Printer Friendly

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

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 [alpha]-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.

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 (PAHS169Z, 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 (http://leonxie.esy.es/RefFinder/) [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 t-test. Paired t-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 (p > 0.05) and sex ([chi square] = 4.880, p = 0.087) distribution, and the UC and CD groups did not statistically differ for the class of treatment ([chi square] = 6.409, p = 0.171).

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.

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) > [absolute value of 2.0] and a p 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).

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 (n = 21) vs. drug-free patients (n = 7), we found that ISG15 ubiquitin-like modifier (ISG15) was downregulated both in inflamed and not inflamed tissues with FC and p value of -2.04, p = 0.003 in IM and -1.84, p = 0.033 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 p = 0.025 in IM.

Comparing patients with biological treatment vs. 5-ASA, we found that CCR1 was upregulated in IM with FC = 2.1 and p = 0.005 and TFF1 was downregulated both in IM and NM with FC = -2.5, p = 0.001 and FC = -2.4, p = 0.004, 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 (p = 0.704, p = 0.718), biological treatment (p = 0.384, p = 0.567), or polytherapy (p = 0.891, p = 0.680). In the CD group, no difference in CCL11 and MMP10 was found comparing the four patients without treatment and the other groups (p > 0.05 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 (p = 0.056).

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 [19-21] 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 naive 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 [40-42]. Interestingly, IL-1RN*2 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.

https://doi.org/10.1155/2018/9208274

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.

Acknowledgments

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 16.22.03.01/2016 and PN 18.21.01.05/2018 with the support of the Romanian Autoritatea Nationala pentru Cercetare Stiintifica (ANCSI through the "Nucleu" Program).

References

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[8] D. C. Baumgart and W. J. Sandborn, "Crohn's disease," The Lancet, vol. 380, no. 9853, pp. 1590-1605, 2012.

[9] I. Ordas, L. Eckmann, M. Talamini, D. C. Baumgart, and W. J. Sandborn, "Ulcerative colitis," The Lancet, vol. 380, no. 9853, pp. 1606-1619, 2012.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[24] M. Dobre, T. E. Manuc, 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.

[25] C. G. Tieranu, M. Dobre, T. E. Manuc 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.

[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.

[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.

[28] H. Taman, C. G. Fenton, I. V. Hensel, E. Anderssen, J. Florholmen, and R. H. Paulssen, "Transcriptomic landscape of treatment-naive ulcerative colitis," Journal of Crohn's & Colitis, vol. 12, no. 3, pp. 327-336, 2018.

[29] L. Makitalo, 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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[38] C. M. Gelbmann, G. Rogler, M. Gierend, V. Gross, J. Scholmerich, 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.

[39] N. E. Daryani, M. Sadr, S. Moossavi et al., "Significance of IL1RA polymorphism in Iranian patients with inflammatory bowel disease," Digestive Diseases and Sciences, vol. 60, no. 5, pp. 1389-1395, 2015.

[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.

[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.

[42] R. Lopez-Hernandez, M. Valdes, 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.

[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.

Maria Dobre, (1) Elena Milanesi (ID), (1) Teodora Ecaterina Manuc (ID), (2) Dorel Eugen Arsene, (1,3) Cristian George Jieranu, (4,5) Carlo Maj (ID), (6) Gabriel Becheanu, (2,4) and Mircea Manuc (2,4)

(1) Victor Babes National Institute of Pathology, 050096 Bucharest, Romania

(2) Fundeni Clinical Institute, 022328 Bucharest, Romania

(3) National Institute of Neurology and Neurovascular Diseases, 041914 Bucharest, Romania

(4) Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania

(5) Elias Emergency University Hospital, 011461 Bucharest, Romania

(6) Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Germany

Correspondence should be addressed to Teodora Ecaterina Manuc; teodora.manuc@gmail.com

Received 9 July 2018; Accepted 4 September 2018; Published 17 October 2018

Guest Editor: Donato Zipeto

Caption: Figure 1: Venn diagram showing the genes differentially expressed across the performed different comparisons with controls. Genes marked with asterisk (*) are differentially expressed in the paired IM-NM analysis. IM = inflamed mucosa; NM = noninflamed mucosa.

