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Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Background : Most array analyses of ulcerative colitis have focused on identifying susceptibility genes for ulcerative colitis.

Aim : To clarify the changes in gene expression during inflammation in ulcerative colitis colon mucosa using cDNA macroarray.

Methods : From 23 ulcerative colitis patients, 16 each of inflamed and non-inflamed specimens (total 32 samples for individual analysis) were obtained by colonoscopic biopsy. Eighteen of the 32 samples, used for pairwise analysis, consisted of nine sample pairs, each pair being from the same patient. We examined expression profiles of approximately 1300 genes with cDNA macroarray. Comparisons were made using two kinds of statistics, t-test and significance analysis of microarray in both analyses. The reproducibility of significant genes from the macroarray analysis was confirmed by real-time ploymerase chain reaction.

Results : We detected five upregulated genes, categorized into proinflammatory genes (MRP14, GROγ and SAA1) and anti-inflammatory genes (TIMP1 and Elafin) in inflamed mucosa, and one upregulated gene (L-FABP) in non-inflamed mucosa.

Conclusions : As the cDNA macroarray analysis in this study exactly reflects the total profile of gene expression in the clinical setting of ulcerative colitis, the genes identified will be directly applicable to diagnostics or as novel therapeutic targets in active ulcerative colitis.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Ulcerative colitis (UC) is a disease of unknown aetiology characterized by frequent relapses or remissions of inflammation in the colonic mucosa. Genetic predispositions together with immunological and environmental factors are currently regarded as contributing to its multifactorial aetiology. There have been a few reports on UC using array analyses in which a small number of patients were examined, with the aim of identifying susceptibility genes of ulcerative in comparison with healthy controls. Dieckgraefe et al.1 reported that approximately 70 of 6000 genes were upregulated in seven UC patients. In comparing five UC with three Crohn's disease patients, Uthoff et al.2 found that among the 600 genes examined, secreted apoptosis-related protein 1 (Sarp1) was upregulated in UC. Lawrance et al.3 identified 170 up- or down-regulated genes from approximately 7300 genes in 12 UC patients. Furthermore, Langmann et al.4 reported that among 55000 genes examined in four Crohn's disease and four UC patients, both pregnane X receptor (PXR) and multidrug resistance protein 1 (MDR1) were downregulated.

Microarray, which was used in most of the above studies, has gained greater popularity and the number of screened genes has increased greatly as the completion of the Human Genome Project. The crux of a representative oligonucleotide microarray such as the GeneChip is the extremely high density of probe pairs that allows genes on a genome-wide scale to be assayed on a single chip. However, one of its shortcomings is that it is impossible to make direct comparisons with microarray data from other researchers or microarray platforms, because of the extremely low reproducibility of the genes.

At the other end of the methods spectrum is cDNA macroarray, which appears to be easy to perform, reliable, cost-effective and versatile. The strong hybridization force produced by longer and larger amounts of probes allows stringent conditions of hybridization and washing, thereby resulting in lower noise and higher reproducibility. Stronger signals are generated and higher sensitivity is achieved, even for low abundance transcripts. Despite the inability to perform cross-platform comparisons, such as between micro- and macro-arrays, low-technology macroarray appears to have the advantage over high-technology microarray as far as minimizing inter- and intra-individual variations is concerned.

To the best of our knowledge, there are no reports that simply compare gene expressions in inflamed and non-inflamed mucosa in UC patients. The aim of this study was to clarify the relationship between changes in gene expressions and the severity of inflammation as observed in the clinical setting of UC. In order to achieve this and considering the above methodological reasons, we chose the macroarray method and used whole endoscopic biopsy without any fabrication of array samples. In this manner, near in vivo conditions were obtained.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Patients and tissues

Approval to conduct the study was obtained from the Institutional Review Board and Ethics Committee of Sapporo Medical University Hospital. All the patients gave their informed consent in writing. Diagnosis of UC was based on classic clinical features as well as radiological, endoscopic, laboratory findings and clinical symptoms.5 Endoscopically observed inflammation was graded 0–4 according to the published grading score;6 non-inflamed and inflamed mucosa was defined as grade 0 or 1 and grade 3 or 4, respectively. Following Riley et al's histological criteria,7 non-inflamed and inflamed mucosa was defined as grade 0 or 1 and grade 3 or 4 respectively. In this study, non-inflamed mucosa was defined as remitting mucosa that was involved within the extent of the disease. The samples were considered as eligible when endoscopic and histological grading agreed, otherwise they were excluded. Two or three biopsy specimens were taken from both inflamed and non-inflamed mucosa in the UC patients during colonoscopy.

