Journal of Immunology Research

Journal of Immunology Research / 2018 / Article
Special Issue

Inflammation in Cancer: Part of the Problem or Part of the Solution?

View this Special Issue

Research Article | Open Access

Volume 2018 |Article ID 9208274 |

Maria Dobre, Elena Milanesi, Teodora Ecaterina Mănuc, Dorel Eugen Arsene, Cristian George Ţieranu, Carlo Maj, Gabriel Becheanu, Mircea Mănuc, "Differential Intestinal Mucosa Transcriptomic Biomarkers for Crohn’s Disease and Ulcerative Colitis", Journal of Immunology Research, vol. 2018, Article ID 9208274, 10 pages, 2018.

Differential Intestinal Mucosa Transcriptomic Biomarkers for Crohn’s Disease and Ulcerative Colitis

Academic Editor: Donato Zipeto
Received09 Jul 2018
Accepted04 Sep 2018
Published17 Oct 2018


Genetic research has shaped the inflammatory bowel disease (IBD) landscape identifying nearly two hundred risk loci. Nonetheless, the identified variants rendered only a partial success in providing criteria for the differential diagnosis between ulcerative colitis (UC) and Crohn’s disease (CD). Transcript levels from affected intestinal mucosa may serve as tentative biomarkers for improving classification and diagnosis of IBD. The aim of our study was to identify gene expression profiles specific for UC and CD, in endoscopically affected and normal intestinal colonic mucosa from IBD patients. We evaluated a panel of 84 genes related to the IBD-inflammatory pathway on 21 UC and 22 CD paired inflamed and not inflamed mucosa and on age-matched normal mucosa from 21 non-IBD controls. Two genes in UC (CCL11 and MMP10) and two in CD (C4BPB and IL1RN) showed an upregulation trend in both noninflamed and inflamed mucosa compared to controls. Our results suggest that the transcript levels of CCL11, MMP10, C4BPB, and IL1RN are candidate biomarkers that could help in clinical practice for the differential diagnosis between UC and CD and could guide new research on future therapeutic targets.

1. Introduction

Inflammatory bowel diseases (IBD) are a distinct class of gastrointestinal diseases mainly represented by Crohn’s disease (CD) and ulcerative colitis (UC). These are chronic diseases characterized by a relapsing remitting course with an increasingly high incidence and prevalence worldwide [1]. The current accepted model for IBD etiology implies the existence of a genetic predisposition, perturbations in the intestinal barrier components, and altered microbiota, which combined will lead to an aberrant immune response [2]. Distinguishing between the two diseases represents a problem in clinical practice due to some similarities in endoscopic and morphological aspects which in turn will lead to a change in diagnosis throughout the course of disease [3]. However, some fundamental differences between CD and UC have been reported: UC is characterized by diffuse inflammation confined to the colorectal mucosa, whereas in CD, the inflammation is discontinuous, transmural, and can affect the entire gastrointestinal tract. Moreover, CD patients often present complications like intestinal strictures, fistulas, and abscesses [4]. Despite these differences, physiopathological mechanisms, clinical criteria, and therapeutical strategies considerably overlap, but CD and UC seem to be triggered and maintained by differential molecular mechanisms, which are not completely known.

Genetic studies in IBD have gained importance during the past decade since endoscopic assessment and biopsies provide limited data regarding early disease activity and factors for relapse. The candidate gene approach, genome-wide association studies, and meta-analyses have contoured the genetic background of these disorders, revealing more than 200 risk loci in both European and non-European individuals [5]. However, previous studies showed that many of these loci are shared between CD and UC [6], and no specific genetic markers entered clinical practice yet.

A number of candidate gene expression studies, RNA sequencing, and microarray studies on mucosa from IBD patients have been published in the last years with the attempt to find a specific profile able to discriminate UC and CD. Gene expression analysis of tissue samples from affected and nonaffected individuals can help in discovering important events involved in disease pathogenesis. For example, individual mRNA levels can be sensitive markers for improving classification and diagnosis, identifying new therapeutic targets, and providing prognostic information [7].

Studies conducted so far analyzing the expression levels of cytokines and transcription factors in mucosa revealed that CD has been associated with an impairment of Th1/Th17 response [8], whereas UC has been associated with a Th2/NKT cell response [9]. Other genes have been indicated as putative differential biomarkers, including α-defensin-5 [10], circadian genes [11], TNFAIP3, PIGR, TNF, and PIGR [12]. Other studies based on RNA-seq approaches revealed important transcriptomic differences between normal mucosa, noninflamed CD mucosa, and inflamed CD mucosa [13] as well as differences among colon biopsies from CD patients, UC patients, and non-IBD controls [4].

