BioMed Research International
Volume 2019 (2019), Article ID 3950628, 11 pages
https://doi.org/10.1155/2019/3950628

Research Article

Quantitative Proteomic Analysis Reveals the Deregulation of Nicotinamide Adenine Dinucleotide Metabolism and CD38 in Inflammatory Bowel Disease

LongGui Ning,¹ Guodong Shan,¹ Zeyu Sun,² Fenming Zhang,¹ Chengfu Xu,¹ Xinhe Lou,¹ Sha Li,¹ Haojie Du,¹ Hongtan Chen,¹ and Guoqiang Xu¹

¹Department of Gastroenterology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
²Proteomics & Metabolomics Platform, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Hangzhou, China

Correspondence should be addressed to Guoqiang Xu; 1193065@zju.edu.cn

Received 27 December 2018; Revised 25 March 2019; Accepted 2 April 2019; Published 23 April 2019

Academic Editor: David Bernardo

Copyright © 2019 LongGui Ning et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Inflammatory bowel disease (IBD) has become a major health challenge worldwide. However, the precise etiological and pathophysiological factors involved in IBD remain unclear. Proteomics can be used for large-scale protein identification analysis. In the current study, using tandem mass tag- (TMT-) based shotgun proteomics, proteomic differences between intestinal tissue from health controls, patients with Crohn’s disease (CD), and patients with ulcerative colitis (UC) were compared. Proteins with fold change >2 or <0.5 and P value < 0.05 between groups were considered differentially expressed. ProteinAtlas was used to analyze the tissue specificity of differentially expressed proteins (DEPs). Reactome pathway analysis was applied to cluster functional pathways. A total of 4786 proteins were identified, with 59 proteins showing higher levels and 43 showing lower levels in patients with IBD than in controls. Seventeen proteins, including angiotensin converting enzyme 2 (ACE2) and angiotensin converting enzyme 1 (ACE), showed higher levels in CD than in UC. Several novel proteins such as CD38, chitinase 3-like 1 (CHI3L1), olfactomedin 4 (OLFM4), and intelectin 1 were screened out between patients with IBD and controls. When proteins with fold change >1.2 or <0.84 and P value < 0.05 between groups were considered differentially expressed, the expression of 10 proteins, including CD38, involved in the nicotinamide adenine dinucleotide (NAD) metabolism and signaling pathway showed significant changes in IBD. Using the NCBI GEO database, we confirmed increased CD38 mRNA expression in patients with UC and in mouse colitis models. Protein CD38 expression was higher in CD and UC than in normal controls. CD38 expression was higher in inflamed tissues than in noninflamed tissues, and CD38 was located in F4/80-positive cells. Our study may provide novel insights into the molecular pathogenesis of IBD. Further studies are required on the role of NAD metabolism and CD38 in intestinal inflammation.

1. Introduction

Inflammatory bowel disease (IBD) is categorized into Crohn’s disease (CD) and ulcerative colitis (UC), which are characterized by relapsing chronic colitis in the gastrointestinal tract. An estimated 2.5 million people are affected by IBD in Europe [1]. In Asia, although the prevalence of IBD is lower than that in Europe, it has rapidly increased over the last decade [2, 3]. Thus, IBD has become a major health challenge worldwide. However, the precise etiological factors of IBD remain unclear. Currently, IBD is thought to result from interplay between environmental factors and host genetics, leading to persistent gastrointestinal immune activation [4, 5].

Various inflammatory molecules, including cytokines, chemokines, and danger-associated molecular patterns (DAMPs), are released from infiltrating inflammatory cells [4], and drugs targeting these inflammatory molecules are developed as therapeutics for IBD treatment [6]. Tumor necrosis factor-α (TNF-α) inhibitors are now the most commonly prescribed biologic therapeutics for patients with IBD. Other new therapeutic concepts such as Janus kinase (JAK) inhibitors, antiadhesion molecules, and anti-Smad7 have shown promising results in current clinical trials [7–9]. Much of the recent research in IBD has been focused on identifying novel molecules that may be therapeutic targets.

Currently, IBD diagnosis depends on clinical, endoscopic, radiographic, and laboratory findings. The differential diagnosis of CD and UC is clear in most cases; however, it is difficult to determine in an estimated 15% of patients because of atypical findings [10, 11]. Accurate diagnosis of IBD and differential diagnosis between UC and CD are essential for ensuring appropriate therapeutic intervention and surveillance [12]. Serological markers, especially perinuclear antineutrophil cytoplasmic antibodies (pANCAs) and anti–Saccharomyces cerevisiae antibodies (ASCAs), aid in differentiating UC from CD [13]; however, the sensitivity of this test is relatively low [14]. Histological biomarkers for this differential diagnosis are not well understood. Identifying molecules differentially expressed between CD and UC may help uncover the differences in their pathogenesis.

Proteomics helps provide novel strategies for large-scale protein identification analysis and valuable insights into disease pathophysiology. In the past decade, proteomic inquiries have helped uncover numerous host proteins and pathways related to IBD pathogenesis. Utilizing matrix-assisted laser desorption/ionization (MALDI)–time-of-flight (TOF) mass spectrometry (MS), Anna et al. [15] identified annexin A2 and programmed cell death protein 8 as being involved in the destruction of intestinal epithelial cell (IEC) homeostasis in UC. Zhao et al. [16] identified the p38 mitogen-activated protein kinase (MAPK) pathway as a molecular signature in UC. Moreover, serum proteomic panels have been used to differentiate CD from UC [17], to predict disease activity [18], and to evaluate response to infliximab (IFX) therapy [19]. In the current study, we aimed to identify potential proteins involved in IBD pathophysiology and to compare the proteomic differences between CD and UC by using tandem mass tag- (TMT-) based quantitative proteomics in order to identify novel proteins that may be associated with the pathogenesis of IBD and differentiation between CD and UC.

