Human Molecular Genetics, 2001, Vol. 10, No. 5 445-456
© 2001 Oxford University Press
Ulcerative colitis and Crohn’s disease: distinctive gene expression profiles and novel susceptibility candidate genes
1Department of Medicine and 2Department of Genetics, Case Western Reserve University School of Medicine, University Hospitals of Cleveland, Cleveland, OH, USA
Received 29 November 2000; Revised and Accepted 8 January 2001.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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To elucidate the biological dysregulation underlying two forms of inflammatory bowel disease (IBD), ulcerative colitis (UC) and Crohn’s disease (CD), we examined global gene expression profiles of inflamed colonic tissue using DNA microarrays. Our results identified several genes with altered expression not previously linked to IBD. In addition to the expected upregulation of various cytokine and chemokine genes, novel immune function-related genes such as IGHG3, IGLL2 and CD74, inflammation-related lipocalins HNL and NGAL, and proliferation-related GRO genes were over-expressed in UC. Certain cancer-related genes such as DD96, DRAL and MXI1 were differentially expressed only in UC. Other genes over-expressed in both UC and CD included the REG gene family and the calcium-binding S100 protein genes S100A9 and S100P. The natural antimicrobial defensin DEFA5 and DEFA6 genes were particularly over-expressed in CD. Overall, significant differences in the expression profiles of 170 genes identified UC and CD as distinct molecular entities. The genomic map locations of the dysregulated genes may identify novel candidates for UC and CD genetic susceptibility.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Ulcerative colitis (UC) and Crohn’s disease (CD), two common inflammatory bowel diseases (IBDs) with shared clinical and demographic characteristics, harbor key differences in tissue damage and prognosis that suggest distinctive etiopathogenic processes. In UC, inflammation with crypt abscess formation is limited to the mucosa, while in CD, transmural granulomatous inflammation leads to fibrostenotic lesions and fistula formation (1). Both are complex clinical entities in which genetic, environmental and microbial factors interact to determine the susceptibility response of immune and non-immune cellular systems mediating inflammation (2,3). Mapping studies suggest a strong inherited component but a large number of putative susceptibility loci have complicated the identification of IBD genes. Confirmed IBD susceptibility regions include 16p12–q13 (IBD1), 12p13.2–q24.1 (IBD2), the major histocompatability complex region on chromosome 6 (IBD3) and 14q11–12 (IBD4) (4–9). Regions awaiting confirmation have been identified on 1p36, 3p21.2, 3q, 4q, 5q, 7q, 14q11–12 and 19p13 (5,10,11).
So far, immunoglobulins, eicosanoids, selected cytokines and immune activation gene products have been primarily studied with no definitive findings (12–14). As a complementary approach to genome-wide searches for IBD genes and to narrow down candidate gene searches, we investigated global gene expression patterns in UC, CD and normal control colonic tissue using DNA microarrays. The results show that the expression profiles of UC and CD are quite different and establish each form of IBD as a distinct molecular entity.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Microarray reproducibility
To assess array- and hybridization-based experimental variability, we initially performed hybridization on duplicate microarrays using the same pool of target RNA (Fig. 1, UC set 1 in experiments 1 and 2). Using all 7306 genes and expressed sequence tags (ESTs) as data points, a linear regression analysis was performed on the average fluorescence intensity difference per gene obtained in experiments 1 and 2. The two experiments were highly reproducible: 99.8% of the genes showed <3-fold difference in response, with a correlation coefficient of 0.97 between the two experiments. Only 12 out of 7306 genes and ESTs showed a >3-fold difference, all due to experimental error or chance. Therefore, for comparative profiling of UC versus control and CD versus control, we defined a 3-fold change as the significance threshold to claim differential expression (Fig. 1).
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RT–PCR validation of selected transcripts
A pool of RNA from colonic tissue samples of six UC, six CD and six control subjects was used for gene expression profiling. All CD and UC samples displayed a similar degree of histological inflammation and derived from patients with clinically comparable degrees of disease activity.
