Serum protein profile of Crohn's disease treated with infliximab

Abstract

The infliximab (IFX) has dramatically improved the treatment of Crohn's disease (CD). However, the need for predictive factors, indicative of patients' response to IFX, has yet to be met. In the current study, proteomics technologies were employed in order to monitor for differences in protein expression in a cohort of patients following IFX administration, aiming at identifying a panel of candidate protein biomarkers of CD, symptomatic of response to treatment. We enrolled 18 patients, who either had achieved clinical and serological remission (Rm, n = 6), or response (Rs, n = 6) and/or were PNRs (n = 6), to IFX. Serum samples were subjected to two-dimensional Gel Electrophoresis. Following evaluation of densitometrical data, protein spots exhibiting differential expression among the groups, were further characterized by MALDI-TOF-MS. Identified proteins where evaluated by immunoblot analysis while functional network association was carried out to asses significance. Proteins apolipoprotein A-I (APOA1), apolipoprotein E (APOE), complement C4-B (CO4B), plasminogen (PLMN), serotransferrin (TRFE), beta-2-glycoprotein 1 (APOH), and clusterin (CLUS) were found to be up-regulated in the PNR and Rs groups whereas their levels displayed no changes in the Rm group when compared to baseline samples. Additionally, leucine-rich alpha-2-glycoprotein (A2GL), vitamin D-binding protein (VTDB), alpha-1B-glycoprotein (A1BG) and complement C1r subcomponent (C1R) were significantly increased in the serum of the Rm group. Through the incorporation of proteomics technologies, novel serum marker-molecules demonstrating high sensitivity and specificity are introduced, hence offering an innovative approach regarding the evaluation of CD patients' response to IFX therapy.

Inflammatory bowel disease, Crohn's disease, Infliximab, Proteomics

1 Introduction

Crohn's disease (CD) and ulcerative colitis (UC) known as inflammatory bowel diseases (IBD) are chronic, multifactorial, polygenic and of unknown etiology immuno-inflammatory pathologies of the gastrointestinal tract.¹ It has been thought that IBD pathogenesis is the consequence of an overly aggressive cell-mediated immune response to commensal enteric bacteria in a genetically susceptible host.² Even if major advances have enhanced the understanding of the influence of genetic, environmental, microbial and inflammatory determinants on IBD, the etiology of the disease remains indefinable.³

Unexceptionably major challenges in IBD research include identification of major pathogenic alterations, as well as effective biomarkers for early diagnosis, prognosis and prediction of therapeutic response. Despite recent advances in the medical therapy of IBD with the introduction of anti-TNFα agents infliximab (IFX) and adalimumab (ADA) important unmet demands include the identification of major pathogenic alterations and biomarkers that could help to timely diagnose and predict response to therapy and the long-term outcome of disease. Recent genome-wide association studies (GWAS) have identified a number of genetic polymorphisms that convey an increased risk for developing IBD and other diseases,⁴ while candidate gene association studies with response to IFX have led to contradictory results.⁵–⁷ However, by only associating genotypes with clinical outcome, little can be inferred on the disease-causing mechanisms themselves. Moreover, the effect size of genetic associations with clinical phenotypes is often small. It is well accepted that biological and functional output of cells is governed primarily by proteins, thus characterization at the level of the proteome is necessary to resolve the crucial changes that occur at different stages of IBD pathogenesis. Current proteomic approaches are beginning to have a profound impact on the way and capacity by which we profile protein expression and post-translational modifications, functional interactions between proteins, and disease biomarkers.⁸–¹⁰ Proteomics technologies such as two-dimensional gel electrophoresis, various variations of mass spectrometry, and protein chip (array) technology are now proving to be powerful tools in biomarker discovery and are beginning to be utilized in IBD biomarker discovery.¹⁰,¹¹ Meuwis et al.¹² analyzed serum samples from IBD patients and controls with surface-enhanced laser desorption/ionization (SELDI)/time-of-flight (TOF) mass spectrometry (MS) identifying as suitable markers platelet aggregation factor 4 (PF4), myeloid-related protein 8 (MRP8), fibrinogen-a (FIBA), and haptoglobin a2 (Hpa2). Nani et al.¹³ by using different selective solid-phase bulk extraction, matrix-assisted laser desorption/ionization (MALDI) TOF MS and chemometric data analysis on healthy and IBD patients they revealed 20 proteins that can be used for discrimination among the categories.