A total of 32 samples from 16 each of inflamed and non-inflamed mucosa for cDNA array analysis were taken from 23 UC patients for ‘individual analysis’. Eighteen of the 32 samples consisted of nine pairs of inflamed and non-inflamed mucosal specimens. Each pair was taken from the same patient and used for ‘pairwise analysis’. Samples in the individual analysis were matched for patients’ age (within 5 years), gender, site from which biopsies was taken (right-sided, left-sided colon and rectum) and medical treatment undergone (use of 5-aminosalicylate, prednisolone and immunosuppressants). In the pairwise analysis, samples were matched for only biopsy site. Samples for quantitative real-time polymerase chain reaction (PCR) analysis were taken from another 20 UC patients and five healthy subjects, which comprised 10 inflamed, 10 non-inflamed and five normal colonic biopsies. The average age was 34.1, 33.5 and 46.8 -yearold in inflamed UC patients, non-inflamed UC patients and healthy subjects, respectively.

Biopsy specimens were immediately stabilized in RNA Later (Qiagen, Hilden, Germany) and kept at −80 °C until isolation of the RNA.

RNA isolation

Total RNA was isolated with Trizol reagent (Invitrogen, Carlsbad, CA, USA) as directed by the manufacturers. About 10–20 μg total RNA was extracted from two or three biopsy specimens (about 15–20 mg). RNA purity was assessed by UV spectrophotometer UV-2400PC (Shimadzu, Kyoto, Japan) and its integrity was confirmed by denaturing agarose gel electrophoresis. The samples were digested with RNase-free DNase 1 (Sigma, St Louis, MO, USA) to remove any traces of genomic DNA contamination.

cDNA macroarray analysis

We adopted the single-channel and spot-form of cDNA macroarray, Gene Navigator cDNA Array (Toyobo, Osaka, Japan). Gene expression profiles were determined using Human Cancer Filter (Toyobo), and the customized filter for which we independently chose approximately 750 genes cloned with PCR by our collaborators; Takara Bio (Otsu, Japan), GeneticLab (Sapporo, Japan) and ourselves. The Human Cancer Filter covered 550 annotated human genes (EPK-202; http://www.toyobo.co.jp/seihin/xr/product/genenavi2/genename2.html). Each purified single-strand cDNA (ss-cDNA) probe (about 500 base length and 10 ng weight) was spotted onto the cation-charged nylon filters. All genes were spotted in duplicate. On the Human Cancer Filter, there were 11 housekeeping genes and two negative controls (luciferase and pUC). On the customized filter, there were two housekeeping genes and one negative control (Takara 1000 base lambda fragment).

The reverse transcription (RT)–PCR, poly(A)+ RNA purification and biotin labelling of cDNA were performed by using GENE NAVIGATOR cDNA AMPLIFICATION SYSTEM ver. 2 (Toyobo) according to the manufacturers’ protocol. Briefly, 1 μg of total RNA was reverse transcribed into ss-cDNA with anchored oligo-dT primers, dNTPs and ReverTra Ace (reverse transcriptase). The Ss-cDNA was purified by ethanol precipitation to remove excess dNTPs and dA tailed cDNA template was synthesized with terminal deoxynucleotidyl transferase and dATPs. Biotin-labelled cDNA was amplified with cDNA template, dNTPs (biotin-16-dUTP was included), anchored oligo-dT primer, anchor-sequence primer and KOD dash (DNA polymerase) by using PCR.