Caption: Figure 2: Bar graphs represent the mean of the 2A-ACT values, and error bars represent the standard error. The graphs show the genes differentially expressed comparing inflamed mucosa (IM) vs. noninflamed mucosa (NM) IM vs. NM (paired), IM vs. C, and NM vs. C in UC (a) and in CD (b). C = non-IBD controls; IM = inflamed mucosa; NM = noninflamed mucosa.
Table 1: Clinical and demographical parameters of individuals
involved in the study.

                               CTRL (n = 21)         UC (n = 21)

Sex, n (%)
Male                              9 (42.9)            16 (76.2)
Female                           12 (57.1)             5 (23.8)
Age, yrs, mean [+ or -] SD   46.5 [+ or -] 16.7   44.4 [+ or -] 12.8
Medications at tissue
  acquisition n (%)
None                              21 (100)             3 (14.3)
Biological                           --                2 (9.5)
5-ASA                                --               14 (66.7)
Cortisone                            --                   --
Polytherapy                          --                2 (9.5)

                                CD (n = 22)

Sex, n (%)
Male                             13 (59.1)
Female                            9 (40.9)
Age, yrs, mean [+ or -] SD   45.1 [+ or -] 15.1
Medications at tissue
  acquisition n (%)
None                              4 (18.2)
Biological                        4 (18.2)
5-ASA                             7 (31.8)
Cortisone                         2 (9.1)
Polytherapy                       5 (22.7)

Table 2: The table shows the transcripts that differed by >2.0 fold
with p < 0.05 in inflamed mucosa (IM) vs. noninflamed mucosa (NM)
in UC and CD patients. Genes are arranged by alphabetic order.
Italic fonts indicate genes differentially expressed only in IM
from UC.

Gene                        Description

C3                         Complement C3
C4BPB       Complement component 4 binding protein beta
CCL11              C-C motif chemokine ligand 11
CCL20              C-C motif chemokine ligand 20
CD55             CD55 molecule (Cromer blood group)
CHI3L1                   Chitinase 3 like 1
CR2                  Complement C3d receptor 2
CXCL1              C-X-C motif chemokine ligand 1
CXCL10            C-X-C motif chemokine ligand 10
CXCL11            C-X-C motif chemokine ligand 11
CXCL2              C-X-C motif chemokine ligand 2
CXCL3              C-X-C motif chemokine ligand 3
CXCL9              C-X-C motif chemokine ligand 9
CXCR1             C-X-C motif chemokine receptor 1
EDN3                        Endothelin 3
FPR1                 Formyl peptide receptor 1
IFNG                      Interferon gamma
IL1RN            Interleukin 1 receptor antagonist
IL23A               Interleukin 23 subunit alpha
IL2RA           Interleukin 2 receptor subunit alpha
CXCL8              C-X-C motif chemokine ligand 8
ITGB2                 Integrin subunit beta 2
LCN2                        Lipocalin 2
LTB                       Lymphotoxin beta
LYZ                           Lysozyme
MMP1                 Matrix metallopeptidase 1
MMP10                Matrix metallopeptidase 10
MMP3                 Matrix metallopeptidase 3
MMP7                 Matrix metallopeptidase 7
NOS2                  Nitric oxide synthase 2
PCK1            Phosphoenolpyruvate carboxykinase 1
PECAM1     Platelet endothelial cell adhesion molecule 1
REG1A            Regenerating family member 1 alpha
S100A8            S100 calcium binding protein A8
S100A9            S100 calcium binding protein A9
SAA1                      Serum amyloid A1
SELL                         Selectin L
SOD2                   Superoxide dismutase 2
STAT1    Signal transducer and activator of transcription 1
TDO2                 Tryptophan 2,3-dioxygenase
TFF1                      Trefoil factor 1
TIMP1            TIMP metallopeptidase inhibitor 1
TNF                    Tumor necrosis factor
UBD                         Ubiquitin D