Infliximab (IFX) is the first anti-TNFα agent accepted for IBD treatment. Although IFX is a potent therapeutic agent for IBD refractory to conventional therapies¹⁴ up to 15% of patients do not respond to the classical induction therapy (5 mg/kg IFX iv at weeks 0, 2 and 6) and are considered as primary non responders (PNR).¹⁵–¹⁷ This has been attributed to different immuno-inflammatory mechanism(s), a differential role of TNFα in certain stages of disease, individual differences in drug metabolism and elimination, drug binding in serum or tissues based on disease activity level, the presence of innate anti-TNFα antibodies that may exhibit greater neutralizing activity in non-responders, absence of inflammation accounting for clinical symptoms, or impacted by, as yet unidentified, genetic or serological backgrounds of individual patients.¹⁶ In contrast to PNR, a varying proportion of patients despite initial response will eventually lose response to IFX. Secondary loss of response can be related to individual differences in bioavailability and pharmacokinetics, leading to inadequate concentrations of a biologic secondary to immunogenicity or other factors that increase drug clearance (decreased circulation half life and possible high conõsumption in severe disease).¹⁸ Foremost, because of the current positioning of anti-TNF biological agents for patients refractory to conventional therapies, the paucity of subsequent alternative medical approaches, and the substantial cost of biological therapy, clinicians should made any effort to avoid secondary loss of response. Although numerous studies have attempted to define specific biomarkers for response to IFX, a limited number of proteomic studies have attempted to mine the entire proteome of the serum or general body fluid for candidate biomarkers.¹⁹ Meuwis et al.¹⁹ performed SELDI-TOF MS analysis for the prediction and characterization of the response to anti-tumor necrosis factor alpha (TNF-α) therapy in a few patients affected by CD, evaluating a correlation between PF4 and the activity of the drug. This is particularly important because the biological basis for the clinical response to IFX is not well understood and the mechanisms are essential to the efficiency of such treatments have not yet been disclosed.¹⁵

Therefore, following a proteomics-based approach this pilot study aims to measure changes in serum protein levels in a small cohort of patients with CD who were either PNR or had responded clinically and serologically to (Rs) or achieved clinical and serological remission (Rm) on IFX, aiming at identifying a panel of candidate protein biomarkers of CD that might predict response to IFX therapy.

2 Materials and methods

2.1 Patients and treatment

Patients attended the Evangelismos Hospital IBD Outpatient Clinic aimed at initiating IFX induction therapy (5 mg/kg of body weight at weeks 0, 2 and 6) for inflammatory, mixed inflammatory/structuring or penetrating phenotype of CD were asked to participate in this study that was approved by the ethical committee in the participating hospital and all patients gave their informed consent. Clinical and serological responses were assessed using the Harvey–Bradshaw Index (H–BI) and the serum levels of C-reactive protein (CRP), respectively, at baseline (before the 1st infusion of IFX), the day before each subsequent IFX infusion and after 12 weeks of treatment. Based on the clinical and serological response to IFX induction therapy we enrolled in this study 18 patients, who either had achieved clinical and serological remission (Rm, n = 6), or response (Rs, n = 6) and or were PNRs (n = 6), to IFX. Rm was considered a H–BI equal or less than 4 and a normal CRP (< 0.5 mg/dl); Rs was considered a decrease in both H–BI score (by > 50%, but still over 4) and the serum CRP value (but still above the upper limit of normal), compared to baseline. PNRs were those with no change or worsening in H–BI score and serum CRP levels. All patients had moderately to severely active inflammatory disease prior to therapy despite stable doses of steroids and/or immunomodulators as indicated by an H–BI ranging from 6 to 12 and abnormal serum CRP levels (Table 1). The activity of disease was also assessed by ileocolonoscopy within 4 weeks prior to administration of IFX and then between 12 and 20 following initiation of IFX based on the response to treatment.