2. Materials and Methods

2.1. Sample Collection

The diagnostic criteria for both UC and CD were based on clinical, endoscopic, and histological features according to the World Gastroenterology Organization Practice Guidelines for IBD diagnosis and management [20]. For proteomic analysis, patients with CD (n = 9) or UC (n = 9) were recruited from inpatients of the Department of Gastroenterology, the First Affiliated Hospital of Zhejiang University. During colonoscopy, two intestinal tissue biopsy samples were obtained from the inflamed area. The normal controls were patients who underwent screening colonoscopies without active gastrointestinal pathology. Age and sex-matched normal control patients (n = 6) were recruited, and samples were obtained from the normal colon tissue during screening colonoscopy.

The independent groups established for validation were as follows: surgically resected colon tissues from three control patients, three patients with CD, and three patients with UC. For patients with CD or UC, both inflamed and noninflamed tissue samples were obtained. Information regarding baseline clinical characteristics was obtained during admission. Informed consent was obtained from all subjects before participation. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki (6th revision, 2008), as reflected in the approval by the ethics committee of the First Affiliated Hospital of Zhejiang University School of Medicine. In all cases, colonoscopy biopsy or resected colon tissue specimens were rinsed in phosphate-buffered saline and immediately frozen in liquid nitrogen before storage at -80°C.

2.2. Protein Extraction and Digestion

Tissues were homogenized in radioimmunoprecipitation assay (RIPA) lysis buffer and were centrifuged. Protein concentrations were measured with the bicinchoninic acid (BCA) assay (Beyotime, Beijing, China). All samples were reduced with dithiothreitol (10 mM) at 60°C for 30 min and alkylated with iodoacetamide (30 mM) for 30 min at room temperature in the dark. The proteins were then incubated with cold acetone for 4 h at 0°C. The protein pellets were centrifuged at 3000 rpm for 15 min at 4°C and resuspended in 50 μM triethylammonium bicarbonate (TEAB). Trypsin (Thermo Fisher Scientific, America) was added at a 1:50 enzyme: substrate ratio for overnight digestion at 37°C. For TMT labeling, 24 samples, each containing 30 μg protein digest, were divided into three TMT experiments. A common reference sample, created by equal mixing of all samples, was labeled with TMT-131 and TMT-130C across all 3 TMT experiments. The TMT-labeling design is shown in Supplementary Table 1.

2.3. Quantitative Proteomic Analysis

TMT-labeled peptides (Thermo Fisher Scientific) were fractionated using the HPRP method and desalted before LC-MS/MS analyses. Technical details regarding instrument parameters and operational processes can be found in Supplementary Materials and Methods. The RAW files acquired were loaded into MaxQuant (version 1.6.1.0) and searched against the human UniProtKB database (88,473 sequences, version 09-2015). Andromeda was used as search engine for the identification of proteins. The database search was performed using the MS2 reporter ion mode with the 10plex TMT option. A mass tolerance of 7 ppm was set for the main database search. Trypsin with up to two missed cleavages was set. Oxidation (M) and carbamidomethyl (C) were set as variable and fixed modifications, respectively. An automatic decoy database search was performed. A protein level false-discovery rate (FDR) of 1% was set to filter the results.

For quantitative analysis, the TMT reporter ion intensity of each protein was first normalized against the median intensity of all proteins within each sample to correct label-to-label variations. Subsequently, it was normalized against the averaged reference ion intensities of 131N and 131C labels within each run to correct run-to-run variations. At least two unique peptides were required for protein quantitation. Proteins with empty values were discarded. Student’s t-test was performed for each protein between groups with the Perseus software. Proteins that showed more than twofold change (fold change of >2 or <0.5) with P value < 0.05 were considered to show significant differential expression. When analyzing proteins involved in nicotinamide adenine dinucleotide (NAD) metabolism and signaling pathways, proteins that showed more than 1.2-fold change (fold change of >1.2 or <0.84) were considered to show significant change. Multivariate principal component analysis (PCA) and heat maps were used to summarize and visualize sample classification on the basis of expression profiles of all proteins.

2.4. Bioinformatics Analysis

ProteinAtlas was used to analyze the tissue specificity of proteins differentially expressed between patients with IBD and the controls. Differentially expressed proteins (DEPs) were then divided into 2 groups: gastrointestinal (GI) tissue-specific group (GI tissue types) and GI tissue-nonspecific group (other tissue types). Subsequently, reactome pathway analysis (https://www.reactome.org/) was used to cluster the pathways in which the 2 groups were involved. The pathway analysis results were visualized using a bubble chart. CD38 gene expression levels of patients with UC (datasets GDS3119 and GDS2642) and of mouse colitis models (datasets GDS4363 and GDS3859) were downloaded from the NCBI GEO database. The expression data were analyzed with the GraphPad Prism 6 software.