In this study, we aimed to identify the inflammatory signature specific for UC and CD both in endoscopically inflamed and not inflamed mucosa and how the type of therapy can influence the gene expression profile in Romanian patients. To address these questions, we evaluated the gene expression profile of a panel of 84 selected genes (previously associated to IBD) in paired mucosa samples of 21 UC and 22 CD patients, and we compared them with the profiles obtained in a group of 21 non-IBD healthy controls.

2. Materials and Methods

2.1. Patients

Forty-three IBD patients (21 UC and 22 CD) and 21 non-IBD controls have been enrolled in the study at the Department of Gastroenterology and Hepatology, “Elias” Emergency University Hospital and at the “Fundeni” Clinical Institute of Bucharest, Romania. In terms of disease location, patients with CD had colonic and ileocolic forms of the disease. All the patients and controls were of Romanian origin. Written informed consent was obtained from all participants prior to biopsy collection, and the study was approved by the local ethics committees. The diagnosis had been made based on clinical, endoscopic, and histological criteria according to European Crohn’s and Colitis Organization Guidelines [3]. From each UC and CD patients, paired colonic inflamed mucosa (IM) and macroscopically colonic noninflamed mucosa (NM) were obtained during a colonoscopy. We defined the inflammation status based on the presence of erythema, ulcerations, and bleeding of the mucosa. A biopsy of a normal-looking colonic mucosa was obtained also from a group of non-IBD controls during a colonoscopy screening. Exclusion criteria for non-IBD controls were as follow: (1) presence of digestive symptoms, (2) current or previous nonsteroidal anti-inflammatory treatments (within the past 3 months), and (3) current or previous anticoagulant/antiplatelet treatments (within the past 3 months). The characteristics of the three groups are reported in Table 1.

CTRL ()UC ()CD ()

Sex, (%)
Male9 (42.9)16 (76.2)13 (59.1)
Female12 (57.1)5 (23.8)9 (40.9)
Age, yrs, mean ± SD46.5 ± 16.744.4 ± 12.845.1 ± 15.1
Medications at tissue acquisition (%)
None21 (100)3 (14.3)4 (18.2)
Biological2 (9.5)4 (18.2)
5-ASA14 (66.7)7 (31.8)
Cortisone2 (9.1)
Polytherapy2 (9.5)5 (22.7)

2.2. Total RNA Isolation and qPCR

Total RNA isolation from fresh-frozen tissues preserved in RNA later was performed using RNeasy mini Kit (Qiagen), according to the manufacturer’s protocols. The quantity and quality of RNA were determined using the NanoDrop 2000 (Thermo Scientific). An amount of 600 ng of RNA was reverse transcribed to cDNA using the RT2 First Strand Kit (Qiagen). The Human Crohn’s Disease RT2 Profiler PCR Array (PAHS-169Z, Qiagen), using SYBR Green chemistry, evaluated the expression of 84 key genes, according to the manufacturer’s protocol, on the ABI-7500 fast instrument (Applied Biosystems). The expression levels of each gene were normalized on the geometric mean values of two housekeeping genes (GAPDH and HPRT1) based on RefFinder algorithm ( [14] analysis of five candidate reference genes (ACTB, B2M, GAPDH, HPRT1, and RPLP0).

2.3. Statistical Analysis

qRT-PCR data analysis was conducted using the Statistical Package for Social Science (SPSS version 17.0). Categorical variables were tested by means of the chi-square test, and continuous variables with the -test. Paired -test was used to assess difference in gene expression levels of IM and NM.

3. Results

The group of patients and controls was homogeneous for age () and sex (χ2 = 4.880, ) distribution, and the UC and CD groups did not statistically differ for the class of treatment (χ2 = 6.409, ).

3.1. Gene Expression Alterations in Paired Inflamed and Noninflamed Mucosa of UC and CD Patients

Gene expression analysis was performed on 21 pairs of tissues representing IMUC and NMUC and 22 pairs of tissues representing IMCD and NMCD. In IM, 11 genes out of 84 were found differentially overexpressed both in UC and CD compared with the paired NM. Thirty-three transcripts were found specifically altered only in UC patients (two downregulated and 31 upregulated). Results are shown in Table 2.