2.2 Proteomic serum profiling

Two blood samples were taken from every patient: before treatment (controls) and after IFX induction (week 12). Following centrifugation sera were collected and stored at − 80 °C until use. The sera protein content was determined using the Bioanalyzer Automated Electrophoresis Station (Agilent Technologies Inc., Waldbornn, Germany) combined with the Protein 200 plus kit (Agilent Technologies Inc., Waldbornn, Germany), as previously described.¹⁹ The experimental procedure of two Dimensional Gel Electrophoresis (2-DE), was performed as previously described.²⁰,²¹ Briefly, 1 mg of total protein from each sample was applied on 18 cm immobilizer pH 3–10 and 17 cm, pH 4–7 linear gradient IPG strips (Bio-Rad Lab, Hercules, CA), at their basic and acidic ends, in sample cups.

Prior to Isoelectric Focusing, IPG strips were rehydrated overnight in 500 μL of rehydration solution consisting of 8 M urea, 2% CHAPS, and 0.4% dithioerythritol (DTE) in rehydration trays.

To ensure maximal reproducibility in 2-DE experiments and prevent variations due to technical factors, all 2-DE gel experiments were carried out simultaneously, under the same electrophoretic conditions [i.e., serum proteins from CD (Rm, Rs and PNR) patients were assayed, extracted and ran always simultaneously with their matched control samples]. First dimensional electrophoresis focusing started at 250 V and voltage was gradually increased to 5000 V at 3 V/min, where it was kept constant for 25 h (approximately 80,000 Vh totally). Prior to second dimension, strips were equilibrated first in 50 mM Tris–HCl (pH 6.8), containing 6 M urea, 30% (v/v) glycerol, 2% (w/v) SDS and 30 mM Dithiothreitol (DTT), for 15 min and then in the same buffer containing 0.23 M iodoacetamide.

Second dimensional electrophoresis was performed in 12% SDS-polyacrylamide gels (180 × 200 × 1.5 mm³) with a run of 40 mA/gel, in PROTEIN-II multicell apparatuses (Bio-Rad). After vertical electrophoresis, proteins were fixed in 50% ethanol containing a 10% acetic acid solution for 2 h. The fixative solution was washed-off by agitation in distilled water for 45 min. Protein spots were visualized by application of Coomassie Blue G-250 staining solution (Novex, San Diego, CA) on 2-DE gels for 12 h.

Gel images were scanned in a GS-800 Calibrated Densitometer (Bio-Rad), an stored on a PC for further analysis.

2.3 Image analysis

Protein spots from all gels analyzed were detected, aligned, matched and quantified using the PD-Quest v8.0 image processing software (Bio-Rad), according to the manufacturer's instructions. Manual inspection of the spots was used to verify the accuracy of matching. Spot volume was used as the analysis parameter to quantify protein expression. Normalization of each individual spot was performed according to the total quantity of the valid spots in each gel, after subtraction of the background values.

Optical density (O.D.) level (%) of each protein from the control or CD group was determined separately and calculated as the sum of the volume % of all spots from all gels containing the same protein.

Selection of protein spots or entire gel regions for MS analysis was based upon O.D. alteration between the two groups analyzed. A minimum of 1.5-fold change in the expression level was used as a selection criterion, at the p < 0.05 level.