2.5. Mouse Model of Dextran Sulfate Sodium Salt-Induced Colitis

12 male C57BL/6J mice aged 6–8 weeks were purchased from the Zhejiang Academy of Medical Science. The mice were orally administered 4% dextran sulfate sodium salt (DSS; molecular weight: 36,000–50,000; MP Biomedicals, Santa Ana, CA, USA) in water for 5 days to induce acute colitis (n = 6/group). Control mice (n = 6/group) were given drinking water. Body weight and stool consistency were recorded every day. On the sixth day, the mice were euthanized with 5% chloral hydrate. The colons were resected and fixed immediately in 10% formalin and embedded. This study was performed according to the guidelines of the animal ethics committee of the First Affiliated Hospital of Zhejiang University School of Medicine.

2.6. Immunohistochemical Staining

Immunohistochemical (IHC) staining was performed on paraffin-embedded sections of patients who had undergone colon resection. Tissues were cut into 5 μm-thick sections and stained with H&E before IHC staining. Slides were incubated with primary antibodies against ITLN1 (ab118232, Abcam) and CD38 (ab108403, Abcam) for humans and CD38 (sc374650, Santa Cruz) for mice overnight at 4°C, followed by incubation with horseradish peroxidase-conjugated anti-IgG for 1 h at 37°C. The samples were visualized with 3,3′-diaminobenzidine (DAB) and observed under a light microscope (Leica, Germany).

2.7. Immunofluorescence Staining

Paraffin-embedded sections of colon tissues from patients with IBD or controls were used. The slides were incubated with primary antibodies against CD38 (sc374650, Santa Cruz) and F4/80 (ab16911, Abcam) at 4°C overnight. On the next day, the slides were incubated with sheep anti-mouse Alexa Fluor 488 (DaWen Biotech, China) and goat anti-rat Cy3 (DaWen Biotech, China) antibodies at 37°C for 1 h, followed by incubation with 4′,6-diamidino-2-phenylindole (DAPI) for 3 min. Images were obtained using a laser scanning microscope (Olympus, Japan).

2.8. Western Blot Analysis

Specimens were homogenized in RIPA buffer and centrifuged at 12,000 rpm for 15 min at 4°C; subsequently, the supernatants were collected. Proteins (40 μg) were separated using 10% sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE) gels and were then transblotted onto 0.45 μm nitrocellulose membranes (Millipore, Merck, Germany). The membranes were blocked with 5% nonfat milk. Primary antibodies against CD38 (ab108403, Abcam) and glyceraldehyde 3-phosphate dehydrogenase (GAPDH; 5174; Cell Signaling Technology) were used. After incubation with the secondary antibody, the bands were visualized using an electrochemiluminescence (ECL) imaging system.

2.9. Statistical Analysis

Experimental data have been expressed in terms of mean ± standard error of the mean (SEM) values, and the GraphPad Prism 6 software was used for comparison between groups. Unpaired Student’s t-test was used to compare differences between two groups. For more than 2 groups, analysis of variance (ANOVA) was used. A two-sided P value <0.05 was considered to be statistically significant.

3. Results

3.1. Proteins Differentially Expressed between Controls and Patients with IBD

We enrolled 6 controls, 9 patients with CD, and 9 patients with UC. Using a protein level FDR of 1% as a criterion, 5702, 5544, and 5552 proteins were identified from 3 TMT experiments (Figure 1(a)). A total of 4786 proteins were identified from the 3 experiments; of these, 102 were differentially expressed between patients with IBD and controls. The proteome between patients with IBD and controls could be clearly separated, as visualized by the PCA plot (Figure 1(b)) and heat map (Figure 1(c)). Among the DEPs, 59 proteins showed higher expression and 43 showed lower expression in patients with IBD than in controls (Figure 2(a)). Seventeen proteins were differentially expressed between patients with CD and patients with UC (Figure 2(b)). To analyze the related functions of proteins differentially expressed between patients with IBD and controls, reactome clustering pathway analysis was performed. Pathways enriched by DEPs from other tissues were mainly involved in the immune system, including the adaptive immune system and processes such as antigen presentation, interferon alpha/beta signaling, and the neutrophil degranulation pathway (Figure 2(c)). Pathways enriched by DEPs from GI tissue types included the mRNA splicing-major pathway, pyruvate metabolism and citric acid (TCA) cycle, gene and protein expression by JAK-STAT signaling after interleukin-12 stimulation, and apoptosis pathway (Figure 2(d)).

(a)

(b)

(c)

Figure 1: Summary of proteomics analysis of NC, CD, and UC using TMT quantitation method. (a) Venn diagram showing proteins identified across 3 TMT experiments, from which 4786 commonly identified proteins were used for downstream analyses. (b) Overall differences of proteome between NC, CD, and UC were summarized by PCA plot. (c) Heat map representation of abundance profiles of all 4786 proteins in all samples. Color shade correlates with relative protein abundances across each row (red/green for upregulation/downregulation).

(a)

(b)

(c)

(d)

Figure 2: Identification and pathway analysis of DEPs. (a) Volcano map of DEPs between IBD and NC. (b) Volcano map of DEPs between CD and UC. (c) Reactome pathway analysis of DEPs enriched from no tissue types. (d) Reactome pathway analysis of DEPs enriched from GI tissue types.