Prior to sample pooling, patient-to-patient variability in selected transcript levels (MMP1, COL1A1, COL3A1) was explored by semi-quantitative RT–PCR. The results of control, UC and CD (n = 10 in each group) for COL1A1, COL3A1, MMP-1, MMP-3, MMP-12, TIMP-1, elafin and SPARC transcripts are shown in Figure 2. Although some expected variability among individuals was observed, transcript levels for the genes tested were generally similar. In UC, transcripts for all the genes tested were significantly higher than the control (P < 0.05), in agreement with the results of the subsequent microarrays. For TIMP-1, although the RT–PCR results showed elevated transcripts, the UC profile did not detect them because the oligonucleotide probes initially used in the arrays were primarily of intronic origin (GeneChip Expression Analysis Sequence Information Database). RT–PCR of CD RNA showed elevated expression of COL1A1 and MMP-1 but no change in SPARC, MMP-3 or COL3A1 expression, also in agreement with the microarray results.
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Variability in gene expression profiles of two independent sets of UC RNA samples
To identify changes in gene expression patterns in IBD compared with control tissue we hybridized biotinylated cRNA prepared from pooled poly(A)+ mRNA to high density oligonucleotide microarrays (HuGene Fl arrays 900160 and 900183). The absolute analysis (GeneChip Expression Analysis software, Affymetrix) yielded an average fluorescence difference for each gene and a call of absent, present or marginally present for each transcript level of the target sample (UC, CD and control). The comparison analysis was performed to determine the relative change in abundance for each transcript between a baseline (control) and an experimental sample (UC or CD) (the complete data may be viewed at http://www.jhmi.edu/~schakrav). To estimate variability between independent experiments, two different UC RNA sets (UC 1 and UC 2, six patients comprising each) were profiled. The same control RNA (also a pool of six individual samples) was profiled each time to generate baseline control 1 and 2 for the UC 1 and UC 2 experiments, respectively. Compared with control set 1, 140 genes were expressed differentially at the >3-fold level in UC 1. Figure 3 shows that expression of these 140 transcripts was similarly regulated in the second profiling with UC 2. Of the 10 most over-expressed transcripts in UC 1, seven were also the most over-expressed in UC 2, whereas 8 of the 10 most repressed genes in UC 1 were also repressed in UC 2. When the fold change in gene expression in UC 1/control 1 and UC 2/control 2 were compared by linear regression analysis, a positive correlation (r = 0.65) was detected, indicating a consistent pattern in gene expression in the two independent UC groups compared with the control group.
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Next, we analyzed the gene expression pattern in UC 1, CD and control 1, comparing each IBD profile to the control 1 profile (Table 1 and Fig. 4). Numbers in the ‘Increase’ and ‘Decrease’ columns represent fold increase and decrease, respectively, in IBD gene expression over control. A total of 170 genes were differentially expressed in UC and CD (Table 1). A large number of them have not been previously associated with IBD, such as small inducible cytokine genes, SCYA2 and SCYA4, Annexin A5, metallothionein genes, MT1H, MT1G, S100A11, Elafin, SPARC, collagen genes, COL6A3, COL6A2 and von Willbrand factor gene, vWF, to name a few. We also found differential expression of genes for known inflammation-associated proteins such as HLA class II antigens, immunoglobulins, chemokine superfamily members, interleukin-1ß, serum amyloid A protein and phospholipase A2, providing biological validity to the results of the microarray analysis.
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Chromosomal locations of differentially expressed genes
Map locations of 165 of the 170 genes differentially expressed in IBD are shown in Table 1 indicating clustering of several of them within IBD susceptibility regions. Within IBD2 (12p13.2–q24.1) we found ATPase2B1 and CRADD (CASP2 and RIPK1 domain containing adapter with death domain) downregulated in CD and UC, respectively. Although IBD1 (16p12–q13) has been primarily associated with CD, among the genes probed by the arrays we did not find any that showed altered expression in CD. Because the arrays represent only 10–12% of all genes, it is possible that such putative CD markers are not included in the arrays yet. The CD profile also failed to detect any differentially expressed genes in chromosome 14q11–12 with a reportedly confirmed linkage to this condition (9,15). Another IBD susceptibility region, 19p13, contains several differentially expressed genes in UC and CD (DF or complement factor DM84526, MAT8 X93036, IF130 J03909, and PALM or paralemmin D87460) that are potential candidate genes. Interestingly, MDR1 (ATP binding cassette B1, M14758) was downregulated in both UC and CD profiles. MDR was recently reported to be associated with UC and CD (16). MDR1 on 7q21.1 is a candidate IBD gene (17) and mdr1a knockout mice develop spontaneous intestinal inflammation (18). Our observation supports the potential status of MDR1 as an IBD candidate gene and its under-expression as indicated by our results may be due to promoter mutations. Candidate genes clustered on 6p included the over-expressed HLA genes in UC and others such as UCA1B (guanylate cyclase activator, M97496) downregulated in both UC and CD.