2.4 Protein identification by mass MALDI-TOF-MS

For Matrix-Assisted Laser Desorption Tandem TOF Mass Spectrometer (MALDI-TOF-MS) analysis, protein spots of interest were manually annotated using the Melanie 4.02 software and excised from 2-DE gels using a Proteiner SPII instrument (Bruker Daltonics, Bremen, Germany). Gel pieces were then placed into 96-well microtitter plates, destained with 150 μL of 30% ACN in 50 mM ammonium bicarbonate and dried in a speed vacuum concentrator (MaxiDry Plus, Heto, Allered, Denmark). In-gel digestion was performed with 0.01 μg/μL trypsin (Roche Diagnostics, Base, Swiss) for 16 h at room temperature. Next, 10 μL of 50% CAN contain 0.3% TFA, were added to each dried gel piece and digested peptides were extracted. Tryptic peptide mixtures (1.5 μL) were applied on an anchor chip MALDI plate with 1 μL of matrix solution, consisting of 0.08% CHCA (Sigma) and the internal standard peptides des-Arg-bradykinin (Sigma, 904.4681 Da) and adrenocorticotropic hormone fragment 18–39 (Sigma, 2465.1989 Da) in 65% ethanol, 50% ACN and 0.1% TFA. Peptide mixtures were analyzed in a MALDI-ToF mass spectrometer (Ultraflex, Bruker Daltonics). Laser shots (n = 400) of intensity between 40% and 60% were collected and summarized and the peak list was created using the Flexanalysis v2.2 software (Bruker). Smoothing was performed with the Savitzky–Golay algorithm (width 0.2 m/z, cycle number 1). S/N was calculated by SNAP algorithm and a threshold ratio of 2.5 was allowed. Peptide matching and protein searches were automatically performed with use of MASCOT Server 2 (Matrix Science). Peptide masses were compared with the theoretical peptide masses of all available proteins from Homo sapiens in the SWISS-PROT and TREmBL databases. Stringent criteria were used for protein identification with a maximum allowed mass error of 25 ppm and a minimum of four matching peptides. Probability score with p < 0.05 was used as the criterion for affirmative protein identification. Monoisotopic masses were used, and one missed trypsin-cleavage site was calculated for proteolytic products. Search parameters included potential residue mass modification for carbamidomethylation and oxidation. Redundancy of proteins that appeared in the database under different names and accession numbers was eliminated. If more than one protein was identified under one spot, the single protein member with the highest protein score was singled out from the multiprotein family.

2.5 Western blot analysis

Total proteins (10 μg) of control and CD patients' group samples were separated by 10% SDS-PAGE, under reducing conditions and electroblotted to Hybond_ECL NC membranes (Amersham Biosciences, Upsala, Sweden). After blocking with 5% non-fat dried milk in TBST solution (20 mM Tris/pH 7.6, 137 mM NaCl, 0.1% Tween20) for 1 h at room temperature, membranes were washed with TBST and incubated overnight at 4 °C with the appropriate primary antibodies against clusterin (CLUS), (sc-56079, dilution 1:200) and apolipoprotein A-I (APOA1) (sc-58230, dilution 1:200). Next, membranes were washed with TBST and incubated with an antimouse HRP-conjugated secondary antibody (1:5000). After a final wash with TBST solution proteins were detected by the ECL, west pico (Pierce) detection system. Western blots were scanned with a GS-800 calibrated densitometer (Bio-Rad). Band quantification was performed with the Quantity One image processing software (Bio-Rad). The human IgG protein was used as internal control to ensure equal sample loading. All antibodies were purchased from Santa Cruz Biotechnology (CA).

2.6 Statistical analysis

To ensure confidence in our experimental approach we employed a design which involved duplicate 2-DE gels per sample (i.e., to determine analytical variation) and separate preparations for each replicate sample per experiment (i.e., to determine biological variation). Comparisons were performed between samples in pairs in all the possible combinations (control vs Rm, control vs Rs, control vs PNR, Rm vs Rs, Rm vs PNR and Rs vs PNR). Mean densitometry values of all spots corresponding to a specific protein from each pair of group were first checked for normal distribution using the Kolmogorov–Smirnov/Lilliefor test (Stat-Plus 2007 software, AnalystSoft, Vancouver, Canada). Data with normally distributed densitometric values were exported to Microsoft Excel 2007 software and compared with the two pair t-test assuming unequal variances. Means of spot intensities for proteins with not normally-distributed values were compared for statistical significance with the Mann–Whitney nonparametric test (GraphPad Instat 3 software, GraphPad software Inc., La Jolla, CA). Statistical significance (a-level) was defined as p < 0.05. To control the False Discovery Rate (FDR), individual a-levels for each spot were adjusted following the FDR correction procedure.²² In Western Blot experiments; mean protein quantification was performed by three independent experiments for each protein analyzed. Optical density means of the bands for each protein were compared with two sample t-test assuming unequal variances of the Microsoft Excel 2007 software. A p < 0.01 was considered statistically significant.

