of further studies and potentially associated with Sar development, emerged. Disease Biomarker Network revealed several common features between Sar and TB, exhorting to orientate the future proteomics investigations also in comparative BALF analysis of Sar and TB. In conclusion, this work deepened the BALF proteomics of active Sar in order to broaden the related pathogenic mechanisms and, in particular, pointed out several interesting signalling pathways that include both lipid, mineral, and vitamin D metabolism, and immune-regulation of macrophage function.

Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.jprot.2015.08.012. Conflict of interest We have no conflict of interest. References [1] P. Rottoli, B. Magi, M.G. Perari, et al., Cytokine profile and proteome analysis in bronchoalveolar lavage of patients with sarcoidosis, pulmonary fibrosis associated with systemic sclerosis and idiopathic pulmonary fibrosis, Proteomics 5 (5) (2005) 1423–1430. [2] V. Jain, S. Hasselquist, M.D. Delaney, PET scanning in sarcoidosis, Ann. N. Y. Acad. Sci. 1228 (2011) 46–58. [3] K.C. Patterson, M.E. Strek, Pulmonary fibrosis in sarcoidosis. Clinical features and outcomes, Ann. Am. Thorac. Soc. 10 (4) (2013) 362–370. [4] D. Leng, C. Huan, T. Xie, et al., Meta-analysis of genetic programs between idiopathic pulmonary fibrosis and sarcoidosis, PLoS One 8 (8) (2013) e71059 (14). [5] Y. Zhang, X. Chen, Y. Hu, et al., Preliminary characterizations of a serum biomarker for sarcoidosis by comparative proteomic approach with tandem-mass spectrometry in ethnic Han Chinese patients, Respir. Res. 14 (2013) 18 (11). [6] T. Okamoto, Y. Miyazaki, R. Shirahama, M. Tamaoka, N. Inase, Proteome analysis of bronchoalveolar lavage fluid in chronic hypersensitivity pneumonitis, Allergol. Int. 61 (1) (2012) 83–92. [7] C. Landi, E. Bargagli, L. Bianchi, et al., Towards a functional proteomics approach to the comprehension of idiopathic pulmonary fibrosis, sarcoidosis, systemic sclerosis and pulmonary Langerhans cell histiocytosis, J. Proteome 27 (83) (2013) 60–75. [8] C. Landi, E. Bargagli, B. Magi, et al., Proteome analysis of bronchoalveolar lavage in pulmonary Langerhans cell histiocytosis, J. Clin. Bioinforma. 10 (1) (2011) 31. [9] R. Vassallo, Diffuse lung diseases in cigarette smokers, Semin. Respir. Crit. Care Med. 33 (5) (2012) 533–542. [10] D. Gupta, A.D. Singh, R. Agarwal, A.N. Aggarwal, K. Joshi, S.K. Jindal, Is tobacco smoking protective for sarcoidosis? A case–control study from North India, Sarcoidosis Vasc. Diffuse Lung Dis. 27 (1) (2010) 19–26. [11] P. Rottoli, B. Magi, R. Cianti, et al., Carbonylated proteins in BAL of patients with sarcoidosis, pulmonary fibrosis associated with systemic sclerosis and idiopathic pulmonary fibrosis, Proteomics 5 (10) (2005) 2612–2618. [12] G.S. Sainani, V. Mahbubani, V. Trikannad, Serum angiotensin converting enzyme activity in sarcoidosis and pulmonary tuberculosis, J. Assoc. Physicians India 44 (1) (1996) 29–30. [13] S. Grosso, M.A. Margollicci, E. Bargagli, et al., Serum levels of chitotriosidase as a marker of disease activity and clinical stage in sarcoidosis, Scand. J. Clin. Lab. Invest. 64 (2004) 57–62. [14] E. Bargagli, M.A. Margollicci, A. Perrone, et al., Chitotriosidase analysis in bronchoalveolar lavage of patients with sarcoidosis, Sarcoidosis Vasc. Diffuse Lung Dis. 24 (2007) 59–64. [15] A. Ng, M. Heynen, D. Luensmann, L.N. Subbaraman, L. Jones, Optimization of a fluorescence-based lysozyme activity assay for contact lens studies, Curr. Eye Res. 38 (2) (2013) 252–259. [16] M.M. Bradford, A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding, Anal. Biochem. 