Paired IM vs. NMPaired IM vs. NM
FC valueFC value

C3Complement C35.480.0106
C4BPBComplement component 4 binding protein beta5.86<0.00012.980.0090
CCL11C-C motif chemokine ligand 112.010.01212.270.0045
CCL20C-C motif chemokine ligand 204.050.0003
CD55CD55 molecule (Cromer blood group)3.63<0.0001
CHI3L1Chitinase 3 like 116.960.00454.190.0328
CR2Complement C3d receptor 27.000.0150
CXCL1C-X-C motif chemokine ligand 117.99<0.00016.820.0413
CXCL10C-X-C motif chemokine ligand 103.230.0008
CXCL11C-X-C motif chemokine ligand 118.930.00014.320.0380
CXCL2C-X-C motif chemokine ligand 214.98<0.0001
CXCL3C-X-C motif chemokine ligand 39.79<0.0001
CXCL9C-X-C motif chemokine ligand 95.160.00025.220.0059
CXCR1C-X-C motif chemokine receptor 119.220.0131
EDN3Endothelin 3−2.920.0048
FPR1Formyl peptide receptor 110.570.0035
IFNGInterferon gamma2.82<0.0001
IL1RNInterleukin 1 receptor antagonist8.150.00185.680.0498
IL23AInterleukin 23 subunit alpha3.11<0.0001
IL2RAInterleukin 2 receptor subunit alpha3.450.0006
CXCL8C-X-C motif chemokine ligand 819.400.0185
ITGB2Integrin subunit beta 22.310.0003
LCN2Lipocalin 213.050.0003
LTBLymphotoxin beta4.010.0007
MMP1Matrix metallopeptidase 19.980.0108
MMP10Matrix metallopeptidase 1014.920.0004
MMP3Matrix metallopeptidase 330.010.0016
MMP7Matrix metallopeptidase 737.370.00366.000.0098
NOS2Nitric oxide synthase 210.990.0005
PCK1Phosphoenolpyruvate carboxykinase 1−6.290.0002
PECAM1Platelet endothelial cell adhesion molecule 12.420.0046
REG1ARegenerating family member 1 alpha10.110.0123
S100A8S100 calcium binding protein A817.910.0018
S100A9S100 calcium binding protein A99.310.0005
SAA1Serum amyloid A162.830.0016
SELLSelectin L5.31<0.0001
SOD2Superoxide dismutase 22.140.00032.030.0429
STAT1Signal transducer and activator of transcription 12.26<0.0001
TDO2Tryptophan 2,3-dioxygenase4.00<0.0001
TFF1Trefoil factor 12.740.0001
TIMP1TIMP metallopeptidase inhibitor 14.48<0.0001
TNFTumor necrosis factor2.580.0022
UBDUbiquitin D7.950.00022.920.0116

3.2. Gene Expression Alterations in CD and UC Patients Compared with Non-IBD Controls

Gene expression analysis was performed on 21 noninflamed and inflamed mucosa from UC patients, 22 from CD, and 21 from healthy controls. Considering a fold change (FC) > |2.0| and a value below 0.05, 32 genes out of 84 were found differentially expressed both in UC and CD compared with C (two downregulated and 30 upregulated), and 17 were specifically altered only in UC patients (four downregulated and 13 upregulated). No gene was found modified only in CD. When comparing the NM tissues vs. C, we found two transcripts upregulated in UC and five upregulated in CD (Table 3). A graphic representation of the results is shown in Figure 1. Genes whose expression differed between NM and controls and are also different comparing paired IM-NM are shown in Figures 2(a) and 2(b).

IM () vs. C ()NM () vs. C ()IM () vs. C ()NM () vs. C ()
FC valueFC valueFC valueFC value