2.7 Network analysis and functional evaluation

All protein identifications, both the ones solely expressed in the different CD groups as well as those differentially expressed among CD groups, were used for pathway analysis. Functional relationships of the identified proteins were determined by submitting protein entry names to the STRING database. The simplified version of the produced network, which involved both up- and down regulated proteins, was adopted.

3 Results

Patient demographic and clinical characteristics are given in Table 1. There were no significant deviations in any demographic or clinical data of patients in the study groups. Although, the baseline mean CRP levels in the Rm group were slightly lower, differences were not significant. Post-treatment CRP levels were significantly lower in Rm group compared to pre-treatment values in contrast to Rs group and PNR (Table 1). Moreover, post-treatment CRP levels were significantly lower in the Rm group compared to Rs group and PNR (Table 1). The baseline mean H–BI was not significantly different between the 3 groups, while post-treatment H–BI was significantly lower in both Rm and Rs groups compared to pre-treatment values in contrast to PNR, while post treatment H–BI was significantly lower in the Rm group compared to both Rs group and PNR (Table 1). Patients who achieved Rm after IFX induction therapy had complete or near complete mucosal healing at ileocolonoscopy (complete resolution of ulcers or a few scattered aphthous ulcer, respectively) performed around week 20 after initiation of IFX. As expected, none of the PNR had any improvement in the endoscopic findings after IFX induction therapy.

To detect serum proteins differently expressed between the Rm, Rs and PNR groups, we separated each protein sample by 2-DE (Fig. 1). The administration of IFX markedly changed the protein profile of serum from CD patients. Statistical analysis of resultant 2-DE gels revealed 240 protein spots differentially expressed among the groups. Fifteen proteins corresponding to 240 spots were identified since more than one spots correspond to the same protein. Table 2 summarizes the identified proteins differentially expressed in all samples of each patient group, gives their identity, theoretical pI, molecular weight, MASCOT score, protein coverage and expression level as calculated with the PDQuest 8.0 software. Expression level > 1 indicate over-expression and < 1 under-expression. Protein spots identified as apolipoprotein A-I (APOA1), apolipoprotein E (APOE), complement C4-B (CO4B), plasminogen (PLMN), serotransferrin (TRFE), beta-2-glycoprotein 1 (APOH), and clusterin (CLUS) were found to be significantly up-regulated in the PNR and/or Rs group of patients whereas their levels display no changes in the Rm group when compared to baseline samples. Additionally, leucine-rich alpha-2-glycoprotein (A2GL), vitamin D-binding protein (VTDB), alpha-1B-glycoprotein (A1BG) and complement C1r subcomponent (C1R) were significantly increased in the serum of Rm patients' group compared to baseline samples. Furthermore, complement C3 (CO3), transthyretin (TTHY) and fibrinogen alpha chain (FIBA) were found to be significantly suppressed in Rm group only compared to Rs and PNR patients.

The differential expression of APOA1 and CLUS was further confirmed by Western Blot analysis using the appropriate antibodies in control (before treatment), Rm, Rs and PNR samples (Fig. 2). Optical density measurements of the bands revealed that there was approximately 2.5 fold increase in the amount of APOA1 and approximately 2 fold increase in the amount of CLUS in Rm and PNR samples compare to Rs and baseline (control) samples.

The pathways which engage proteins identified in our samples as well as those differentially expressed in groups tested were studied using STRING database (Fig. 3).