72 (1976) 248–254. [17] B. Bjellqvist, C. Pasquali, F. Ravier, J.C. Sanchez, D. Hochstrasser, A nonlinear widerange immobilized pH gradient for two-dimensional electrophoresis and its definition in a relevant pH scale, Electrophoresis 14 (1993) 1357–1365. [18] D.F. Hochstrasser, M.G. Harrington, A.C. Hochstrasser, M.J. Miller, C.R. Merril, Methods for increasing the resolution of two-dimensional protein electrophoresis, Anal. Biochem. 173 (2) (1988) 424–435. [19] D.F. Hochstrasser, A. Patchornik, C.R. Merril, Development of polyacrylamide gels that improve the separation of proteins and their detection by silver staining, Anal. Biochem. 173 (1988) 412–423. [20] B.R. Oakley, D.R. Kirsch, N.R. Morris, A simplified ultrasensitive silver stain for detecting proteins in polyacrylamide gels, Anal. Biochem. 105 (1980) 361–363. [21] H.D. Meiring, E. Van der Heeft, G.J. Ten Hove, A. De Jong, Nanoscale LC–MS(n)technical design and applications to peptide and protein analysis, J. Sep. Sci. 25 (2002) 557–568. [22] M. Minter, J. Towbin, J. Harter, E.R. McCabe, Enzyme product blot for nondestructive assay of protein catalytic function in polyacrylamide gels, Anal. Biochem. 178 (1) (1989) 22–26. [23] S.R. Gallagher, One-dimensionalSDS, gel electrophoresis of proteins, Curr. Protoc. Protein Sci. 10 (2012) 44. [24] M.W. Ziegenhagen, M.E. Rothe, M. Schlaak, J. Müller-Quernheim, Bronchoalveolar and serological parameters reflecting the severity of sarcoidosis, Eur. Respir. J. 21 (3) (2003) 407–413. [25] K.W. Chong, M.J. Chen, E.S.C. Koay, et al., Annexin A3 is associated with cell death in lactacystin-mediated neuronal injury, Neurosci. Lett. 485 (2010) 129–133. [26] D. Faugaret, F.C. Chouinard, D. Harbour, M.A. El Azreq, S.G. Bourgoin, An essential role for phospholipase D in the recruitment of vesicle amine transport protein-1 to membranes in human neutrophils, Biochem. Pharmacol. 81 (2011) 144–156. [27] W. Osman, Y. Okada, Y. Kamatani, M. Kubo, K. Matsuda, Y. Nakamura, Association of common variants in TNFRSF13B, TNFSF13, and ANXA3 with serum levels of nonalbumin protein and immunoglobulin isotypes in Japanese, PLoS One 7 (4) (2012) 32,683.

C. Landi et al. / Journal of Proteomics 128 (2015) 375–387 [28] L.M. Teles, E.N. Aquino, A.C. Neves, et al., Comparison of the neutrophil proteome in trauma patients and normal controls, Protein Pept. Lett. 19 (6) (2012) 663–672. [29] T. Miyakawa, H. Shinomiya, F. Yumoto, et al., Different Ca2+-sensitivities between the EF-hands of T- and L-plastins, Biochem. Biophys. Res. Commun. 429 (2012) 137–141. [30] B. Janji, L. Vallar, Z. Al Tanoury, et al., The actin filament cross-linkerL-plastin confers resistance to TNF-α in MCF-7 breast cancer cells in a phosphorylation-dependent manner, J. Cell. Mol. Med. 14 (6A) (2010) 1264–1275. [31] L. Makowski, K.C. Brittingham, J.M. Reynolds, J. Suttles, G.S. Hotamisligil, The fatty acid-binding protein, aP2, coordinates macrophage cholesterol trafficking and inflammatory activity: macrophage expression of aP2 impacts peroxisome proliferator-activated receptor γ and IκB kinase activities, J. Biol. Chem. 280 (13) (2005) 12,888–12,895. [32] E. Ghelfi, C. Karaaslan, S. Berkelhamer, S. Akar, H. Kozakewich, S. Cataltepe, Fatty acid-binding proteins and peribronchial angiogenesis in bronchopulmonary dysplasia, Am. J. Respir. Cell Mol. Biol. 45 (2011) 550–556. [33] H. Vanni, A. Kazeros, R. Wang, et al., Cigarette smoking induces overexpression of a fat-depleting gene AZGP1 in the human, Chest 135 (5) (2009) 1197–1208. [34] T. Mracek, D. Gao, T. Tzanavari, et al., Downregulation of zinc-a2-glycoprotein in adipose tissue and liver of obese ob/ob mice and by tumour necrosis factor-a in adipocytes, J. Endocrinol. 204 (2) (2010) 165–172. [35] A. Salazar, J. Maña, X. Pinto, J.M. Argimon, I. Hurtado, R. Pujol, Corticosteroid therapy increases HDL-cholesterol concentrations in patients with active sarcoidosis and hypoalphalipoproteinemia, Clin. Chim. Acta 320 (2002) 59–64. [36] D.C. Yeung, K.S. Lam, Y. Wang, A.W. Tso, A. Xu, Serum Zinc-α2Glycoproteincorrelates with adiposity, triglycerides, and the key components of the metabolic syndrome in Chinese subjects, J. Clin. Endocrinol. Metab. 