ABCB1ATP binding cassette subfamily B member 1−7.040.0158−3.760.0417
ALDOBAldolase, fructose-bisphosphate B−18.720.0269
C3Complement C33.770.0264
C4BPBComplement component 4 binding protein beta10.05<0.00018.21<0.00012.760.0310
CCL11C-C motif chemokine ligand 113.990.00032.0560.0033.90<0.0001
CCL2C-C motif chemokine ligand 22.580.0413
CCL20C-C motif chemokine ligand 203.360.0009
CCL25C-C motif chemokine ligand 25−13.470.0403
CCR9C-C motif chemokine receptor 9−2.890.0179
CD55CD55 molecule (Cromer blood group)4.63<0.00012.800.0006
CHI3L1Chitinase 3 like 142.550.001639.260.0049
CSTACystatin A2.640.0045
CXCL1C-X-C motif chemokine ligand 112.34<0.000112.600.0240
CXCL10C-X-C motif chemokine ligand 102.850.00139.360.0416
CXCL11C-X-C motif chemokine ligand 117.280.000312.590.0077
CXCL2C-X-C motif chemokine ligand 29.16<0.000110.090.0368
CXCL3C-X-C motif chemokine ligand 37.17<0.00015.740.0123
CXCL9C-X-C motif chemokine ligand 95.36<0.000111.040.0016
CXCR1C-X-C motif chemokine receptor 150.540.010353.310.0152
DEFA5Defensin alpha 5−11.680.0422
DEFA6Defensin alpha 6−12.360.0368
FPR1Formyl peptide receptor 111.090.0031
IFNGInterferon gamma2.890.00013.110.0287
IL13Interleukin 134.080.00462.820.0083
IL17AInterleukin 17A4.310.00322.390.0048
IL1RNInterleukin 1 receptor antagonist12.450.001114.780.02332.60.0233
IL23AInterleukin 23 subunit alpha3.260.0013
IL2RAInterleukin 2 receptor subunit alpha2.610.00262.440.0073
CXCL8C-X-C motif chemokine ligand 824.750.0169
ITGB2Integrin subunit beta 22.320.00051.750.0126
LCN2Lipocalin 219.250.000213.560.00065.9570.0074
LTBLymphotoxin beta2.910.0061
MMP1Matrix metallopeptidase 116.790.0074
MMP10Matrix metallopeptidase 1053.710.00023.60.008522.84<0.0001
MMP3Matrix metallopeptidase 352.130.0013
MMP7Matrix metallopeptidase 7286.330.002787.380.0030
MUC1Mucin 1, cell surface associated2.65<0.00012.790.00172.140.0024
NOS2Nitric oxide synthase 211.930.00067.09<0.0001
PCK1Phosphoenolpyruvate carboxykinase 1−6.49<0.0001−2.360.0077
PECAM1Platelet and endothelial cell adhesion molecule 13.090.00212.570.0060
S100A8S100 calcium binding protein A828.570.0014
S100A9S100 calcium binding protein A916.560.000233.970.03643.610.0257
SELLSelectin L3.870.00033.970.0418
SOD2Superoxide dismutase 22.170.00052.330.0166
TDO2Tryptophan 2,3-dioxygenase3.88<0.0001
TFF1Trefoil factor 13.060.00012.910.0440
TIMP1TIMP metallopeptidase inhibitor 16.12<0.00014.210.0011
UBDUbiquitin D5.190.00064.990.0007
VWFVon Willebrand factor3.17<0.00012.840.0022

3.3. Differences in Gene Expression in IBD Patients on Different Treatments

Due to the limited sample size of the UC and CD groups, we analyzed the treatment effect on gene expression levels considering the entire IBD cohort. Comparing the patients treated with 5-ASA () vs. drug-free patients (), we found that ISG15 ubiquitin-like modifier (ISG15) was downregulated both in inflamed and not inflamed tissues with FC and value of −2.04, in IM and −1.84, in NM. Moreover, we found that the six patients with biologic treatment showed lower levels of serum amyloid A1 (SAA1) with FC of −6.66 and in IM.

Comparing patients with biological treatment vs. 5-ASA, we found that CCR1 was upregulated in IM with FC = 2.1 and and TFF1 was downregulated both in IM and NM with FC = −2.5, and FC = −2.4, , respectively.

Despite the limited size of the two groups, an additional analysis to find a putative effect of the treatment on the candidate genes (IL1RN and C4BP4 for UC and CCL11 and MMP10 for CD) has been performed separately both on UC and CD groups. No changes in IL1RN and C4BP4 levels were found between the three UC patients without treatment and the UC patients in treatment with 5-ASA (, ), biological treatment (, ), or polytherapy (, ). In the CD group, no difference in CCL11 and MMP10 was found comparing the four patients without treatment and the other groups ( in all the comparisons). However, a trend toward significance was observed in MMP10 levels comparing the 4 CD patients without treatment and the group of the seven patients using 5-ASA ().

4. Discussion

Overlapping features have been reported in up to 30% of IBD [15] leading to a not accurate diagnosis and increasing the risk of inappropriate treatment. In this study, we sought to determine whether mucosal gene profile could be used to develop diagnostic biomarker(s) to discriminate between the two main inflammatory bowel diseases (UC and CD) more accurately.

To the best of our knowledge, this is the first study that evaluated 84 transcripts by qRT-PCR considering a larger cohort of participants than previous studies, including paired inflamed and not inflamed tissues from CD and UC as well as a cohort of non-IBD controls.

Using this approach, we identified 17 genes differentially expressed only in the inflamed mucosa from UC that did not differ for the CD patients. A common signature of 32 genes was identified, and no gene specific for CD inflamed mucosa was found.

Among the genes belonging to the common signature, five and two were found differentially expressed comparing the not inflamed mucosa with mucosa from non-IBD controls of CD and UC, respectively.

Interestingly, in UC, CCL11 and MMP10 were increased substantially in non-IBD controls, NM and IM, whereas in CD, this increase was observed for C4BPB and IL1RN. Hence, these four genes seem to be specific markers of UC and CD inflammation levels.

Eotaxin-1 (CCL11), a potent eosinophil chemoattractant that is considered a major contributor to tissue eosinophilia, is a key regulator of intestinal inflammation [16] and seems to be involved both in UC and CD. Indeed, unlike other chemokines, the human mRNA for eotaxin-1 is constitutively expressed in the small intestine and colon [17] where the intestinal myeloid cells seem to be a source [18].