4 Discussion

Although numerous studies have attempted to define specific biomarkers for response to IFX in IBD, a limited number of proteomic studies have attempt to mine the entire proteome of the serum for candidate biomarkers. Since predicting response to IFX is still a major unmet therapeutic demand in CD due to cost and potential side effects of this drug, in this study using mass MALDI-TOF-MS we have characterized several potential biomarkers closely correlated to the response or primary lack of response to IFX. The primary time point chosen for analysis was 12 weeks after initiation of IFX because the vast majority of patients are expected to have shown a meaningful clinical and serological response or even remission. Despite the small number of patients tested, and the potential influence of selection bias, we have obtained results suggesting that proteomic markers might help to understand response to IFX and perhaps define new markers that predict response or lack of response. Among the proteins identified in our study the APOA1, APOE, CO4B, PLMN, TRFE, APOH and CLUS were found to be up-regulated in the PNR and Rs groups of patients after IFX treatment. Our results are in agreement with previous studies on autoimmune diseases treated with IFX such as rheumatoid arthritis.²³–²⁵ Our data did not confirm the results of Meuwis et al.¹⁸ who by using SELDI-TOF-MS identified that platelet aggregation factor 4 (PF4) could be a potential biomarker for IFX response prediction of IBD patients. Additionally, our data suggest that Rs and PNR have a distinct serum protein profile since we found that spots corresponding to CO3, TTHY, and FIBA were enhanced only to Rs patients. Furthermore, Rs were characterized by significantly increased levels of A2GL, VTDB, A1BG and C1R. Thus, the data generated support the utility of this approach in defining quantitative changes that occur in a wide spectrum of proteins, and they demonstrate consistent changes in TNF-α-regulated proteins, particularly in the CD patients who had the most robust clinical responses.

Using the protein network program STRING a functional relationship of differentially expressed proteins was obtained which involved a major protein cluster and significant peripheral relationships in the most of the 15 proteins. The data provide evidence of synergism between pathways possibly implicated in the action of IFX.

Differential expression of proteins involved in immune response was identified. Most of them i.e. APOA1 modifies the activation of monocytes/macrophages.²⁶,²⁷ APOA1 blocks the contact-mediated activation of monocytes by T-lymphocytes, resulting in the inhibition of IL-1β and TNF-α production. IFX changes lipid profiles, which may play a role in modulating inflammation indirectly.²⁸,²⁹ Additionally, efforts at understanding how anti-TNF-α agents work in immune-mediated disease centered on the ability of anti-TNF-α antibodies to neutralize soluble TNF-α or to block TNF receptors from binding to their ligands.²⁸ Subsequent studies suggested that complement-mediated lysis or antibody-dependent cytotoxicity (ADCC) played a role in the effects of these agents.³⁰ Differential expression of CLUS was also noticed. It is known that CLUS inhibits complement mediated cell lysis.³¹ Concerning PLMN, is a positive acute-phase protein that has an important role in thrombogenesis, and elevated plasma PLMN concentrations have been associated with cardiovascular disease.³²,³³ Recent data suggest that TNF-α blockade by IFX not only decreases inflammation, but also reduces the inhibition of fibrinolysis.³⁴ Concerning the TRFE, its concentration typically decreases in the context of an inflammatory response.³⁵,³⁶ Interestingly however, Low et al.³⁵ have reported an up-regulation of TRFE in juvenile idiopathic arthritis, as we noticed in PR and PNR to IFX CD patients. The activation, proliferation and maturation of most immune cells is iron dependent, so that high levels of TRFE could directly contribute to the response to IFX.

It is known that serum A2GL concentrations correlated with disease activity in RA and CD.³⁷ Differential expression of VTDB was also observed. It is known that vitamin D directly and indirectly regulates the differentiation, activation of CD4 + T-lymphocytes and can prevent the development of autoimmune processes.³⁸ Recently Stio et al.³⁹ suggested that Vitamin D derivatives induced a decrease in cell proliferation higher in IFX responsive patients than in the unresponsive one. Increased VDR levels during IFX therapy were registered only in the unresponsive patient. However, in our study VTDB was found to be up-regulated in the Rm group.