94 (7) (2009) 2531–2536. [37] V. Ceperuelo-Mallafré, X. Escoté, C. Viladés, et al., Zinc alpha-2 glycoprotein is implicated in dyslipidaemia in HIV-1-infected patients treated with antiretroviral drugs, HIV Med. 13 (5) (2012) 297–303. [38] I. Hassan, A. Waheed, S. Yadav, T.J. Singh, F. Ahmad, Zinc A2-glycoprotein: a multidisciplinary protein, Mol. Cancer Res. 6 (6) (2008) 892–906. [39] L. Langouche, S.V. Perre, J. Frystyk, A. Flyvbjerg, T.K. Hansen, G. Van den Berghe, Adiponectin, retinol-binding protein 4, and leptin in protracted critical illness of pulmonary origin, Crit. Care 13 (4) (2009) 112. [40] M. Rosell, E. Hondares, S. Iwamoto, et al., Peroxisome proliferator-activatedreceptors- α and -γ, and cAMP-mediated pathways, control retinol-bindingprotein-4 gene expression in brown adipose tissue, Endocrinology 153 (3) (2012) 1162–1173. [41] A.M. Mondul, K. Yu, W. Wheeler, et al., Genome-wide association study of circulating retinol levels, Hum. Mol. Genet. 20 (23) (2011) 4724–4731. [42] T. Toyama, Y. Asano, T. Takahashi, et al., Clinical significance of serum retinol binding protein-4 levels in patients with systemic sclerosis, J. Eur. Acad. Dermatol. Venereol. 27 (3) (2013) 337–344. [43] C.J. Yu, C.L. Wang, C.I. Wang, et al., Comprehensive proteome analysis of malignant pleural effusion for lung cancer biomarker discovery by using multidimensional protein identification technology, J. Proteome Res. 10 (2011) 4671–4682. [44] J.W. Choi, H. Liu, R. Mukherjee, J.W. Yun, Downregulation of fetuin-B and zinc-α2glycoprotein is linked to impaired fatty acid metabolism in liver cells, Cell. Physiol. Biochem. 30 (2012) 295–306. [45] Q. Jiang, F. Dibra, M.D. Lee, R. Oldenburg, J. Uitto, Overexpression of fetuin-A counteracts ectopic mineralization in a mouse model of pseudoxanthoma elasticum (Abcc6−/−), J. Investig. Dermatol. 130 (2010) 1288–1296. [46] M.N. Kundranda, S. Ray, M. Saria, et al., Annexins expressed on the cell surface serve as receptors for adhesion to immobilized fetuin-A, Biochim. Biophys. Acta 1693 (2) (2004) 111–123. [47] N.X. Chen, K.D. O'Neill, X. Chen, et al., Fetuin-A uptake in bovine vascular smooth muscle cells is calcium dependent and mediated by annexins, Am. J. Physiol. Ren. Physiol. 292 (2007) 599–606. [48] M. Herrmann, M. Schafer, A. Heiss, et al., Clearance of fetuin-A-containing calciprotein particles is mediated by scavenger receptor-A, Circ. Res. 111 (2012) 575–584. [49] V. Gerke, S.E. Moss, Annexins: from structure to function, Physiol. Rev. 82 (2002) 331–371. [50] D. Gou, A. Mishra, T. Weng, et al., Annexin A2 interactions with Rab14 in alveolar type II cells, J. Biol. Chem. 283 (2008) 13,156–13,164. [51] J.F.A. Swisher, N. Burton, S.M. Bacot, S.N. Vogel, G.M. Feldman, Annexin A2 tetramer activates human and murine macrophages through TLR4, Blood 115 (3) (2010) 549–558. [52] Y.T. Fang, C.F. Lin, C.Y. Wang, R. Anderson, Y.S. Lin, Interferon-g stimulates p11dependent surface expression of annexin A2 in lung epithelial cells to enhance phagocytosis, J. Cell. Physiol. 227 (2012) 2775–2787. [53] N.R. Chintagari, N. Jin, P. Wang, T.A. Narasaraju, J. Chen, L. Liu, Effect of cholesterol depletion on exocytosis of alveolar type II cells, Am. J. Respir. Cell Mol. Biol. 34 (2006) 677–687. [54] P. Rottoli, S. Gonnelli, S. Silitro, et al., Alterations in calcium metabolism and bone mineral density in relation to the activity of sarcoidosis, Sarcoidosis 10 (2) (1993) 161–162. [55] N.J. Sweiss, E.E. Lower, P. Korsten, T.B. Niewold, M.J. Favus, R.P. Baughman, Bone health issues in sarcoidosis, Curr. Rheumatol. Rep. 13 (3) (2011) 265–272. [56] R.P. Baughman, E.E. Lower, Goldilocks, vitamin D and sarcoidosis, Arthritis Res. Ther. 16 (3) (2014) 111.