Gene expression profiles of UC and CD
The numeric distribution of all differentially expressed genes in UC and CD highlight shared and unique features in both diseases (Fig. 4A). An equal number of genes were over-expressed or repressed in each IBD subtype, implying that broad up- or downregulation of genes per se does not define these diseases. Of note, not a single gene was upregulated in one and downregulated in the other form of IBD. Furthermore, 20% of differentially regulated genes were common to both forms of IBD, probably reflecting common events secondary to inflammation. Nevertheless, 108 and 29 differentially expressed genes unique to each IBD subtype spell distinctive disease signatures for UC and CD that underscore fundamental differences in their pathogenesis.
We assigned the differentially expressed genes to eight functional clusters (Fig. 4B and Table 1) and identified key differences in gene expression in UC and CD. We noted significant over-expression of HLA II and immunoglobulin genes in UC (cluster I). Over-representation of chemokines, cytokines and growth factors (cluster II) and other inflammatory mediators (cluster III) in UC represented another difference between UC and CD. Over-expressed lipocalins (HNL, S75256, NGAL or LCN2, X99133), nitric oxide synthase (NOS2, X85781), superoxide dismutase (SOD2, X65965), phospholipase A2 (PLA2, M22430), serum amyloid A protein (SAA, X51441) and lysozyme (LSZA, M21119) reflect an acute destructive inflammatory component in UC. Pro-inflammatory and mitogenic GRO genes (X54489, M57731 and X53800 cluster II) previously implicated in melanomas were over-expressed in UC (19).
The cancer-related gene cluster (cluster IV) defined a clinically and biologically important difference between UC and CD. Consistent with the known tendency to neoplastic transformation in UC, we found several cancer-related genes differentially regulated in UC, but not CD. For example, DD96 (U21049), expressed at low levels in normal epithelium, but over-expressed in lung, breast and colon carcinoma (20), was over-expressed in UC. MXI1, (L07648), a negative regulator of myc and a putative tumor suppressor gene (21), was downregulated in UC, as was DRA (L02785), whose absence is associated with proliferation and neoplastic transformation of the crypt epithelium (22).
Compared with CD, considerably more extracellular matrix (ECM) genes (cluster V, fibronectin 1, X02761, COL4A2, X05610, COL1A2, Z74616 and COL1A1, M55998) were over-expressed in UC (cluster V). vWF (M10321), COL6A3 and COL6A2, (X52022, X15882) were both upregulated in UC. ECM genes may be induced by the regulatory genes TGF ß(M77349) OSF-2 (D13666) SPARC (J03040), all of which were over-expressed in UC. Several matrix metalloproteinase (MMP) genes (MMP-1, –3, –9 and –12, X54925, X05232, J05070 and L23808) were also over-expressed in UC, possibly in response to induction by SPARC (23). Cluster VI includes a large number of metabolic enzymes and ion transport mediators that were, for the most part, under-expressed in UC (47 compared with 16 in CD).
An important distinction between UC and CD was the strong over-expression of DEFA5 and DEFA6, two inducible natural antimicrobial peptide genes in CD (cluster VII). The gene for defensin 5 was the single highest over-expressed gene in the CD profile, twice as high as the UC profile.
Finally, a number of cell-cycle regulators and transcription factors were also regulated differently in each form of IBD (cluster VIII). This cluster contained some of the most highly over-expressed genes in both forms of IBD. The REG1B and REG1A (regenerating islet-derived protein genes, L08010, J05412, cluster VIII) were strikingly over-expressed, particularly in UC, whereas PAP (pancreatitis-associated protein, L15533), another member of the REG family, was only moderately less upregulated in UC and CD.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Ulcerative colitis and CD are two related yet different forms of chronic intestinal inflammation resulting from the interaction of multiple gene products. Due to the complexity of both forms of IBD, investigation of relevant gene products has been focused primarily on inflammation and immune response genes (3). Such a narrow approach has intrinsic limitations and prevents reaching a solid grasp of all the genes that are potentially relevant to IBD pathogenesis. The objective of this investigation was to review the entire repertoire of available transcripts and elucidate all differentially regulated transcripts in representative colonic tissue using high-density oligonucleotide microarrays. With this innovative approach we hoped to identify expression patterns that would differentiate the two diseases, unravel novel aspects of UC and CD pathogenesis, identify genes not hitherto associated with IBD and eventually identify novel candidate susceptibility genes. We chose to use RNA from whole colonic tissue, which comprises heterogeneous cell types, with the specific purpose of gaining a global and representative insight into all cellular changes associated with IBD pathogenesis.
This highly innovative and comprehensive approach is not free of limitations. For instance, the pooling strategy could mask some degree of disease heterogeneity, but analysis of individual patient samples would have made this study economically prohibitive. In addition, several of the detected gene expression variations may reflect secondary rather than primary abnormalities, which, nevertheless, are still highly relevant to pathogenesis in life-long conditions such as IBD, where late events may be totally unrelated to early etiopathogenic events (3). Lending legitimacy to this approach and supporting the validity of our results is a recent study by Dieckgraefe et al. (24). Utilizing the same oligonucleotide arrays on pooled UC tissue these authors also found similar patterns of over-expression. Thus, 28% (21/74) of the over-expressed genes were also detected in our analysis. Most noticeably, the same genes, namely the REG family, MMPs, defensin 5 and S100 were the most over-expressed genes in both studies.
After ensuring the reproducibility and representativeness of the microarray methodology, the expression profiling of UC and CD revealed that of all the differentially expressed genes, only 19% (33/170) were shared by both diseases. These similarly regulated transcripts may be indicative of shared pathogenic events secondary to chronic inflammation. More notable was the observation that a large number of changes in gene expression were unique to each type of IBD. Thus, expression of 64% (108/170) and 17% (29/170) of genes was uniquely altered in UC and CD, respectively, demonstrating clear distinctions between these two diseases.
In addition to the known IBD susceptibility chromosomes, the majority of the differentially regulated genes tended to cluster on certain chromosomes, such as 4 and 17, with few differentially expressed genes on 15 and 18. Differentially expressed genes on chromosomes 4 and 17 may be indicative of other as yet unidentified IBD loci. Recently, expression profiling has been successfully used to complement conventional genetic approaches to identifying monogenic disease genes, such as Cd36 (FAT) responsible for insulin-resistance syndrome in the rat hypertension model (25). A similar approach could be most effective in identifying function-based associations in complex multigenic diseases such as IBD, since current genetic mapping identifies regions that are still large enough to span hundreds of genes. While the implicit assumption has been that in multigenic diseases there is one gene per genomic region identifiable by positional cloning, the hypothesis that multiple genes in each region contribute to disease risk is also valid. Our data show multiple differentially regulated genes within one location, providing support for this second hypothesis and clues to new candidate genes.
Due to the early nature of this study and the large number of over- and under-expressed genes in each cluster, it is presently impossible to properly interpret the global biological significance of all detected variations. Nonetheless, some limited and cautious speculations can be put forward based on current understanding of UC and CD pathophysiology.
Over-representation of several HLA II transcripts in UC implies gain rather than loss of immune function and an abnormal immune regulation in disease pathogenesis. The UC profile also emphasizes strong proliferative and regenerative responses, dedifferentiation and neoplastic propensity. A widespread downregulation of genes involved in protein, lipid, carbohydrate metabolism and various ion-transport is indicative of a major disruption in cellular homeostasis and energy utilization in UC. Furthermore, downregulation of the citric acid cycle and oxidative electron transport indicate anaerobic conditions favoring glycolysis and lactate accumulation. This broad repression of metabolic pathways revives an earlier hypothesis that energy deficiency of the colonic epithelium is a significant factor in UC pathogenesis (26).
The over-expression of antimicrobial defensins was dominant in the CD profile. Since defensins mediate innate mucosal defenses, such over-expression lends particularly strong support to the current theory of a pivotal role for the enteric flora in CD pathogenesis (27). Defensins are also T cell chemotactic and may help sustain recruitment of T cells in the CD mucosa (28).
Tissue damage is fundamentally different in UC and CD (1). Despite higher expression of ECM genes in UC, connective tissue deposition and stricture formation are features of CD. Our results suggest that this may be due to distinctive regulation of ECM and remodeling genes in each form of IBD. In UC, a rapid turnover of ECM proteins by the elevated MMPs could limit excessive ECM deposition, reducing the risk of fibrotic changes. In contrast, fibrostenotic events in CD may arise from limited ECM remodeling.
In summary, the results show that the coordinated activity of multiple immune, inflammatory, microbial and metabolic genes is profoundly altered in IBD. The identification of novel differentially expressed genes, such as REG, COL6A2 and COL6A3, vWf, MMP-12, elafin, MDR1, DF and PALM, points to as yet unexplored pathobiologies and IBD-predisposing candidate genes. Future gene expression profiling of endoscopic biopsy tissue during the clinical evolution of IBD will enable the compilation of biologically relevant co-regulated gene clusters (29), which could uncover the triggers of disease initiation and elucidate the dynamics of disease progression.
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MATERIALS AND METHODS |
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Tissue selection
Samples were derived from surgically resected colonic tissue from six UC set 1, six UC set 2, six CD and six control patients, each group consisting of three men and three women. The ages of UC 1 and UC 2 patients ranged from 32 to 74 years (average 49 years) and 28 to 72 years (average 51 years), respectively. The CD group ranged from 25 to 86 years (average 47 years). Classical clinical, radiologic and endoscopic criteria were used to confirm all diagnoses and all patients had comparable moderately severe clinical disease (30,31). A gastrointestinal pathologist with special expertise in IBD analyzed the tissues in a blinded and random fashion, and those displaying a moderately severe degree of inflammation were selected. To ensure comparability of the RNA samples special care was taken to select tissues with similar amounts and types of inflammatory infiltrates. Control subjects ranged from 58 to 70 years (average 65 years), and were operated for colon cancer (n = 4), diverticular disease (n = 1) and cecal polyp (n = 1). Tissues used for control RNA extraction were obtained from at least 10 cm away from the area of pathology and all were histologically normal. The project was approved by the Case Western Reserve University Institutional Review Board.
RNA extraction and preparation of target biotinylated cRNA
Fresh full-thickness colonic tissues were used. Total RNA was separately extracted from each sample using the guanidinium thiocyanate method (32) and then pooled into each group (UC, CD and control). The extracted RNA was used for RT–PCR or to prepare biotinylated cRNA for hybridization to microarrays.
Poly(A)+ RNA purification, biotin labeling of cRNA and hybridization were performed according to manufacturers’ protocols. Double-stranded cDNA (ds-cDNA) was synthesized from 1 µg of poly(A)+ RNA using the Superscript Choice system (Gibco BRL) and an HPLC-purified T7 RNA polymerase promoter containing an oligo(dT) tail (Genset). The ds-cDNA was extracted by phenol/chloroform/isoamyl alcohol and recovered by ethanol precipitation. In vitro transcription was performed using 1.0 µg of ds-cDNA template and a T7 Megascript Kit (Ambion) in the presence of biotin-labeled CTP, UTP (bio-11-CTP and bio-16-UTP from Enzo Diagnostics) and unlabeled ATP, CTP, GTP and UTP. A RNeasy affinity column (Qiagen) was used to purify biotin-labeled cRNA.
Hybridization of cRNA to oligonucleotide microarrays
We initially used Affymetrix HuGene FL set 900160 containing 7070 genes/ESTs distributed over subarrays A, B, C and D. This array set contained all GenBank genes and ESTs deposited in the database prior to 1997 (http://www.affymetrix.com/). The UC 2 and control target RNA were profiled using the HuGene FL array 900183, with all genes/ESTs on one array. Since two array types were used, the control RNA was hybridized to each array type to generate a control 1 profile for comparison with UC 1 and CD and a control 2 profile for comparison with UC 2.
The biotinylated cRNA was fragmented randomly to 35–200 bases by incubation at 94°C for 35 min. At a final concentration of 0.05 µg/µl, fragmented cRNA was added to hybridization buffer (100 mM MES, 1 M Na+, 20 mM EDTA and 0.01% Tween-20) containing 50 pM control oligonucleotide B2, control cRNA cocktail, 0.1 mg/ml herring sperm DNA and 0.5 mg/ml acetylated BSA. Target samples were hybridized to the arrays at 45°C overnight in a GeneChip hybridization oven 640 (Affymetrix) set to 60 revolutions per minute, washed and stained with streptavidin–phycoerythrin and read at a resolution of 6 µm with a Hewlett-Packard GeneArray Scanner.
Microarray data analysis
The Affymetrix absolute analysis algorithm (v3.1) was used to analyze scanned images. Detailed protocols were provided by the manufacturer and have been described previously (33). The scanned images were visually inspected using criteria provided by the manufacturer to assess uniform hybridization. The ‘noise’ level (500–600 fluorescence intensity units) per chip was within the acceptable range of the manufacturer’s protocol. We employed global scaling using all probes to set the average intensity to an arbitrary target intensity of 150 as recommended by the software. Each hybridization was first analyzed using the Absolute Analysis Software and then by Comparative Analysis to compare UC and CD with controls, respectively, as baseline. The absolute analysis yielded the average fluorescence difference and an absolute call of absent or present for each transcript. In the comparative analysis, transcripts were considered as significantly altered over control when the ratio of average fluorescence difference of experimental to control was 
3-fold and the average fluorescence difference change was greater than 100 arbitrary units. The latter criteria minimized selection of genes that showed large fold changes over a control involving weak absolute responses (low average fluorescence differences). The complete gene expression profile data may be viewed at http://www.jhmi.edu/~schakrav. The LocusLink (http://www.ncbi.nlm.nih.gov/LocusLink/) database and its links to Unigene, OMIM, GenBank and PubMed databases were used to identify all known human structural homologs, functions and chromosome location. Information on genes contributing to common biochemical metabolic pathways was obtained from Biochemical Pathways (34).
RT–PCR analysis
Total RNA (1 µg) was reverse-transcribed to synthesize cDNA. ß-actin was used as an internal control. The amount of cDNA, as judged by the intensity of a control ß-actin signal, was comparable in all samples. Multiple reactions with varying numbers of PCR cycles were run for each transcript and one within the linear range of band intensity of ethidium bromide-stained agarose gels was chosen for each transcript. Primers used to amplify specific gene products were as follows: COL1A1 (forward, 5'-GGC GGC CAG GGC TCC GAC CC-3', reverse, 5'-AAT TCC TGG TCT GGG GCA CC-3'), COL3A1 (forward, 5'-CCC AGA ACA TCA CAT ATC AC-3', reverse, 5'-CAA GAG GAA CAC ATA TGG AG-3'), MMP-1 (forward, 5'-GGT GAT GAA GCA GCC CAG-3', reverse, 5'-CAG TAG AAT GGG AGA GTC-3'), MMP-3 (forward, 5'-GTT AGG AGA AAG GAC AGT GGT CCT G-3', reverse, 5'-GGC ATA GGC ATG GGC CAA AAC ATT-3'), MMP-12 (forward, 5'-TCA CGA GAT TGG CCA TTC CTT-3', reverse, 5'-TCT GGC TTC AAT TTC ATA AGC-3'), elafin (forward, 5'-GCA GCT TCT TGA TCG TGG TG-3', reverse, GCC GTG GGC ATC CTG AAT GGG-3'), TIMP1 (forward, 5'-AGT CAA CCA GAC CAC CTT ATA CCA-3', reverse, 5'-TTT CAG AGC CTT GGA GGA GCT GGT-3') and SPARC (forward, 5'-TGA GAA TGA GAA GCG CCT GGA-3', reverse, 5'-TTG GGG GAA ACA CGA AGG GGA-3'). PCR conditions comprised a hot start at 94°C for 5 min, followed by the sequence of 94°C for 30 s, annealing at 54–56°C for 60 s and extension at 72°C for 90 s. The products were run on a 1% TAE agarose gel with 0.25 µg/ml ethidium bromide and quantified by densitometric scanning on a Bio-Rad Gel Doc 1000. Statistically significant differences between control and IBD samples were determined using the Mann–Whitney non-parametric t-test and a P value of <0.05.
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ACKNOWLEDGEMENTS |
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We gratefully acknowledge Drs Huntington Willard, Richard W. Hanson and Steve Brant for reviewing the manuscript. We thank Drs Aravinda Chakravarti for advice in data analysis and critical comments and Joseph Willis for the histological evaluation of the surgical specimens. This study was supported by funds provided to S.C. by the Gastroenterology Division (Department of Medicine) and Department of Genetics of the Case Western Reserve University School of Medicine, and the Crohn’s and Colitis Foundation of America. Results of this study were presented at the Nature Genetics Conference (1999), the Microarray meeting, Scottsdale, AZ.
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FOOTNOTES |
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+ To whom correspondence should be addressed at: Department of Medicine, Johns Hopkins University School of Medicine, Ross 954, 720 Rutland Avenue, Baltimore, MD 21205, USA. Tel+1 410 502 7627; Fax: +1 410 614 4834; Email: schakrav@jhmi.edu
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