The Swiss-Prot accession numbers of the differentially expressed proteins were inserted into the String Database. This open access web-tool aims to collect, predict and unify most types of protein–protein associations, including direct and indirect associations. In order to cover protein interactions not yet addressed experimentally, STRING runs on a set of prediction algorithms, and transfers known interactions from model organisms to other species based on predicted orthology of the respective proteins.⁴⁰ Strong association of proteins, apolipoprotein H (APOH), apolipoprotein A-I (APOA1), transhyretin (TTR), complement component 3 (C3), apolipoprotein E (APOE), transferring (TF), group-specific component (vitamin D binding protein, GC), complement component 1 (C1R), fibrinogen alpha chain (FGA), plasminogen (PLG), clusterin (CLUS), apolipoprotein A-IV (APOA4) and complement C4-B precursor (ENSG00000224389), are annotated by thicker lines as well as neighboring positions in the present network. While proteins APOH, APOA4 and alpha-1-B glucoprotein (A1FG) seems to consist of another group of proteins weekly associated regarding their functionality activity, according to known published experimental data. Finally, one of the identified proteins (leucine-rich alpha-2-glycoprotein, LRG1) does not have any established relationship with the other proteins differentially expressed.

In conclusion, in this pilot study we report on 15 proteins differentially present in the serum of CD patients following IFX treatment. Among them protein spots identified as APOA1, APOE, CO4B, PLMN, TRFE, APOH, and CLUS were found to be significantly up-regulated in the PNR and/or Rs group of patients compared to Rm. Our study shows the feasibility to disclose and identify potential biomarkers associated with response to IFX by a proteomic strategy. Unfortunately, financial constrains prevented us from assessing sequential changes in the serum proteomic profile in our patients prior to the 2nd add 3rd infusion of IFX which might help in earlier identification of patients who are aimed to be PNR or simply Rs. This approach might also helped in separating PNR from Rs which also has major clinical implications. Nevertheless, undoubtedly future experiments in a larger cohort of CD patients are needed to evaluate the relevance of our preliminary findings.

Conflict of interest

No conflict of interest to declare.

Figure 1

Representative, comprehensive analyses of serum-derived proteins from control (before treatment) (A), Rm (B), Rs (C) and PNR (D) CD patients treated with IFX. In total, 240 definite protein spots were obtained for MS analysis.

Figure 2

Confirmation of the overexpression of APOA1 and CLUS by western Blot analysis. Quantification of protein content was performed using scanning densitometry. Each bar represents the mean optical density ± SD of three independent experiments. Differences were significant at the level of p < 0.01.

Figure 3

Interaction networks and enriched functional annotations of proteins differentially expressed in examined samples. Thicker network lines demonstrate strong protein relation as well as neighboring positions. APOH, apolipoprotein H; APOA1, apolipoprotein A-I; TTR, transhyretin; C3, complement component 3; APOE, apolipoprotein E; A1BG, alpha-1-B glucoprotein; TF, transferring; GC, group-specific component (vitamin D binding protein); C1R, complement component 1; LRG1, leucine-rich alpha-2-glycoprotein; FGA, fibrinogen alpha chain; PLG, plasminogen; CLU, clusterin; APOA4, apolipoprotein A-IV; ENSG00000224389, complement C4-B precursor.

Table 1

Clinical characteristics of patients with Crohn's disease.