387

[57] N. Saidenberg-Kermanhac'h, L. Semerano, H. Nunes, et al., Bone fragility in sarcoidosis and relationship with calcium metabolism disorders: a cross sectional study on 142 patients, Arthritis Res. Ther. 16 (2) (2014) R78. [58] S.S. Takahashi, S.V. Reddy, J.M. Chirgwin, et al., Cloning and identification of annexin I1 as an autocrine/paracrine factor that increases osteoclast formation and bone resorption, J. Biol. Chem. 268 (46) (1994) 28,696–28,701. [59] D.T. Baran, J.M. Quail, R. Ray, T. Honeyman, Binding of 1a,25-dihydroxyvitamin D 3 to annexin II: effect of vitamin D metabolites and calcium, J. Cell. Biochem. 80 (2) (2000) 259–265. [60] C. Johansen, K. Kragballe, J. Henningsen, M. Westergaard, K. Kristiansen, L. Iversen, 1α,25-Dihydroxyvitamin D3 stimulates activator protein 1 DNA-binding activity by a phosphatidylinositol 3-kinase/Ras/MEK/extracellular signal regulated kinase 1/2 and c-Jun.N-terminal kinase 1-dependent increase in c-Fos, Fra1, and cJun.Expression in human keratinocytes, J. Investig. Dermatol. 120 (4) (2003) 561–570. [61] M.T. Mizwicki, J.E. Bishop, C.J. Olivera, J. Huhtakangas, A.W. Norman, Evidence that annexin II is not a putative membrane receptor for 1α,25(OH)2-vitamin D3, J. Cell. Biochem. 91 (4) (2004) 852–863. [62] T. Nijenhuis, J.G.J. Hoenderop, R.J.M. Bindels, TRPV5 and TRPV6 in Ca(2+) (re)absorption: regulating Ca(2+) entry at the gate, Pflugers Arch. 451 (2005) 181–192. [63] S.M. Maier, S.J. Scherer, M. Seifert, et al., 1[α],25-Dihydroxyvitamin D(3)enhances annexin II dependent proliferation of osteoblasts, J. Cell. Biochem. 100 (3) (2007) 679–692. [64] A. Maver, I. Medica, B. Peterlin, Search for sarcoidosis candidate genes by integration of data from genomic, transcriptomic and proteomic studies, Med. Sci. Monit. 15 (12) (2009) 22–28. [65] K. Wehmeier, A. Beers, M.J. Haas, et al., Inhibition of apolipoprotein AI gene expression by 1,25-dihydroxyvitamin D3, Biochim. Biophys. Acta 1737 (2005) 16–26. [66] B.P. Barna, D.A. Culver, A. Kanchwala, et al., Alveolar macrophage cathelicidin deficiency in severe sarcoidosis, J. Innate Immun. 4 (2012) 569–578. [67] J.D. Bortner, J.P. Richie, A. Das, et al., Proteomic profiling of human plasma by iTRAQ reveals down-regulation of ITI-HC3 and VDBP by cigarette smoking, J. Proteome Res. 10 (2011) 1151–1159. [68] L.A. McVoy, R.R. Kew, CD44 and annexin A2 mediate the C5a chemotactic cofactor function of the vitamin D binding protein, J. Immunol. 