3.2. DEPs according to Disease Subtypes of Inflammatory Bowel Disease

102 proteins were significantly dysregulated in patients with IBD (Figure 2(a)). Of these, several proteins have been used as disease markers. Among them, lipocalin 2 (LCN2), S100A12, and matrix metallopeptidase 9 (MMP9) were upregulated in both UC and CD, while S100A8, S100A9, myeloperoxidase (MPO), and lactotransferrin (LTF) were upregulated in CD (Supplementary Table 2). Several novel proteins were also identified (Table 1). Chitinase 3-like 1 (CHI3L1), CD38 molecule (CD38), and olfactomedin 4 (OLFM4) were upregulated in patients with CD or UC, whereas intelectin 1 (ITLN1) was downregulated. We also identified proteins that were differentially expressed between the two subtypes of IBD. Our results indicated that 17 proteins were upregulated in CD compared to UC (Figure 2(b)). Some of the proteins with abundance changes are shown in Table 2; from these, angiotensin converting enzyme 2 (ACE2) and angiotensin converting enzyme 1 (ACE) showed significantly higher expression in CD than in UC.

Table 1: Novel proteins showing abundance changes in CD or UC patients.

UniProt accession
Protein
UC/Con
CD/Con
CD/UC
Unique peptides
GI related or not

Fold change
p value
Fold change
p value
Fold change
p value

P36222
CHI3L1
3.42
0.0434
3.13
0.0048
0.92
0.7712
4
not

P28907
CD38
2.28
0.0015
2.70
0.0004
1.18
0.2881
9
not

Q6UX06
OLFM4
2.32
0.0779
1.89
0.0030
0.81
0.4557
5
not

Q8WWA0
ITLN1
0.50
0.0048
0.36
0.0000
0.73
0.1718
6
yes

CD, Crohn’s disease. UC, ulcerative colitis. Con, control. GI, gastrointestinal.

Table 2: Proteins showing abundance changes between CD and UC patients.

UniProt accession
Protein
UC/Con
CD/Con
CD/UC
Unique peptides
GI related or not

Fold change
p value
Fold change
p value
Fold change
p value

P05062
ALDOB
1.55
0.1156
8.62
0.0843
5.56
0.0477
17
not

Q9BYF1
ACE2
1.30
0.2869
6.41
0.0575
4.91
0.0274
2
not

P12104
FABP2
1.02
0.9457
4.87
0.0912
4.78
0.0394
9
not

P12821
ACE
1.26
0.0372
3.96
0.0580
3.13
0.0324
12
not

CD, Crohn’s disease. UC, ulcerative colitis. Con, control. GI, gastrointestinal.

3.3. NAD Metabolism and Signaling Pathway Showed Alterations in Patients with IBD

Using reactome pathway analysis, we noted that many proteins are involved in immune pathways. Previous studies have revealed that CD38 plays multiple functions in rheumatoid arthritis (RA), allergic airway disease, and multiple myeloma and is expressed in the membrane of immune cells [21–23]. Furthermore, Michael reported that CD38 is expressed on inflammatory cells of the intestine and promotes intestinal inflammation [24]. Therefore, we studied the role of CD38 in IBD. CD38 participates in the synthesis of cyclic ADP ribose (cADPR) from NAD, and the NAD metabolism pathway is reported to promote inflammation in the gut [24, 25]; therefore, we analyzed our proteomic results for the expression of other molecules involved in NAD⁺ metabolism and signaling. Twenty-two proteins involved in NAD metabolism and signaling were identified by the proteomic analysis (Table 3). When the fold change level was set at >1.2 or <0.84; 10 of them showed significant change (Table 3); most of these were enzymes related to NAD synthesis and cleavage. The role and expression of these proteins within the NAD metabolism and signaling pathway are indicated in Figure 3.

Table 3: Expression of proteins involved in NAD metabolism and signaling pathway within the proteomic results.

UniProt accession
Protein
p value
Fold change
Unique
GI related or not

IBD/Con
peptides
not

P36222
CD38
0.0030
2.49
9
not

O95544
NADK
0.0768
1.43
5
not

Q4G0N4
NADK2
0.0183
0.82
6
not

Q6IA69
NADSYN1
0.7181
0.97
3
not

C9JF35
NAMPT
0.0084
1.77
14
not

Q6XQN6
NAPRT
0.3029
0.88
14
not

Q9HAN9
NMNAT1
0.112
0.87
3
not

Q96T66
NMNAT3
0.1096
0.79
2
not

P40261
NNMT
0.0013
1.73
6
not

Q13423
NNT
0.0023
0.79
4
yes

Q8TCD5
NT5C
0.1245
0.83
6
not

P49902
NT5C2
0.4623
0.94
4
not

Q9H0P0
NT5C3A
0.0288
0.67
7
not

P21589
NT5E
0.7597
0.92
3
not

Q9BQG2
NUDT12
0.0875
0.82
2
not

P00491
PNP
0.0131
1.34
10
not

Q15274
QPRT
0.1998
1.47
4
not

Q96EB6
SIRT1
0.1482
1.19
2
not

Q8IXQ6
PARP9
0.0038
1.53
9
not

Q460N5
PARP14
0.0013
1.52
12
yes

Q00653
NFKB2
0.0446
1.65
4
not

P19838
NFKB1
0.7749
1.02
11
not

IBD, inflammatory bowel disease. Con, control. GI, gastrointestinal.