	Rm, (n = 6)	Rs, (n = 6)	PNR, (n = 6)	p
Age median range (years)	33 (21–65)	34 (19–52)	35 (22–55)	NS
Males/females	4/2	4/2	3/3	NS
Disease years	6.8 ± 5.51	5.84 ± 3.99	7.6 ± 9.91	NS
Localization
Ileum	1	1	1	NS
Colon	1	2	1	NS
Ileocolon	4	3	4	NS
Behaviour
Inflammatory	3	2	3	NS
Stricturing	1	2	1	NS
Penetrating	2	2	2	NS
Baseline
5-ASA	3	4	2	NS
Azathioprine	3	2	4	NS
Corticosteroids (per os) (methylprednisolone 16 mg, or prednisolone 20 mg)	3	4	5	NS
Methotrexate	0	0	0	NS
Co-morbidities	0	0	0	NS
Extra-intestinal manifestations	3	4	5	NS
Surgeries (ileocolonic resections)	2	4	2	NS
CRP levels (mg/dL, mean ± SD)
Pre-treatment (week 0)	2.85 ± 0.68	4.44 ± 2.73	4.28 ± 2.32	NS
Post-treatment (week 12)	0.31 ± 0.19⁎,#,$	2.67 ± 1.26	1.91 ± 1.33
H–BI (mean ± SD)
Pre-treatment (week 0)	9.66 ± 1.75	10.33 ± 1.75	9.33 ± 1.63	NS
Post-treatment (week 12)	1.33 ± 0.82⁎⁎,#,$	5 ± 0.63⁎	9.83 ± 1.72

NS: non significant.

⁎

p < 0.01.

⁎⁎

p < 0.001, pre-treatment vs post treatment.

#

p < 0.001. Rm vs Rs.

$

p < 0.001 Rm vs PNR.

Table 2

Proteins differentially present in the serum of Rm, Rs and PNR to IFX therapy.

Protein name	Protein symbol	MW	pI	Mascot score	Coverage	Expression levels
						Rm	Rs	PNR
Leucine-rich alpha-2-glycoprotein	A2GL_HUMAN	38,382.00	6.50	97	32	3.8⁎⁎	0.94	0.74
Apolipoprotein A-I	APOA1_HUMAN	30,759.00	5.50	261	59	1.07	3.75⁎⁎	3.24⁎⁎
Apolipoprotein A-IV	APOA4_HUMAN	45,371.00	5.20	231	57	1.02	0.49⁎	1.65⁎
Apolipoprotein E	APOE_HUMAN	36,246.00	5.50	170	51	0.96	1.02	2.02⁎
Complement C3	CO3_HUMAN	188,569.00	6.00	367	37	2.4⁎	1.33⁎	2.17⁎
Complement C4-B	CO4B_HUMAN	194,247.00	6.70	208	23	2.21⁎	3.57⁎	4.21⁎⁎
Plasminogen	PLMN_HUMAN	93,247.00	7.30	311	50	1.13	0.78	3.65⁎⁎
Transthyretin	TTHY_HUMAN	15,991.00	5.40	162	64	5.84⁎⁎	1.62⁎	5.55⁎⁎
Serotransferrin	TRFE_HUMAN	79,280.00	7.00	257	43	0.24⁎⁎	1.08	1.652⁎
Vitamin D-binding protein	VTDB_HUMAN	54,526.00	5.30	245	55	4.01⁎⁎	1.47⁎	2.71⁎
Alpha-1B-glycoprotein	A1BG_HUMAN	54,809.00	5.50	195	46	2.15⁎	1.05	0.98
Beta-2-glycoprotein 1	APOH_HUMAN	39,584.00	9.49	128	58	0.98	1.51⁎	1.91⁎
Complement C1r subcomponent	C1R_HUMAN	81,606.00	5.80	158	42	3.35⁎⁎	0.96	0.97
Clusterin	CLUS_HUMAN	53,031.00	5.90	75	21	1.09	3.33⁎⁎	4.28⁎⁎
Fibrinogen alpha chain	FIBA_HUMAN	95,656.00	5.60	250	36	1.64⁎	0.91	1.94⁎

Differentially expressed proteins in all samples of each patient group present with their name and symbol, theoretical pI and molecular weight calculated using the CalPI/MW available on the Swiss-Prot Web site. MASCOT search score and coverage of each identification are also listed. Score is − 10 Log (p), where p is the probability that the observed match is a random event. Scores > 55 indicate identity or extensive homology at the p < 0.05 level. Expression elevels were calculated in relation to control densities. Expression level > 1 indicates overexpression, expression level < 1 indicates suppression.

⁎

p < 0.05.

⁎⁎

p < 0.005.