175 (7) (2005) 4754–4760. [69] M. Speeckaert, G. Huang, J.R. Delanghe, Y.E.C. Taes, Biological and clinical aspects of the vitamin D binding protein (Gc-globulin) and its polymorphism, Clin. Chim. Acta 372 (2006) 33–42. [70] C. Landi, E. Bargagli, A. Carleo, et al., A system biology study of BALF from patients affected by idiopathic pulmonary fibrosis (IPF) and healthy controls, Proteomics Clin. Appl. 8 (11–12) (2014) 932–950. [71] P. Rottoli, B. Magi, R. Cianti, et al., Carbonylated proteins in bronchoalveolar lavage of patients with sarcoidosis, pulmonary fibrosis associated with systemic sclerosis and idiopathic pulmonary fibrosis, Proteomics 5 (10) (2005) 2612–2618. [72] A. Conti, C. Sueur, J. Lupo, et al., Interaction of ubinuclein-1, a nuclear and adhesion junction protein, with the 14-3-3 epsilon protein in epithelial cells: implication of the PKA pathway, Eur. J. Cell Biol. 92 (3) (2013) 105–111. [73] A. Maver, I. Medica, B. Salobir, M. Tercelj, B. Peterlin, Lack of association of immuneresponse-gene polymorphisms with susceptibility to sarcoidosis in Slovenian patients, Genet. Mol. Res. 9 (1) (2010) 58–68. [74] A.A. Fryer, A. Bianco, M. Hepple, P.W. Jones, R.C. Strange, M.A. Spiteri, Polymorphism at the glutathione S-transferase GSTP1 locus. A new marker for bronchial hyperresponsiveness and asthma, Am. J. Respir. Crit. Care Med. 161 (5) (2000) 1437–1442. [75] F. Malli, F. Bardaka, I. Tsilioni, E. Karetsi, K.I. Gourgoulianis, Z. Daniil, 8-Isoprostane levels in serum and bronchoalveolar lavage in idiopathic pulmonary fibrosis and sarcoidosis, Food Chem. Toxicol. 61 (2013) 160–163. [76] Y. Ahmad, D. Shukla, I. Garg, et al., Identification of haptoglobin and apolipoprotein A-I as biomarkers for high altitude pulmonary edema, Funct. Integr. Genomics 11 (2011) 407–417. [77] Y. Uehara, S. Ando, E. Yahiro, et al., FAMP, a novel ApoA-I mimetic peptide, suppresses aortic plaque formation through promotion of biological HDL function in ApoEdeficient mice, J. Am. Heart Assoc. 2 (3) (2013) 48. [78] I. Mochizuki, K. Kubo, T. Honda, Relationship between mitochondria and the development of specific lipid droplets in capillary endothelial cells of the respiratory tract in patients with sarcoidosis, Mitochondrion 11 (2011) 601–606. [79] E. Bargagli, B. Magi, C. Olivieri, N. Bianchi, C. Landi, P. Rottoli, Analysis of serum amyloid A in sarcoidosis patients, Respir. Med. 105 (5) (2011) 775–780. [80] M.I. Gabrilovich, J. Walrath, J. van Lunteren, et al., Disordered Toll-like receptor 2 responses in the pathogenesis of pulmonary sarcoidosis, Clin. Exp. Immunol. 173 (3) (2013) 512–522. [81] P. Rogliani, M. Mura, M. Assunta Porretta, C. Saltini, New perspectives in the treatment of idiopathic pulmonary fibrosis, Ther. Adv. Respir. Dis. 2 (2) (2008) 75–93. [83] E. Mortaz, M.R. Masjedi, P. Tabarsi, M. Pourabdollah, I.M. Adcock, Immunopathology of sarcoidosis, Iran. J. Allergy Asthma Immunol. 13 (5) (2014) 300–306. [84] D.R. Moller, Potential etiologic agents in sarcoidosis, Proc. Am. Thorac. Soc. 4 (5) (2007) 465–468. [85] A. Dubaniewicz, A. Zimmermann, M. Dudziak, M. Typiak, M. Skotarczak, Tuberculosis in the course of sarcoidosis treatment: is genotyping necessary for personalized therapy? Expert Rev. Clin. Immunol. 9 (4) (2013) 349–360.