Figure 3: The role and expression of proteins identified by proteomic analysis within NAD+ metabolism and signaling pathway. Red (green) rectangle represents proteins upregulated (downregulated) in IBD. Red (green) rectangle with red pentagram represents proteins upregulated (downregulated) in IBD with p value < 0.05. Yellow rectangle represents proteins that are unchanged between IBD and controls. Gray rectangle represents proteins undetected by proteomic analysis.

3.4. CD38 Expression Increased in Patients with IBD and in the Mouse Colitis Models

We analyzed CD38 mRNA expression by using the NCBI GEO database. The expression data in the GDS3119 database indicated that the inflamed tissues in UC had higher CD38 expression than the controls (Figure 4(a)). The CD38 expression in the inflamed regions was higher than that in the noninflamed regions (Figures 4(a) and 4(b)). In the DSS-induced colitis model and T cell transport colitis model, CD38 expression gradually increased with the emergence of colitis (Figures 4(c) and 4(d)).

(a)

(b)

(c)

(d)

Figure 4: CD38 mRNA expression in patients with UC and animal colitis models from GEO datasets. (a) Colon mucosa CD38 mRNA expression in NC, UCI, and UCNI patients based on GEO GDS3319 dataset. (b) Colon mucosa CD38 mRNA expression in UCI and UCNI patients based on GEO GDS2642 dataset. (c) Colon CD38 mRNA expression in DSS induced mouse colitis model at 0D, 2D, 4D, and 6D based on GEO GDS3859 dataset. (d) Colon CD38 mRNA expression in T cell transfer mouse colitis model at 0W, 4W, and 8W based on GEO GDS4363 dataset. Significance level: P < 0.05, P < 0.01, and P < 0.001. NC, normal control; CD, Crohn’s disease; UC, ulcerative colitis; UCI, ulcerative colitis inflamed; UCNI, ulcerative colitis noninflamed; D, day; W, week.

We then analyzed CD38 protein expression in colon specimens from patients with CD or UC. CD38 protein expression was higher in patients with CD or UC than in controls (Figures 5(a) and 5(b)). The CD38 expression in inflamed regions was higher than that in noninflamed regions (Figures 5(c) and 5(d)). IHC and immunofluorescence (IF) staining confirmed increased CD38 expression and membrane CD38 distribution (Figures 5(e) and 5(f)). We noted colocalization of CD38 with the macrophage marker F4/80 (Figure 5(f)). CD38 protein expression also increased in the mice with DSS-induced colitis (Supplementary Figure 1).

(a)

(b)

(c)

(d)

(e)

(f)

Figure 5: CD38 is increased in IBD patients. (a) Western blot analysis of CD38 expression in control and UC patients (N=3 per group). (b) Western blot analysis of CD38 expression in 3 matched pairs of inflamed (I) and noninflamed (NI) UC tissues. (c) Western blot analysis of CD38 expression in control and CD patients (N=3 per group). (d) Western blot analysis of CD38 expression in 3 matched pairs of inflamed (I) and noninflamed (NI) CD tissues. (e) Expression of CD38 by IHC (original magnification×100). (f) Immunofluorescence staining of DAPI (blue), CD38 (green), and F4/80 (red) in control, UC, and CD patients (N=3 per group) (original magnification, ×200). CD, Crohn’s disease; UC, ulcerative colitis.

4. Discussion

Since the first study by Barceló-Batllori et al. [26], who identified increased indoleamine-2,3-dioxygenase expression in cytokine-treated colon epithelial cells by using proteomics technology, numerous studies have investigated proteomic changes in IBD. We previously identified several protein peaks in relation to serum samples, which were helpful for differentiating CD from intestinal tuberculosis (ITB) [27]. Isobaric chemical labeling for quantitative proteomics has better quantification performance and reproducibility than other proteomic methods [28]. In this study, we employed TMT-based quantitative proteomics to identify DEPs in patients with IBD.

We identified several previously reported proteins, such as S100A8/9, S100A12, LTF, LCN2, and MMP9, most of which are used to evaluate the disease activity of IBD [29–32]. We also identified several novel proteins associated with IBD; CH3L1, CD38, and OLFM4 showed increased levels, whereas ITLN1 showed decreased levels. Previous DNA microarray analysis has shown that CH3L1 is upregulated in inflamed mucosa [33]; our result is consistent with these microarray results. Several studies also revealed that fecal CHI3L1 aids in predicting the severity and activity of intestinal inflammation in both pediatric and adult IBD [34, 35]. However, fecal CHI3L1 analysis has not been analyzed in Asian populations. OLFM4 protein expression was found to increase by 1.7 folds in CD and 3.7 folds in UC [36], which is similar to our results. ITLN1 is a lactoferrin receptor that can recognize microbial glycans in the intestine [37]. Previous studies revealed that serum ITLN1 levels decrease in IBD and are negatively correlated with its disease activity [38]. However, the role of ITLN1 in IBD pathogenesis is still unclear.

Differential diagnosis between CD and UC is important for guiding treatment and follow-up. In the current study, we identified 17 proteins that showed differences in expression between CD and UC. Among these, ACE2 and ACE showed much higher expression in patients with CD than in patients with UC. Both ACE2 and ACE are associated with the development of organ fibrosis [39, 40] and CD is characterized by subepithelial fibrosis in some patients, which might explain the increased ACE2 and ACE levels in CD. However, the functions of the proteins identified were unclear and need to be confirmed in future studies.

Proteomic analysis indicated that the expression of many proteins involved in NAD metabolism and signaling showed changes, suggesting that NAD metabolism and signaling are associated with the gut inflammation noted in IBD. NAD is a major coenzyme in bioenergetic processes, including oxidative phosphorylation and energy homeostasis [41]. NAD is also the substrate for NAD-cleaving enzymes such as poly (ADP-ribose) polymerases (PARPs), sirtuins (SIRTs), and cADP-ribose synthases such as CD38 [42–44]. NAD cleavage by these enzymes is important for many physiological processes. NAD synthesis consists of two pathways, the de novo synthesis pathway and salvage synthesis pathway, with the latter playing an important role in mammals. In the salvage pathway, NAMPT is the key enzyme catalyzing NAD synthesis. A previous proteomic study revealed that NAMPT levels increase in the inflamed colonic mucosa of patients with IBD [45], which was also confirmed by our study. Romana et al. reported that the NAMPT inhibitor FK866 alleviates the PARP/SIRT-mediated inflammatory response and alters macrophage polarization in DSS-induced colitis in mice [25]. NAMPT inhibition leads to decreased CD38⁺ immune cell infiltration into the inflamed colon. However, the roles of the other enzymes identified by our study in the pathogenesis of colitis are not clear.

The CD38 expression level has been further confirmed by validation studies. CD38 is an ectoenzyme that catalyzes the synthesis of cADPR and NAADP from NAD+ [46]. CD38-cADPR signaling can mediate airway hyperresponsiveness by increasing calcium release in airway smooth muscle cells [22]. CD38 is also involved in multiple myeloma; antibodies against CD38, including daratumumab and MOR202, are promising therapeutics for multiple myeloma [23]. Using microarray analysis, Chang et al. [21] found that CD38 increased in RA synovial tissues. Recently, a study using RNA sequencing also revealed that CD38 was significantly upregulated in the synovial tissue of patients with RA at various stages [47]. Furthermore, their ex vivo experiments showed that daratumumab effectively depletes plasma cells in peripheral blood mononuclear cells (PBMCs) and that CD38 inhibition can be a novel treatment option for both RA and systemic lupus erythematosus (SLE). CD38−/− mice have shown decreased immune cell infiltration and mild colitis symptoms upon DSS treatment [24]. Shu et al. reported that CD38 expression increased in macrophages upon LPS stimulation and CD38 suppression inhibited macrophage M1 polarization and activation of nuclear factor-κB (NF-κB) signaling [48], suggesting that CD38 expression in macrophages is proinflammatory. Our results showed that CD38 was localized in F4/80⁺ macrophages; however, we could not exclude the distribution of CD38 within other cell types. The molecular mechanisms underlying the effect of CD38 in intestinal macrophages in colitis require further research.

5. Conclusion

Using TMT proteomic quantification, the current study identified proteins that were differentially expressed between patients with IBD and controls. We found that proteins involved in the NAD metabolism and signaling pathway showed significant alterations in IBD; of these, the expression of CD38 was validated. Further studies are required to clarify the mechanisms underlying the promotion of intestinal inflammation by CD38 and to determine whether CD38 inhibition can be used as a treatment option for IBD.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Acknowledgments

The authors thank Ming Yang and Lihua Chen for biopsy sample collection and Huatuo Zhu for the technical assistance. This work was supported by the Medical Science Research Foundation of Health Bureau of Zhejiang Province (WKJ-ZJ-1516), the National Key Research and Development Program (2017YFC1200100), and the National Natural Science Foundation of China (81400589).

Supplementary Materials

Supplementary 1. Materials and Methods: technical details regarding the instrument parameters and operational process of TMT labeling, high pH reversed phase fractionation, and LC-MS/MS analysis.

Supplementary 2. Table 1: the TMT-labeling design for controls, patients with UC, and patients with CD.

Supplementary 3. Table 2: proteins used as disease markers and upregulated in UC or CD.

Supplementary 4. Figure 1: CD38 protein expression increased in mice with DSS-induced colitis.

Supplementary 5. Excel files: quantitation values, significance in the different comparisons performed, peptides identified, and so forth.

References

J. Burisch, T. Jess, M. Martinato, and P. L. Lakatos, “The burden of inflammatory bowel disease in Europe,” Journal of Crohn's and Colitis, vol. 7, no. 4, pp. 322–337, 2013.

L. Prideaux, M. A. Kamm, P. P. De Cruz, F. K. L. Chan, and S. C. Ng, “Inflammatory bowel disease in Asia: a systematic review,” Journal of Gastroenterology and Hepatology, vol. 27, no. 8, pp. 1266–1280, 2012.

S. C. Ng, H. Y. Shi, N. Hamidi et al., “Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies,” The Lancet, vol. 390, no. 10114, pp. 2769–2778, 2017.

J. H. Park, L. Peyrin-Biroulet, M. Eisenhut, and J. I. Shin, “IBD immunopathogenesis: A comprehensive review of inflammatory molecules,” Autoimmunity Reviews, vol. 16, no. 4, pp. 416–426, 2017.

R. J. Xavier and D. K. Podolsky, “Unravelling the pathogenesis of inflammatory bowel disease,” Nature, vol. 448, no. 7152, pp. 427–434, 2007.

M. F. Neurath, “New targets for mucosal healing and therapy in inflammatory bowel diseases,” Mucosal Immunology, vol. 7, no. 1, pp. 6–19, 2014.

W. J. Sandborn, S. Ghosh, J. Panes et al., “Tofacitinib, an oral Janus kinase inhibitor, in active ulcerative colitis,” The New England Journal of Medicine, vol. 367, no. 7, pp. 616–624, 2012.

W. J. Sandborn, B. G. Feagan, and P. Rutgeerts, “Vedolizumab as induction and maintenance therapy for Crohn's disease,” The New England Journal of Medicine, vol. 369, no. 8, pp. 711–721, 2013.

G. Monteleone, M. F. Neurath, S. Ardizzone et al., “Mongersen, an oral SMAD7 antisense oligonucleotide, and crohn's disease,” The New England Journal of Medicine, vol. 372, no. 12, pp. 1104–1113, 2015.

K. Geboes, J.-F. Colombel, A. Greenstein et al., “Indeterminate colitis: A review of the concept - What's in a name?” Inflammatory Bowel Diseases, vol. 14, no. 6, pp. 850–857, 2008.

W. J. Tremaine, “Is indeterminate colitis determinable?” Current Fungal Infection Reports, vol. 14, no. 2, pp. 162–165, 2012.

M. Farmer, R. E. Petras, L. E. Hunt, J. E. Janosky, and S. Galandiuk, “The importance of diagnostic accuracy in colonic inflammatory bowel disease,” American Journal of Gastroenterology, vol. 95, no. 11, pp. 3184–3188, 2000.

M. Ferrante, L. Henckaerts, M. Joossens et al., “New serological markers in inflammatory bowel disease are associated with complicated disease behaviour,” Gut, vol. 56, no. 10, pp. 1394–1403, 2007.

G. E. Reese, V. A. Constantinides, C. Simillis et al., “Diagnostic precision of anti-Saccharomyces cerevisiae antibodies and perinuclear antineutrophil cytoplasmic antibodies in inflammatory bowel disease,” American Journal of Gastroenterology, vol. 101, no. 10, pp. 2410–2422, 2006.

A. Shkoda, T. Werner, H. Daniel, M. Gunckel, G. Rogler, and D. Haller, “Differential protein expression profile in the intestinal epithelium from patients with inflammatory bowel disease,” Journal of Proteome Research, vol. 6, no. 3, pp. 1114–1125, 2007.

X. Zhao, B. Kang, C. Lu et al., “Evaluation of P38 MAPK pathway as a molecular signature in ulcerative colitis,” Journal of Proteome Research, vol. 10, no. 5, pp. 2216–2225, 2011.

M. Hatsugai, M. S. Kurokawa, T. Kouro et al., “Protein profiles of peripheral blood mononuclear cells are useful for differential diagnosis of ulcerative colitis and Crohn’s disease,” Journal of Gastroenterology, vol. 45, no. 5, pp. 488–500, 2010.

R. Burakoff, V. Pabby, L. Onyewadume et al., “Blood-based biomarkers used to predict disease activity in crohn's disease and ulcerative colitis,” Inflammatory Bowel Diseases, vol. 21, no. 5, pp. 1132–1140, 2015.

M. Gazouli, A. K. Anagnostopoulos, A. Papadopoulou et al., “Serum protein profile of Crohn's disease treated with infliximab,” Journal of Crohn's and Colitis, vol. 7, no. 10, pp. e461–e470, 2013.

C. N. Bernstein, M. Fried, J. H. Krabshuis et al., “World gastroenterology organization practice guidelines for the diagnosis and management of IBD in 2010,” Inflammatory Bowel Diseases, vol. 16, no. 1, pp. 112–124, 2010.

X. Chang, L. Yue, W. Liu et al., “CD38 and E2F transcription factor 2 have uniquely increased expression in rheumatoid arthritis synovial tissues,” Clinical & Experimental Immunology, vol. 176, no. 2, pp. 222–231, 2014.

D. A. Deshpande, A. G. P. Guedes, F. E. Lund, S. Subramanian, T. F. Walseth, and M. S. Kannan, “CD38 in the pathogenesis of allergic airway disease: Potential therapeutic targets,” Pharmacology & Therapeutics, vol. 172, pp. 116–126, 2017.

N. W. C. J. Van De Donk, P. G. Richardson, and F. Malavasi, “CD38 antibodies in multiple myeloma: Back to the future,” Blood, vol. 131, no. 1, pp. 13–29, 2018.

M. Schneider, V. Schumacher, T. Lischke et al., “CD38 is expressed on inflammatory cells of the intestine and promotes intestinal inflammation,” Plos One, vol. 10, no. 5, Article ID e0126007, 2015.

R. R. Gerner, V. Klepsch, S. Macheiner et al., “NAD metabolism fuels human and mouse intestinal inflammation,” Gut, pp. 1813–1823, 2017.

S. Barceló-Batllori, M. André, C. Servis et al., “Proteomic analysis of cytokine induced proteins in human intestinal epithelial cells: Implications for inflammatory bowel diseases,” Proteomics, vol. 2, no. 5, pp. 551–560, 2002.

F. Zhang, C. Xu, L. Ning et al., “Exploration of serum proteomic profiling and diagnostic model that differentiate crohn's disease and intestinal tuberculosis,” Plos One, vol. 11, no. 12, Article ID e0167109, 2016.

Z. Li, R. M. Adams, K. Chourey, G. B. Hurst, R. L. Hettich, and C. Pan, “Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ orbitrap velos,” Journal of Proteome Research, vol. 11, no. 3, pp. 1582–1590, 2012.

S. Ikhtaire, M. S. Shajib, W. Reinisch, and W. I. Khan, “Fecal calprotectin: its scope and utility in the management of inflammatory bowel disease,” Journal of Gastroenterology, vol. 51, no. 5, pp. 434–446, 2016.

J. Stallhofer, M. Friedrich, A. Konrad-Zerna et al., “Lipocalin-2 is a disease activity marker in inflammatory bowel disease regulated by IL-17A, IL-22, and TNF-α and modulated by IL23R genotype status,” Inflammatory Bowel Diseases, vol. 21, no. 10, pp. 2327–2340, 2015.

K.-L. Kolho, T. Sipponen, E. Valtonen, and E. Savilahti, “Fecal calprotectin, MMP-9, and human beta-defensin-2 levels in pediatric inflammatory bowel disease,” International Journal of Colorectal Disease, vol. 29, no. 1, pp. 43–50, 2014.

A. A. Soubières and A. Poullis, “Emerging biomarkers for the diagnosis and monitoring of inflammatory bowel diseases,” Inflammatory Bowel Diseases, vol. 22, no. 8, pp. 2016–2022, 2016.

C.-C. Chen, J. Pekow, V. Llado et al., “Chitinase 3-like-1 expression in colonic epithelial cells as a potentially novel marker for colitis-associated neoplasia,” The American Journal of Pathology, vol. 179, no. 3, pp. 1494–1503, 2011.

T. Aomatsu, H. Imaeda, K. Matsumoto et al., “Faecal chitinase 3-like-1: A novel biomarker of disease activity in paediatric inflammatory bowel disease,” Alimentary Pharmacology & Therapeutics, vol. 34, no. 8, pp. 941–948, 2011.

A. Buisson, E. Vazeille, R. Minet-Quinard et al., “Faecal chitinase 3-like 1 is a reliable marker as accurate as faecal calprotectin in detecting endoscopic activity in adult patients with inflammatory bowel diseases,” Alimentary Pharmacology & Therapeutics, vol. 43, no. 10, pp. 1069–1079, 2016.

M. Gersemann, S. Becker, S. Nuding et al., “Olfactomedin-4 is a glycoprotein secreted into mucus in active IBD,” Journal of Crohn's and Colitis, vol. 6, no. 4, pp. 425–434, 2012.

D. A. Wesener, K. Wangkanont, R. McBride et al., “Recognition of microbial glycans by human intelectin-1,” Nature Structural & Molecular Biology, vol. 22, no. 8, pp. 603–610, 2015.

Y. Lu, L. Zhou, L. Liu et al., “Serum omentin-1 as a disease activity marker for crohn's disease,” Disease Markers, vol. 2014, Article ID 162517, 2014.

A. Hirose, M. Ono, T. Saibara et al., “Angiotensin II type 1 receptor blocker inhibits fibrosis in rat nonalcoholic steatohepatitis,” Hepatology, vol. 45, no. 6, pp. 1375–1381, 2007.

C. H. Österreicher, K. Taura, S. De Minicis et al., “Angiotensin-converting-enzyme 2 inhibits liver fibrosis in mice,” Hepatology, vol. 50, no. 3, pp. 929–938, 2009.

C. Cantó, K. J. Menzies, and J. Auwerx, “NAD+ metabolism and the control of energy homeostasis: a balancing act between mitochondria and the nucleus,” Cell Metabolism, vol. 22, no. 1, pp. 31–53, 2015.

V. Schreiber, F. Dantzer, J. C. Ame, and G. de Murcia, “Poly(ADP-ribose): novel functions for an old molecule,” Nature Reviews Molecular Cell Biology, vol. 7, no. 7, pp. 517–528, 2006.

N. M. Borradaile and G. Pickering, “NAD+, sirtuins, and cardiovascular disease,” Current Pharmaceutical Design, vol. 15, no. 1, pp. 110–117, 2009.

S. Partida-Sanchez, A. Gasser, R. Fliegert et al., “Chemotaxis of mouse bone marrow neutrophils and dendritic cells is controlled by ADP-ribose, the major product generated by the CD38 enzyme reaction,” The Journal of Immunology, vol. 179, no. 11, pp. 7827–7839, 2007.

A. E. Starr, S. A. Deeke, Z. Ning et al., “Proteomic analysis of ascending colon biopsies from a paediatric inflammatory bowel disease inception cohort identifies protein biomarkers that differentiate Crohn's disease from UC,” Gut, vol. 66, no. 9, pp. 1573–1583, 2017.

F. E. Lund, D. A. Cockayne, T. D. Randall, N. Solvason, F. Schuber, and M. C. Howard, “CD38: A new paradigm in lymphocyte activation and signal transduction,” Immunological Reviews, vol. 161, pp. 79–93, 1998.

S. Cole, A. Walsh, X. Yin et al., “Integrative analysis reveals CD38 as a therapeutic target for plasma cell-rich pre-disease and established rheumatoid arthritis and systemic lupus erythematosus,” Arthritis Research & Therapy, vol. 20, no. 1, p. 85, 2018.

B. Shu, Y. Feng, Y. Gui et al., “Blockade of CD38 diminishes lipopolysaccharide-induced macrophage classical activation and acute kidney injury involving NF-κB signaling suppression,” Cellular Signalling, vol. 42, pp. 249–258, 2018.