Levels of eotaxin-1 have been found increased in sera from UC patients [1921] as well as in colon biopsies [22]. In line with our findings that suggested an increase according to the inflammation status, a significant increase of its levels was found in patients with active UC but not in the quiescent state [23]. These data suggest that also the peripheral levels may increase accordingly to the inflammation grade as we observed in mucosa.

Increased levels of eotaxin-1 have been found also in the sera from CD patients [19, 20], and our group found that its mucosal mRNA levels were higher in active CD than in controls. However, no changes were observed in the remission state [24] or in UC [25].

Another transcript having a similar trend like CCL11 in UC was MMP10. MMP10 belongs to the human matrix metalloproteinases family consisting of 24 zinc-dependent endopeptidases and is produced by infiltrating myeloid cells. Their levels are transcriptionally upregulated in response to proinflammatory cytokines, and both transcripts and protein levels of some MMPs are demonstrated to be upregulated in inflamed mucosa or serum of IBD patients [26, 27] even in the naïve to treatment subgroup [28]. In addition, increased expression of epithelial MMP10 has been found in colonic mucosa of both UC and CD pediatric patients compared to non-IBD patients [29]. MMP10 was seen as a possible therapeutic target in IBD because its expression had been observed close to the edges of healing ulcers in human specimens of UC [30]. Its influence, however, can be debated since it could have a role in disease resolution but also in the proinflammatory process. In animal models of experimental colitis, MMP10 seems to promote mucosal healing, and in its absence due to persistent colonic inflammation, dysplastic lesions could be promoted [31]. Human genetic studies identified six SNPs across the MMP10 gene associated with UC, suggesting that these genetic variants may play a role in UC susceptibility and clinical outcome [32].

Moving forward to the specific genes associated to CD in our cohort, C4BPB and IL1RN, they will be discussed below.

The C4BPB gene encodes for C4b-binding protein, a multimeric protein that controls the complement cascade. There is one single study for this gene in CD which evaluated the serum level of C4BPB in patients treated with infliximab, revealing that upregulation of this protein is associated with primary nonresponse to this treatment [33]. Our investigation took into account current biologic treatment, but none of the patients included had had a nonresponse status declared. Thus, we can only suggest that increased expression can only be attributed to the inflammatory process.

Finally, our analysis associated the IL1RN (interleukin 1 receptor antagonist) gene with inflammation in CD. The IL1RN gene encodes for a protein member of the interleukin 1 cytokine family. This protein inhibits the interleukin 1 alpha and beta activities and modulates a variety of related immunoinflammatory responses.

Discordant results regarding the associations between IL1RN genetic variants and IBD have been published. Some studies reported significant associations with CD [34, 35] and UC predispositions [36, 37], treatment outcome [38], and age at the onset [39]; on the contrary, other studies did not find any associations [4042]. Interestingly, IL-1RN2 variant has been associated with reduced levels of IL-1ra protein and IL-1RN mRNA in the colonic mucosa from UC patients [43].

Summarizing, against our expectations, only four putative candidate biomarkers able to discriminate UC and CD were found. This can be due to the large gene expression intravariability observed both in the colonic mucosa from non-IBD and IBD groups. Indeed, due to a number of parameters not yet included (histologically active/in remission, duration, and response to treatment), this group intravariability might have increased. Furthermore, the raw data reported that a larger number of genes seemed to be differentially expressed (with high fold difference) without reaching statistical significance due to the high standard deviation. Accordingly, in order to find a more specific signature, the study should be validated in a larger, more homogenous cohort.

Another aim of this study was to evaluate the influence of treatment on the entire IBD cohort. Our results showed a downregulation of ISG15 in patients treated with 5-ASA and a downregulation of SAA 1 in patients with biologic treatment compared to patients without IBD treatment. The effect of different therapeutic agents on IBD gene expression should be assessed in a longitudinal cohort.

The main limitation of this study was the absence of data regarding the clinical scores (MAYO and CDAI) measuring the activity and severity of IBD.

5. Conclusions

In conclusion, we obtained differential intestinal mucosa expression signatures of 17 genes that could specifically characterize the UC inflamed mucosa. Of note, two genes in UC (CCL11 and MMP10) and two in CD (C4BPB and IL1RN) had significantly upregulated expression in the noninflamed and inflamed mucosa compared to controls. Our putative biomarkers, once validated in a larger cohort, could help in clinical practice for the differential diagnosis between UC and CD and could guide new researches on future therapeutic targets.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflict of interests.

Authors’ Contributions

Maria Dobre and Elena Milanesi contributed equally to this work.


The authors thank the patients involved in the study and the endoscopy department team of the hospitals involved for the generous collaboration. This research was supported by projects PN and PN with the support of the Romanian Autoritatea Nationala pentru Cercetare Stiintifica (ANCSI through the “Nucleu” Program).


  1. N. A. Molodecky, I. S. Soon, D. M. Rabi et al., “Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review,” Gastroenterology, vol. 142, no. 1, pp. 46–54.e42, 2012. View at: Publisher Site | Google Scholar
  2. T. Manuc, M. Manuc, and M. Diculescu, “Recent insights into the molecular pathogenesis of Crohn’s disease: a review of emerging therapeutic targets,” Clinical and Experimental Gastroenterology, vol. 9, pp. 59–70, 2016. View at: Publisher Site | Google Scholar
  3. F. Magro, P. Gionchetti, R. Eliakim et al., “Third European evidence-based consensus on diagnosis and management of ulcerative colitis. Part 1: definitions, diagnosis, extra-intestinal manifestations, pregnancy, cancer surveillance, surgery, and ileo-anal pouch disorders,” Journal of Crohn's & Colitis, vol. 11, no. 6, pp. 649–670, 2017. View at: Publisher Site | Google Scholar
  4. K. Holgersen, B. Kutlu, B. Fox et al., “High-resolution gene expression profiling using RNA sequencing in patients with inflammatory bowel disease and in mouse models of colitis,” Journal of Crohn's & Colitis, vol. 9, no. 6, pp. 492–506, 2015. View at: Publisher Site | Google Scholar
  5. Y. Zhang, L. Tian, P. Sleiman, S. Ghosh, H. Hakonarson, and On behalf of the International IBD Genetics Consortium, “Bayesian analysis of genome-wide inflammatory bowel disease data sets reveals new risk loci,” European Journal of Human Genetics, vol. 26, no. 2, pp. 265–274, 2018. View at: Publisher Site | Google Scholar
  6. C. A. Anderson, G. Boucher, C. W. Lees et al., “Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47,” Nature Genetics, vol. 43, no. 3, pp. 246–252, 2011. View at: Publisher Site | Google Scholar
  7. B. K. Dieckgraefe, W. F. Stenson, J. R. Korzenik, P. E. Swanson, and C. A. Harrington, “Analysis of mucosal gene expression in inflammatory bowel disease by parallel oligonucleotide arrays,” Physiological Genomics, vol. 4, no. 1, pp. 1–11, 2000. View at: Publisher Site | Google Scholar
  8. D. C. Baumgart and W. J. Sandborn, “Crohn’s disease,” The Lancet, vol. 380, no. 9853, pp. 1590–1605, 2012. View at: Publisher Site | Google Scholar
  9. I. Ordás, L. Eckmann, M. Talamini, D. C. Baumgart, and W. J. Sandborn, “Ulcerative colitis,” The Lancet, vol. 380, no. 9853, pp. 1606–1619, 2012. View at: Publisher Site | Google Scholar
  10. A. D. Williams, O. Y. Korolkova, A. M. Sakwe et al., “Human alpha defensin 5 is a candidate biomarker to delineate inflammatory bowel disease,” PLoS One, vol. 12, no. 8, article e0179710, 2017. View at: Publisher Site | Google Scholar
  11. O. Palmieri, G. Mazzoccoli, F. Bossa et al., “Systematic analysis of circadian genes using genome-wide cDNA microarrays in the inflammatory bowel disease transcriptome,” Chronobiology International, vol. 32, no. 7, pp. 903–916, 2015. View at: Publisher Site | Google Scholar
  12. M. E. C. Bruno, E. W. Rogier, R. I. Arsenescu et al., “Correlation of biomarker expression in colonic mucosa with disease phenotype in Crohn’s disease and ulcerative colitis,” Digestive Diseases and Sciences, vol. 60, no. 10, pp. 2976–2984, 2015. View at: Publisher Site | Google Scholar
  13. S. N. Hong, J. G. Joung, J. S. Bae et al., “RNA-seq reveals transcriptomic differences in inflamed and noninflamed intestinal mucosa of Crohn’s disease patients compared with normal mucosa of healthy controls,” Inflammatory Bowel Diseases, vol. 23, no. 7, pp. 1098–1108, 2017. View at: Publisher Site | Google Scholar
  14. F. Xie, P. Xiao, D. Chen, L. Xu, and B. Zhang, “miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs,” Plant Molecular Biology, vol. 80, no. 1, pp. 75–84, 2012. View at: Publisher Site | Google Scholar
  15. F. Magro, C. Langner, A. Driessen et al., “European consensus on the histopathology of inflammatory bowel disease,” Journal of Crohn's & Colitis, vol. 7, no. 10, pp. 827–851, 2013. View at: Publisher Site | Google Scholar
  16. T. Adar, S. Shteingart, A. Ben Ya'acov, A. Bar-Gil Shitrit, and E. Goldin, “From airway inflammation to inflammatory bowel disease: eotaxin-1, a key regulator of intestinal inflammation,” Clinical Immunology, vol. 153, no. 1, pp. 199–208, 2014. View at: Publisher Site | Google Scholar
  17. M. Kitaura, T. Nakajima, T. Imai et al., “Molecular cloning of human eotaxin, an eosinophil-selective CC chemokine, and identification of a specific eosinophil eotaxin receptor, CC chemokine receptor 3,” The Journal of Biological Chemistry, vol. 271, no. 13, pp. 7725–7730, 1996. View at: Publisher Site | Google Scholar
  18. M. Lampinen, A. Waddell, R. Ahrens, M. Carlson, and S. P. Hogan, “CD14+CD33+ myeloid cell-CCL11-eosinophil signature in ulcerative colitis,” Journal of Leukocyte Biology, vol. 94, no. 5, pp. 1061–1070, 2013. View at: Publisher Site | Google Scholar
  19. W. Chen, B. Paulus, D. Shu, I. Wilson, and V. Chadwick, “Increased serum levels of eotaxin in patients with inflammatory bowel disease,” Scandinavian Journal of Gastroenterology, vol. 36, no. 5, pp. 515–520, 2001. View at: Publisher Site | Google Scholar
  20. A. Mir, M. Minguez, J. Tatay et al., “Elevated serum eotaxin levels in patients with inflammatory bowel disease,” The American Journal of Gastroenterology, vol. 97, no. 6, pp. 1452–1457, 2002. View at: Publisher Site | Google Scholar
  21. P. Manousou, G. Kolios, V. Valatas et al., “Increased expression of chemokine receptor CCR3 and its ligands in ulcerative colitis: the role of colonic epithelial cells in in vitro studies,” Clinical and Experimental Immunology, vol. 162, no. 2, pp. 337–347, 2010. View at: Publisher Site | Google Scholar
  22. L. A. Coburn, S. N. Horst, R. Chaturvedi et al., “High-throughput multi-analyte Luminex profiling implicates eotaxin-1 in ulcerative colitis,” PLoS One, vol. 8, no. 12, article e82300, 2013. View at: Publisher Site | Google Scholar
  23. T. Adar, S. Shteingart, A. Ben-Ya’acov et al., “The importance of intestinal eotaxin-1 in inflammatory bowel disease: new insights and possible therapeutic implications,” Digestive Diseases and Sciences, vol. 61, no. 7, pp. 1915–1924, 2016. View at: Publisher Site | Google Scholar
  24. M. Dobre, T. E. Mănuc, E. Milanesi et al., “Mucosal CCR1 gene expression as a marker of molecular activity in Crohn’s disease: preliminary data,” Romanian Journal of Morphology and Embryology, vol. 58, no. 4, pp. 1263–1268, 2017. View at: Google Scholar
  25. C. G. Ţieranu, M. Dobre, T. E. Mănuc et al., “Gene expression profile of endoscopically active and inactive ulcerative colitis: preliminary data,” Romanian Journal of Morphology and Embryology, vol. 58, no. 4, pp. 1301–1307, 2017. View at: Google Scholar
  26. M. J. W. Meijer, M. A. C. Mieremet-Ooms, A. M. van der Zon et al., “Increased mucosal matrix metalloproteinase-1, -2, -3 and -9 activity in patients with inflammatory bowel disease and the relation with Crohn’s disease phenotype,” Digestive and Liver Disease, vol. 39, no. 8, pp. 733–739, 2007. View at: Publisher Site | Google Scholar
  27. T. Rath, M. Roderfeld, J. Graf et al., “Enhanced expression of MMP-7 and MMP-13 in inflammatory bowel disease: a precancerous potential?” Inflammatory Bowel Diseases, vol. 12, no. 11, pp. 1025–1035, 2006. View at: Publisher Site | Google Scholar
  28. H. Taman, C. G. Fenton, I. V. Hensel, E. Anderssen, J. Florholmen, and R. H. Paulssen, “Transcriptomic landscape of treatment-naïve ulcerative colitis,” Journal of Crohn's & Colitis, vol. 12, no. 3, pp. 327–336, 2018. View at: Publisher Site | Google Scholar
  29. L. Mäkitalo, K.-L. Kolho, R. Karikoski, H. Anthoni, and U. Saarialho-Kere, “Expression profiles of matrix metalloproteinases and their inhibitors in colonic inflammation related to pediatric inflammatory bowel disease,” Scandinavian Journal of Gastroenterology, vol. 45, no. 7-8, pp. 862–871, 2010. View at: Publisher Site | Google Scholar