Hybridization was performed using Imaging High -Chemilumi- and PerfectHyb Hybridization Solution (Toyobo) according to the manufacturers’ protocol. Briefly, the biotin-labelled cDNA was denatured by incubation at 100 °C for 5 min and cooled on ice. Hybridization was performed in a columnar glass bottle (Thermo, Waltham, MA, USA) at 68 °C overnight in an Hybridization Incubator, Model 400 (SciGene, Sunnyvale, CA, USA) with gentle rotation. The hybridized filter was washed with 20 mL 2 × saline-sodium citrate (SCC) + 0.1% Sodium dodecyl sulphate (SDS) three times and with 20 mL 0.1 × SCC + 0.1% SDS another three times for 5/,min each wash at 68 °C. Moreover, biotin-labelled cDNA was bonded with both streptavidin and biotinylated alkaline phosphatase. We could detect signals using CDP-Star which was decomposed by biotinylated alkaline phosphatase and emitted chemiluminescence. Chemiluminescence signals were detected with a Fluor-S MultiImager and QuantityOne (Bio-Rad, Hercules, CA, USA). Signals were converted to numerical data by imagene ver. 4.2 (BioDiscovery, Segundo, CA, USA).

Data analysis for cDNA array

Signal intensity values were excluded in this analysis when they were less than the maximum signal values of negative controls. Next, those of the duplicated spots within an array were averaged and normalized according to the median signal intensity for a given array. In the individual analysis, gene expression profiles of inflamed and non-inflamed mucosa were compared using two kinds of statistics, unpaired t-test and significance analysis of microarray (SAM; http://www-stat.stanford.edu/~tibs/SAM/)8 whereas in the pairwise analysis, a paired t-test and SAM were used.

Unpaired and paired t-test were two-tailed and P < 0.001 was considered as statistically significant. Moreover, a more than threefold difference was adopted as the threshold for differential expression, because all signal scattering which originated from the same samples was less than threefold different by duplicated analysis. SAM computes a two-sample t-statistic for the normalized log-intensity of gene expression levels for each gene. Data were permuted using BRB-ARRAY TOOL ver. 3.2.1 (http://linus.nci.nih.gov/BRB-ArrayTools.html) with the following parameters: two-class, unpaired (individual analysis) or paired (pairwise analysis), 100 permutations and 10% target false discovery rate (the percentage of genes identified by chance alone) as significance threshold. The output criteria for SAM included more than threefold or less than a threefold difference.

Employing these four statistical methods, the genes were designated as an inflammatory gene signature when they were significant in at least two statistical methods resulting from both individual and pairwise analysis. Moreover, genes which were significant in at least two statistical methods in either analysis were also considered as candidates for inflammatory gene signature.

Quantitative real-time RT–PCR

About 1 mg of isolated RNA was reverse transcribed into cDNA with oligo-dT primers using the Thermoscript RT-PCR System (Invitrogen), according to the manufacturers’ protocol. Each reaction was duplicated to minimize the experimental variations (standard deviation was calculated for each reaction). The reagents and software used following synthesis of cDNA were all from Applied Biosystems (Foster City, CA, USA) except for primer synthesis. TaqMan GAPDH control reagent kit was used as an internal standard for both the integrity and quantity of RNA isolated. The primer sequences used were designed with PRIMER EXPRESS Software, and were synthesized by Sigma (Table 1). Real-time PCR was performed in a GeneAmp 5700 Sequence Detection System SDS (Applied Biosystems) using SYBR Green I PCR Master Mix for 40 cycles of two-step PCR amplification (95 °C for 15 s and 60 °C for 1 min). Data were analysed using the amplification plot method, the so- called ΔΔ CT method, involving calculation of the amplification efficacy by analysing the change of fluorescence intensity throughout the linear phase for every sample.9 Melting curves were depicted using Dissociation Curves Software to ensure only a single product was amplified. All data were expressed as the mean ± SE. P- values of < 0.05 were considered as statistically significant in the two-tailed unpaired t-test.

Table 1.  The list of forward and reverse primer sequences for real-time PCR
GeneForward primer (5′–3′)Reverse primer (5′–3′)
  1. MRP14, migration inhibitory factor-related protein 14; GROγ, growth-related oncogene γ; TIMP1, tissue inhibitor of metalloproteinase 1, SAA1; Serum amyloid A1; L-FABP; liver fatty acid-binding protein; PCR, polymerase chain reaction.

MRP14AGTTCATCATGCTGATGGCGGCATCTTCTCGTGGGAGGC
GROγTGTGAATGTAAGGTCCCCCGGCTATGACTTCGGTTTGGGC
TIMP1CGCAGCGAGGAGTTTCTCATGTGCAAGAGTCCATCCTGCA
ElafinCAGCTGTCACGGGAGTTCCTCACGGCCTTTGACAGTGTCTT
SAA1CTGCAGAAGTGATCAGCGATTGTGTACCCTCTCCCC
L-FABPAAGTTCACCATCACCGCTGGCACCGTGAATTCGTTTTGGAT

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

cDNA macroarray

The genes which were significantly upregulated in inflamed mucosa compared with those of non-inflamed mucosa are listed in Table 2, while the genes which were significantly upregulated in non-inflamed mucosa are shown in Table 3. Migration inhibitory factor-related protein 14 (MRP14) and growth-related oncogene-gamma (GROγ) were significant in all four statistical analyses of both of the individual and pairwise studies. Tissue inhibitor of metalloproteinase 1 (TIMP-1), elafin and serum amyloid A1 (SAA1) were significant in three of the four statistical analyses, the exception being the unpaired t-test of the individual study. These five genes were all upregulated in inflamed mucosa. Liver fatty acid-binding protein (L-FABP) was the only gene which was upregulated in non-inflamed mucosa and was significant in the SAM analysis of both the individual and pairwise studies.

Table 2.  Genes upregulated in inflamed mucosa compared with non-inflamed mucosa
Gene nameAccession NumberIndividual analysisPairwise analysisFold change (active/inactive)
SAMUnpaired t-testSAMPaired t-test
  1. +, statistically significant in each statistic.

MRP14M26311++++4.3
GROγX53800++++3.7
TIMP1X03124+ ++4.3
ElafinL10343+ ++3.3
SAA1NM_000331+ ++3.0
inline image
LCN2NM_005564  ++4.7
DD96U21049  ++3.5
TFF1X52003  ++3.3
Table 3.  Genes upregulated in non-inflamed mucosa compared with inflamed mucosa
Gene nameAccession NumberIndividual analysisPairwise analysisFold change
SAMUnpaired t-testSAMPaired t-test
  1. +, statistically significant in each statistic.

L-FABPM10050+ + 0.3
inline image
APM2D45370  ++0.3
SELENBP1NM_003944  ++0.2

Three genes, lipocalin 2 (LCN2), DD96 and trefoil factor 1 (TFF1), upregulated in inflamed mucosa, and two genes, adipose -specific collagen-like factor (APM2) and selenium-binding protein 1 (SELNBP1), upregulated in non-inflamed mucosa, did not meet our criteria for inflammatory gene signature but had the potential to do so.

Quantitative PCR

The reproducibility of the six significant genes picked up from the cDNA macroarray analysis was confirmed by real-time PCR (Figure 1). All six genes showed a significant difference (P < 0.05) between inflamed and non-inflamed mucosa and between inflamed and normal mucosa, but these expressions were not significantly different between non-inflamed and normal mucosa.

image

Figure 1. The expression of the significant genes in inflamed, non-inflamed and healthy mucosa measured by quantitative polymerase chain reaction (PCR). The bar represents the mean  ± S.E. from independent measurements of active and quiescent ulcerative colitis (UC) patients (n = 10, each) and from healthy subjects (n = 5). Statistical significance (P < 0.05) was assessed by two-tailed unpaired t-test. All six genes showed statistically significant differences between inflamed and non-inflamed or healthy control mucosa (*).

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Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The major limitation of our findings is that the upregulated genes are not necessarily UC-specific, but may be features of inflammation in the colon, regardless of cause. Our results suggest that the inhibition of acute neutrophilic inflammation should remain the mainstay of therapy against active UC because three (MRP14, GROγ and Elafin) of the six genes identified as having an inflammatory gene signature are implicated in neutrophil inflammation. Intriguingly, this is seemingly in contradiction to the fact that proinflammatory (MRP14 and GROγ) and anti-inflammatory genes (Elafin, TIMP1 and LCN2) were simultaneously upregulated in inflamed mucosa. However, the dynamic balance of these factors should be of pivotal importance for the pathophysiology of UC, not only in T cell-mediated gut injury, for which matrix metalloproteinases (MMPs) are considered to be a key effector but also in neutrophil-mediated gut injury, for which human neutrophil elastase (HNE) is considered a key effector.

In the excellent ex vivo model using foetal human intestine, MacDonald TT et al.10, 11 showed that overproduction of MMPs caused severe tissue injury whereas it was completely inhibited by treatment with synthetic MMP inhibitors. On the contrary, HNE has been proposed as a marker12 and a therapeutic target for UC.13, 14 Moreover, HNE by cleaving key receptors such as CD14 may be responsible for the impaired apoptotic cell recognition by macrophages, thereby prolonging inflammation.15 Taken together, augmentation of these inhibitor activities seems intuitively promising as a novel targeting therapy against active UC.

MRP14 forms heterodimers with MRP8 as calprotectin, which is a major component of neutrophil cytoplasmic proteins. Although the primary role of MRP14 still remains to be clarified, it may play a role in leucocyte migration.16GROγ is one of the C-X-C chemokines, which is secreted from macrophages and induces the migration of neutrophils to the inflamed site. Yang et al.17 reported that GRO family members were upregulated in UC, reinforcing the validity of our results. SAA1 is well-known as an acute reactive protein primarily derived from the liver. The function of SAA1 produced locally in various epithelia including intestine18, 19 remains to be elucidated. Measuring MRP14 as calprotectin in faeces and SAA in serum has already been applied in practice to evaluate inflammation.20, 21

Elafin, which is produced by macrophages and epithelial cells, is one of the specific inhibitors of HNE, a key effector of neutrophil-mediated tissue injury. Elafin is believed to have antimicrobial properties, such as defensin, which are implicated in host defence through innate immunity and are involved in the maintenance of epithelial integrity.22 Gene therapy using Elafin against multidrug-resistant Pseudomonas aeruginosa in intractable airway infections was recently reported.23TIMP1 is one of the intrinsic inhibitors of MMPs, a key effector of T cell-mediated tissue injury.24–26

The transport of fatty acids from the plasma membrane to cellular organelles is believed to be performed by the FABP family. As FABP is consumed during inflammation of its producing organs,27, 28 such as liver, heart and intestinal epithelium, L-FABP in inflamed UC mucosa may decrease by a similar mechanism. The result of quantitative PCR suggested that L-FABP was downregulated in inflamed mucosa rather than upregulated in non-inflamed mucosa. In 1980, Roediger29 hypothesized that UC was an expression of an energydeficiency disease of the colonic mucosa. Our results suggest that this classic but insightful notion was correct.

We considered LCN2, DD96, TFF1 and SELENBP1 as possible candidates for inflammatory gene signature of UC. Measurement of LCN, which is an enzyme in specific granules of neutrophils,30 in serum and faces has already been reported as a method of evaluating the extent of inflammation.31, 32 Lawrance et al.3 suggested that DD96 was implicated in carcinogenesis of UC.33, 34 TFF1 is involved in mucosal defence, remodelling35–38 and tumour-suppressor function.39 Chronic depletion of SELENBP1 during inflammation could attenuate the tumour-suppression function of selenium which inhibits the covalent bonding of carcinogens to DNAs.40

In conclusion, as cDNA macroarray analysis of the inflammatory gene signature can precisely reflect the whole profile of gene expression in the clinical setting of UC, the genes identified in this study will be directly applicable to the evaluation of the severity of inflammation and as novel therapeutic targets in active UC.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Authors thank Takara Bio (Otsu, Japan) and GeneticLab (Sapporo, Japan) for gene spotting onto a customized array filter. Both t-test and SAM in our analyses were performed using BRB-ARRAY TOOLS v3.2.1 developed by Dr Richard Simon and Amy Peng Lam. This study was supported by a grant-in-aid from the Ministry of Health, Labour and Welfare (K.I.), in Japan.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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