                 UC               CD

Gene     Paired IM vs. NM   Paired IM vs. NM

          FC     p value      FC    p value

C3       5.48     0.0106
C4BPB    5.86    <0.0001     2.98   0.0090
CCL11    2.01     0.0121     2.27   0.0045
CCL20    4.05     0.0003
CD55     3.63    <0.0001
CHI3L1   16.96    0.0045     4.19   0.0328
CR2      7.00     0.0150
CXCL1    17.99   <0.0001     6.82   0.0413
CXCL10   3.23     0.0008
CXCL11   8.93     0.0001     4.32   0.0380
CXCL2    14.98   <0.0001
CXCL3    9.79    <0.0001
CXCL9    5.16     0.0002     5.22   0.0059
CXCR1    19.22    0.0131
EDN3     -2.92    0.0048
FPR1     10.57    0.0035
IFNG     2.82    <0.0001
IL1RN    8.15     0.0018     5.68   0.0498
IL23A    3.11    <0.0001
IL2RA    3.45     0.0006
CXCL8    19.40    0.0185
ITGB2    2.31     0.0003
LCN2     13.05    0.0003
LTB      4.01     0.0007
LYZ      2.06     0.0009     2.16   0.0344
MMP1     9.98     0.0108
MMP10    14.92    0.0004
MMP3     30.01    0.0016
MMP7     37.37    0.0036     6.00   0.0098
NOS2     10.99    0.0005
PCK1     -6.29    0.0002
PECAM1   2.42     0.0046
REG1A    10.11    0.0123
S100A8   17.91    0.0018
S100A9   9.31     0.0005
SAA1     62.83    0.0016
SELL     5.31    <0.0001
SOD2     2.14     0.0003     2.03   0.0429
STAT1    2.26    <0.0001
TDO2     4.00    <0.0001
TFF1     2.74     0.0001
TIMP1    4.48   <0.0001
TNF      2.58     0.0022
UBD      7.95     0.0002     2.92   0.0116

Table 3: The table shows the transcripts that differed by >2.0 fold
with p < 0.05 in inflamed mucosa (IM) and noninflamed mucosa (NM)
of UC and CD patients compared with healthy controls. Genes are
arranged by alphabetic order. Italic fonts indicate genes
differentially expressed both in NM and IM compared to controls.

Gene                        Description

ABCB1        ATP binding cassette subfamily B member 1
ALDOB            Aldolase, fructose-bisphosphate B
C3                         Complement C3
C4BPB       Complement component 4 binding protein beta
CCL11              C-C motif chemokine ligand 11
CCL2               C-C motif chemokine ligand 2
CCL20              C-C motif chemokine ligand 20
CCL25              C-C motif chemokine ligand 25
CCR9              C-C motif chemokine receptor 9
CD55            CD55 molecule (Cromer blood group)
CHI3L1                  Chitinase 3 like 1
CSTA                        Cystatin A
CXCL1             C-X-C motif chemokine ligand 1
CXCL10            C-X-C motif chemokine ligand 10
CXCL11            C-X-C motif chemokine ligand 11
CXCL2             C-X-C motif chemokine ligand 2
CXCL3             C-X-C motif chemokine ligand 3
CXCL9             C-X-C motif chemokine ligand 9
CXCR1            C-X-C motif chemokine receptor 1
DEFA5                    Defensin alpha 5
DEFA6                    Defensin alpha 6
FPR1                 Formyl peptide receptor 1
IFNG                     Interferon gamma
IL13                      Interleukin 13
IL17A                     Interleukin 17A
IL1RN            Interleukin 1 receptor antagonist
IL23A              Interleukin 23 subunit alpha
IL2RA          Interleukin 2 receptor subunit alpha
CXCL8             C-X-C motif chemokine ligand 8
ITGB2                 Integrin subunit beta 2
LCN2                        Lipocalin 2
LTB                      Lymphotoxin beta
MMP1                 Matrix metallopeptidase 1
MMP10               Matrix metallopeptidase 10
MMP3                 Matrix metallopeptidase 3
MMP7                 Matrix metallopeptidase 7
MUC1             Mucin 1, cell surface associated
N0S2                  Nitric oxide synthase 2
PCK1            Phosphoenolpyruvate carboxykinase 1
PECAM1   Platelet and endothelial cell adhesion molecule 1
S100A8            SI00 calcium binding protein A8
S100A9            SI00 calcium binding protein A9
SELL                        Selectin L
S0D2                  Superoxide dismutase 2
TD02                Tryptophan 2,3-dioxygenase
TFF1                     Trefoil factor 1
TIMP1            TIMP metallopeptidase inhibitor 1
UBD                         Ubiquitin D
VWF                    Von Willebrand factor