  30. M. Vaalamo, M. L. Karjalainen-Lindsberg, P. Puolakkainen, J. Kere, and U. Saarialho-Kere, “Distinct expression profiles of stromelysin-2 (MMP-10), collagenase-3 (MMP-13), macrophage metalloelastase (MMP-12), and tissue inhibitor of metalloproteinases-3 (TIMP-3) in intestinal ulcerations,” The American Journal of Pathology, vol. 152, no. 4, pp. 1005–1014, 1998. View at: Google Scholar
  31. F. L. Koller, E. A. Dozier, K. T. Nam et al., “Lack of MMP10 exacerbates experimental colitis and promotes development of inflammation-associated colonic dysplasia,” Laboratory Investigation, vol. 92, no. 12, pp. 1749–1759, 2012. View at: Publisher Site | Google Scholar
  32. A. R. Morgan, D. Y. Han, W. J. Lam et al., “Genetic variations in matrix metalloproteinases may be associated with increased risk of ulcerative colitis,” Human Immunology, vol. 72, no. 11, pp. 1117–1127, 2011. View at: Publisher Site | Google Scholar
  33. M. Gazouli, A. K. Anagnostopoulos, A. Papadopoulou et al., “Serum protein profile of Crohn’s disease treated with infliximab,” Journal of Crohn's & Colitis, vol. 7, no. 10, pp. e461–e470, 2013. View at: Publisher Site | Google Scholar
  34. B. Stankovic, S. Dragasevic, D. Popovic et al., “Variations in inflammatory genes as molecular markers for prediction of inflammatory bowel disease occurrence,” Journal of Digestive Diseases, vol. 16, no. 12, pp. 723–733, 2015. View at: Publisher Site | Google Scholar
  35. R. D. Mittal, H. K. Bid, and U. C. Ghoshal, “IL-1 receptor antagonist (IL-1Ra) gene polymorphism in patients with inflammatory bowel disease in India,” Scandinavian Journal of Gastroenterology, vol. 40, no. 7, pp. 827–831, 2009. View at: Publisher Site | Google Scholar
  36. D. M. M. Queiroz, A. G. Oliveira, I. E. B. Saraiva et al., “Immune response and gene polymorphism profiles in Crohn’s disease and ulcerative colitis,” Inflammatory Bowel Diseases, vol. 15, no. 3, pp. 353–358, 2009. View at: Publisher Site | Google Scholar
  37. N. A. Tountas, V. Casini–Raggi, H. Yang et al., “Functional and ethnic association of allele 2 of the interleukin-1 receptor antagonist gene in ulcerative colitis,” Gastroenterology, vol. 117, no. 4, pp. 806–813, 1999. View at: Publisher Site | Google Scholar
  38. C. M. Gelbmann, G. Rogler, M. Gierend, V. Gross, J. Schölmerich, and T. Andus, “Association of HLA-DR genotypes and IL-1ra gene polymorphism with treatment failure of budesonide and disease patterns in Crohn’s disease,” European Journal of Gastroenterology & Hepatology, vol. 13, no. 12, pp. 1431–1437, 2001. View at: Publisher Site | Google Scholar
  39. N. E. Daryani, M. Sadr, S. Moossavi et al., “Significance of IL-1RA polymorphism in Iranian patients with inflammatory bowel disease,” Digestive Diseases and Sciences, vol. 60, no. 5, pp. 1389–1395, 2015. View at: Publisher Site | Google Scholar
  40. A. C. Ferreira, S. Almeida, M. Tavares et al., “NOD2/CARD15 and TNFA, but not IL1B and IL1RN, are associated with Crohn’s disease,” Inflammatory Bowel Diseases, vol. 11, no. 4, pp. 331–339, 2005. View at: Publisher Site | Google Scholar
  41. A. Craggs, S. West, A. Curtis, and M. Welf, “Absence of a genetic association between IL-1RN and IL-1B gene polymorphisms in ulcerative colitis and Crohn disease in multiple populations from Northeast England,” Scandinavian Journal of Gastroenterology, vol. 36, no. 11, pp. 1173–1178, 2001. View at: Publisher Site | Google Scholar
  42. R. López-Hernández, M. Valdés, J. A. Campillo et al., “Pro- and anti-inflammatory cytokine gene single-nucleotide polymorphisms in inflammatory bowel disease,” International Journal of Immunogenetics, vol. 42, no. 1, pp. 38–45, 2015. View at: Publisher Site | Google Scholar
  43. M. J. Carter, S. Jones, N. J. Camp et al., “Functional correlates of the interleukin-1 receptor antagonist gene polymorphism in the colonic mucosa in ulcerative colitis,” Genes and Immunity, vol. 5, no. 1, pp. 8–15, 2004. View at: Publisher Site | Google Scholar

Copyright © 2018 Maria Dobre 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.

More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.