                  UC

Gene     IM (n = 21) vs.     NM (n = 21) vs.
         C(n = 21)           C (n = 21)

           FC     p value     FC     p value

ABCB1    -7.04     0.0158
ALDOB    -18.72    0.0269
C3        3.77     0.0264
C4BPB    10.05    <0.0001
CCL11     3.99     0.0003    2.056    0.003
CCL2      2.58     0.0413
CCL20     3.36     0.0009
CCL25    -13.47    0.0403
CCR9     -2.89     0.0179
CD55      4.63    <0.0001
CHI3L1   42.55     0.0016
CSTA      2.64     0.0045
CXCL1    12.34    <0.0001
CXCL10    2.85     0.0013
CXCL11    7.28     0.0003
CXCL2     9.16    <0.0001
CXCL3     7.17    <0.0001
CXCL9     5.36    <0.0001
CXCR1    50.54     0.0103
DEFA5    -11.68    0.0422
DEFA6    -12.36    0.0368
FPR1     11.09     0.0031
IFNG      2.89     0.0001
IL13      4.08     0.0046
IL17A     4.31     0.0032
IL1RN    12.45     0.0011
IL23A     3.26     0.0013
IL2RA     2.61     0.0026
CXCL8    24.75     0.0169
ITGB2     2.32     0.0005
LCN2     19.25     0.0002
LTB       2.91     0.0061
MMP1     16.79     0.0074
MMP10    53.71     0.0002     3.6    0.0085
MMP3     52.13     0.0013
MMP7     286.33    0.0027
MUC1      2.65    <0.0001
N0S2     11.93     0.0006
PCK1     -6.49    <0.0001
PECAM1    3.09     0.0021
S100A8   28.57     0.0014
S100A9   16.56     0.0002
SELL      3.87     0.0003
S0D2      2.17     0.0005
TD02      3.88    <0.0001
TFF1      3.06     0.0001
TIMP1     6.12    <0.0001
UBD       5.19     0.0006
VWF       3.17    <0.0001

                 CD

Gene     IM (n = 22) vs.   NM (n = 22) vs.
         C (n = 21)        C (n = 21)

          FC     p value    FC     p value

ABCB1    -3.76   0.0417
ALDOB
C3
C4BPB    8.21    <0.0001   2.76    0.0310
CCL11    3.90    <0.0001
CCL2
CCL20
CCL25
CCR9
CD55     2.80    0.0006
CHI3L1   39.26   0.0049
CSTA
CXCL1    12.60   0.0240
CXCL10   9.36    0.0416
CXCL11   12.59   0.0077
CXCL2    10.09   0.0368
CXCL3    5.74    0.0123
CXCL9    11.04   0.0016
CXCR1    53.31   0.0152
DEFA5
DEFA6
FPR1
IFNG     3.11    0.0287
IL13     2.82    0.0083
IL17A    2.39    0.0048
IL1RN    14.78   0.0233     2.6    0.0233
IL23A
IL2RA    2.44    0.0073
CXCL8
ITGB2    1.75    0.0126
LCN2     13.56   0.0006    5.957   0.0074
LTB
MMP1
MMP10    22.84   <0.0001
MMP3
MMP7     87.38   0.0030
MUC1     2.79    0.0017    2.14    0.0024
N0S2     7.09    <0.0001
PCK1     -2.36   0.0077
PECAM1   2.57    0.0060
S100A8
S100A9   33.97   0.0364    3.61    0.0257
SELL     3.97    0.0418
S0D2     2.33    0.0166
TD02
TFF1     2.91    0.0440
TIMP1    4.21    0.0011
UBD      4.99    0.0007
VWF      2.84    0.0022
COPYRIGHT 2018 Hindawi Limited
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research Article
Author:Dobre, Maria; Milanesi, Elena; Manuc, Teodora Ecaterina; Arsene, Dorel Eugen; Jieranu, Cristian Geor
Publication:Journal of Immunology Research
Date:Jan 1, 2018
Words:5994
Previous Article:Differentiation and Function of T Cell Subsets in Infectious Diseases.
Next Article:Conventional DCs from Male and Female Lupus-Prone B6.NZM Sle1/Sle2/Sle3 Mice Express an IFN Signature and Have a Higher Immunometabolism That Are...
Topics:

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters