Journal of Theoretical Biology 354 (2014) 48–53
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Journal of Theoretical Biology journal homepage: www.elsevier.com/locate/yjtbi
TIBS: A web database to browse gene expression in irritable bowel syndrome Jing Yan a, Yan Xu b, Brian Hu c, Sammy Alnajm d, Lina Liu a, Yin Lu e, Zhiguang Sun a,n, Feng Cheng b,f,nn a
Institute of First Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing 210046, Jiangsu, People Republic of China Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa 33612, USA c Essenology Software Inc., Wuxi 214187, People Republic of China d Department of Biology, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne 50674, Germany e Department of Pharmacology, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, People Republic of China f Department of Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa 33612, USA b
H I G H L I G H T S
A web database to browse gene expression in irritable bowel syndrome patients. Spatial difference in gene expression across four different tissues. Gene expression in IBS patients compared to healthy volunteers or UC patients. Sex difference in gene expression in IBS patients.
art ic l e i nf o
a b s t r a c t
Article history: Received 18 January 2014 Received in revised form 19 February 2014 Accepted 14 March 2014 Available online 24 March 2014
Irritable bowel syndrome (IBS) is a chronic functional gastrointestinal disorder. Its symptoms include chronic abdominal pain, bloating gas, diarrhea and constipation. Many IBS patients also have psychological symptoms like depression or anxiety. These unpleasant symptoms significantly lower patients' quality of life. The prevalence of IBS in Europe and North America is about 10–15% of the population, which makes IBS a disorder with a high social cost. The pathophysiology of IBS is considered to be multifactorial and the exact cause of the disease remains poorly understood. Recently, a genome-wide expression microarray technique has been applied to investigate the possible mechanisms of IBS. However, a user-friendly database that allows scientists without bioinformatics background to query gene expression levels in these data sets and compare gene expression patterns across different tissues has not yet been established. Therefore, we have integrated four public expression microarray data (320 samples) from the Gene Expression Omnibus (GEO) and ArrayExpress databases into an online database called Transcriptome of Irritable Bowel Syndrome (TIBS). The gene expression change in IBS patients compared to healthy volunteers or UC patients in jejunum, sigmoid colon, rectum, and descending colon can be queried by gene symbols. Users can compare gene expression levels of IBS patients across these tissues. Sex difference of gene expression in IBS patients was also shown in the database. The current version of TIBS database contains 42,400 annotated gene probe sets represented on the Affymetrix Human Genome U133 plus 2.0 platform. TIBS will be an invaluable resource for a better understanding of the pathogenesis of IBS at the molecular level and for drug development. The TIBS database is available online at http://www.chengfeng.info/tibs_database.html. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Microarray Online server IBS Ulcerative colitis Bioinformatics
n Corresponding author at: Institute of First Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, 210046, Jiangsu, People Republic of China. Tel.: þ 86 25 8362 0504; fax: þ 86 25 8632 0532. nn Corresponding author at: Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa 33612, USA. Tel.: þ1 813 974 4288; fax: þ1 813 905 9890. E-mail addresses: [email protected] (J. Yan), [email protected] (Y. Xu), [email protected] (B. Hu), [email protected] (S. Alnajm), [email protected] (L. Liu), [email protected] (Y. Lu), [email protected] (Z. Sun), [email protected] (F. Cheng).
http://dx.doi.org/10.1016/j.jtbi.2014.03.026 0022-5193/& 2014 Elsevier Ltd. All rights reserved.
1. Introduction Irritable bowel syndrome (IBS) is a chronic functional gastrointestinal disorder. The symptoms of IBS include chronic abdominal pain, bloating gas, diarrhea and constipation (Anastasi et al., 2009). Based on the symptoms, IBS could be mainly classified into two subgroups: IBS with constipation (IBS-C) and IBS with diarrhea (IBS-D). Many IBS patients also have psychological
J. Yan et al. / Journal of Theoretical Biology 354 (2014) 48–53
Table 1 Four microarray data sets integrated in the TIBS database. Data set
Cell or tissue type
#IBS
#Non-IBS
IBS vs HV GSE14841 GSE36701
Jejunal biopsies Rectal biopsies
5 (IBS-D) 87 (34 IBS-C, 53 IBS-D) 76 (32 IBS-C, 44 IBS-D)
4 HV 40 HV
10
17 UC
10
17 UC
E-TABM-176 IBS vs UC GSE13367 GSE13367
Sigmoid colon mucosal biopsies Mucosal colonic biopsy from the descending colon Colonocytes from the descending colon
55 HV
HV: healthy volunteers; UC: ulcerative colitis; IBS-C: IBS with constipation; IBS-D: IBS with diarrhea.
symptoms like depression and anxiety (Lydiard, 2001). These symptoms significantly lower patients' quality of life. The prevalence of IBS in North America and Europe is around 10–15% of the population (Quigley et al., 2006; Saito et al., 2002). The disease affects significantly more women than men. It was estimated that 60–75% of IBS patients are women (Hulisz, 2004). The IBS is costly and the annual medical costs for IBS are over $20 billion in the United States (Hulisz, 2004). The high prevalence and medical expense make IBS a disorder with a high social cost. However, there is still no effective cure for IBS clinically because the pathophysiology of IBS is not well understood (Andresen and Camilleri, 2006). The only treatments that attempt to relieve symptoms such as dietary modification or psychotherapies are often chosen (Camilleri and Andresen, 2009). Additionally, some inflammatory bowel diseases such as ulcerative colitis (UC) have similar symptoms as IBS, which can make diagnosis of IBS difficult. Precise detection of gene expression changes in IBS patients is important to the understanding of the pathophysiology of IBS at molecular level (Andresen and Camilleri, 2006; Camilleri and Andresen, 2009). Recently, expression microarray has been applied to detect gene expression of IBS patients in different tissues such as the jejunum, the sigmoid colon, the rectum, and the descending colon. The expression microarray technique is a high-throughput genomic method that could detect the gene expression levels of thousands of genes simultaneously and investigate genome-wide transcriptional patterns (Cheng, 2012; Cheng et al., 2010, 2011). These projects generated valuable data for IBS research. However, a user-friendly database that allows scientists without bioinformatics background to query gene expression levels in these data sets and compare gene expression patterns across different tissues has not yet been established. The development of biological web database or server is a core area of bioinformatics (Cheng, 2013; Chou and Shen, 2008; Shen and Chou, 2008). Many studies have indicated that user-friendly web-servers can timely provide very useful information and insights for biological systems such as showing mRNA expression levels (Chintapalli et al., 2007; Wu et al., 2009; Xia et al., 2007), predicting the heat shock protein families (Feng et al., 2013), identifying nucleosomes (Xiao et al., 2013), identifying DNA recombination spots (Chen et al., 2013), protein subcellular location prediction (Chou et al., 2012), protein cleavage site prediction (Chou, 1996), and hence are widely welcome by the science community (Chou and Shen, 2009). The present study is attempted to establish a web database called Transcriptome of Irritable Bowel Syndrome (TIBS) for browsing gene expression in IBS in hopes that it can become a useful tool for drug development.
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The TIBS database was generated from 320 samples collected from four public expression microarray data sets, E-TABM-176 from the ArrayExpress database, GSE14841, GSE36701(Swan et al., 2013), and GSE13367 (Aerssens et al., 2008) from the NCBI GEO database. Gene expression in IBS patients compared to healthy volunteers or UC patients in jejunum, sigmoid colon, rectum and descending colon can be queried by gene symbols. Users can compare gene expression levels of IBS patients across these tissues. Sex difference in gene expression of IBS patients could also be shown in the database. The current version of TIBS contains 42,400 annotated gene probe sets represented on the Affymetrix Human Genome U133 Plus 2.0 platform. TIBS will be an invaluable tool and resource for scientists with little experience in bioinformatics to identify reliable genes for IBS pathophysiology studies or drug discovery. The TIBS database is available online at http://www. chengfeng.info/tibs_database.html.
2. Material and methods 2.1. Data sources 320 samples in four published microarray sets (shown in Table 1) from the GEO (http://www.ncbi.nlm.nih.gov/geo/) and the ArrayExpress (http://www.ebi.ac.uk/arrayexpress/) database were integrated in the TIBS. Samples in these four sets were profiled using the Affymetrix Human Genome U133 Plus 2.0 GeneChip Array containing 54,000 probe sets that assay expression of about 38,500 human gene transcripts. Three microarray data sets, GSE14841, GSE36701(Swan et al., 2013), and E-TABM-176, were chosen to compare gene expression from healthy volunteers and IBS patients. The first data set, GSE14841, included jejunal biopsy samples from four healthy people and five IBS-D patients. The second data set, GSE36701, evaluated the gene expression levels in rectal biopsies from 87 IBS patients (34 IBS-C and 53 IBS-D) and 40 healthy volunteers. The third data set, E-TABM-176 from the ArrayExpress database, contains transcription profiling of sigmoid colon mucosal biopsies from 76 IBS patients (32 IBS-C, 44 IBS-D) and 55 healthy volunteers. Microarray data set, GSE13367 (Aerssens et al., 2008), was chosen to investigate the gene expression of IBS patients compared to patients with ulcerative colitis. The data set contained mucosal colonic biopsies and isolated colonocytes from the descending colon in patients with active UC (n ¼8), quiescent UC (n ¼9), and with IBS (n ¼10). 2.2. Data normalization and analysis Raw Affymetrix CEL files of these four microarray data sets were downloaded from the GEO and the ArrayExpress database. The RMAExpress program (http://rmaexpress.bmbolstad.com/) was chosen to normalize all CEL files in these four data sets and to summarize expression of gene probe sets. Default settings including RMA background correction, quantile normalization, and log2-transformation were chosen. In the current version of TIBS database, 42,400 annotated gene probe sets with official gene symbols were included. A boxplot has been plotted to check whether the distributions of samples from different data sets are comparable. Comparisons of the mean expression levels between two groups and among three groups were carried out by the t test and anova functions, respectively, in the R program (http://www. r-project.org/). The gene expression levels were plotted using the barplot function in R.
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Fig. 1. The query interface of the TIBS database. The input to the database is official gene symbols. All probe sets representing the queried gene will be shown; each probe set is reported in one pdf file. DUOX2 gene was used as an example to query the TIBS database in this paper. The gene has one probe set in the Affymetrix Gene-Chip Human Genome U133 plus 2.0 platform, 219727_at.
3. Results
2.0 platform, 219727_at (shown in Fig. 1). The output results of this probe set are shown in Figs. 2 and 3.
3.1. Query interface of the TIBS database TIBS is a php-based online database powered by the iPage incorporation (http://www.ipage.com). Analyzed microarray data are stored in a mySQL relational database. As shown in Fig. 1, the input to the system is official gene symbols (for example, DUOX2 or PPARG), which could be looked up from the NCBI gene bank (http://www.ncbi.nlm.nih.gov/gene) or the GeneCards database (http://www.genecards.org). All probe sets representing the queried gene will be shown; each probe set is reported in one Acrobats pdf file. The pdf file includes two graphs. The first graph provides gene expression levels in IBS patients compared to healthy volunteers or UC patients in four different tissues including jejunum, sigmoid colon, rectum, and descending colon. The second graph shows sex difference in gene expression of IBS patients in rectum and descending colon. TIBS provides some user-friendly functions to query the database. Users can enter a list of multiple gene symbols separated by spaces for the batch searching (for example, “DUOX2 PPARG HSPA6”). Results of all genes in the list will be shown. Users can also enter a query string for the fuzzy searching (For example, “DUOX” or “PAR”). Any gene symbols containing the query string will be displayed. 3.2. Output of the TIBS database DUOX2 gene was used as an example to query the TIBS database in this paper. The gene encodes dual oxidase 2, an enzyme prominent in the gastrointestinal tract (El Hassani et al., 2005). Dual oxidase 2 contains both an NADPH oxidase domain and a heme peroxidase domain. The function of the enzyme is to regulate the production of reactive oxygen species, which plays an important role in host defense mechanisms (Rokutan et al., 2008). DUOX2 has been proved to be an IBS related gene (Aerssens et al., 2008). The gene has one probe set in the Affymetrix Gene-Chip Human Genome U133 plus
3.2.1. Spatial difference in gene expression across four tissues The first graph of the output pdf file (Fig. 2) compares the gene expression profiles of IBS patients with healthy volunteers or UC patients in jejunum, sigmoid colon, rectum, and descending colon. Users can compare gene expression levels of IBS patients across these tissues. As shown in Fig. 2, there is a spatial difference in gene expression levels among four tissues. DUOX2 shows low expression levels (signal intensity is lower than 4) in jejunal biopsies from both healthy volunteers and IBS-D patients. The expression levels of DUOX2 are higher in rectal biopsies, sigmoid colon mucosal biopsies and descending colons. This finding agrees with the conclusions from Mark T. Quinn that DUOX2 is highly expressed in distal gastrointestinal tract, such as sigmoidal colon and rectal glands (Quinn, 2013). 3.2.2. Gene expression in IBS patients compared to healthy volunteers or UC patients As shown in Fig. 2, DUOX2 shows significant difference in expression levels among healthy volunteers, IBS-C and IBS-D patients in sigmoid colon mucosal (P-value ¼0.00059) and in rectal biopsies (P-value ¼0.003674). The findings are consistent with the PCR results reported by Aerssens et al. (2008) that DUOX2 mRNA expression levels in the colon mucosal biopsies of IBS patients increased. DUOX2 shows higher expression levels in UC patients than in IBS patients in descending colons. The DUOX2 gene is also more upregulated in active UC than in quiescent UC patients. The expression patterns are consistent with the findings from Linpinski, et al. that colonic epithelial cells express high levels of DUOX2 under proinflammatory conditions (Lipinski et al., 2009). Although UC has very similar symptoms as IBS, the pathophysiology of these two diseases is quite different. UC is a type of inflammatory bowel disease while
J. Yan et al. / Journal of Theoretical Biology 354 (2014) 48–53
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Fig. 2. The first graph of the output pdf file from the TIBS database (the query gene: DUOX2). The graph shows gene expression levels in IBS patients compared to healthy volunteers or UC (including active UC (UC_inflamed) and quiescent UC (UC_noninflamed)) patients in jejunum, sigmoid colon, rectum, and descending colon. Comparisons of the mean expression levels between two groups and among three groups were carried out by the t.test and anova functions, respectively, in the R program. Barplots of the gene expression levels were plotted using the barplot function in R.
Fig. 3. The second graph of the output pdf file from the TIBS database (the query gene: DUOX2). The graph compared gene expression levels in male samples (blue) to female samples (pink) in rectum and descending colon. Comparisons of the mean expression levels between two groups and among three groups were carried out by the t.test and anova functions, respectively, in the R program. Barplots of the gene expression levels were plotted using the barplot function in R. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
IBS does not cause obvious inflammation. DUOX2 plays an important role in host defense mechanisms during inflammation, which can explain why the gene shows higher expression levels in UC patients than in IBS patients.
3.2.3. Sex difference in gene expression Sex difference was shown in IBS patients. IBS affects significantly more women than men. It was estimated that 60–75% of IBS patients are women (Hulisz, 2004). The second graph of the
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J. Yan et al. / Journal of Theoretical Biology 354 (2014) 48–53
Fig. 4. The second graph of the output pdf file from the TIBS database (the query gene: EIF1AY). The graph compared gene expression levels in male samples (blue) to female samples (pink) in rectum and descending colon. Comparisons of the mean expression levels between two groups and among three groups were carried out by the t.test and anova functions, respectively, in the R program. Barplots of the gene expression levels were plotted using the barplot function in R. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
output pdf file (Fig. 3) from the TIBS database could help researchers to identify sex difference in gene expression of IBS patients. Sex differences in DUOX2 expression levels of IBS patients are observed in rectal biopsies. As shown in Fig. 3, male IBS patients did not show a significant difference from healthy people (P-value ¼0.0898), however female IBS patients did (P-value ¼0.00525). A Y-chromosome gene, EIF1AY, was also chosen to validate the sex difference shown in the TIBS database. The EIF1AY gene encodes eukaryotic translation initiation factor 1A that stabilizes the binding of the initiator Met-tRNA to 40S ribosomal subunits in the initiation phase of eukaryotic translation. The gene has two probe sets in the Affymetrix Gene-Chip Human Genome U133 plus 2.0 platform, 204409_s_at and 204410_at. The output results of the first probe set are shown in Fig. 4. As shown in the figure, the gene EIF1AY shows much higher expression levels in male than in female samples, which is consistent with the knowledge that the Y chromosome is only present in males and that genes on the Y chromosome are expressed only in males. The results proved the reliability of data in the TIBS database.
genes in which tissue would be important for IBS development. The TIBS database provides useful information regarding the study of molecular mechanism of IBS and aid in the discovery of novel drugs. The difference of males and females shown in the database might provide the basis for understanding the molecular mechanisms underlying the sex differences in the incidence, prevalence and severity of IBS. User-friendly and publicly accessible web-servers represent the future direction in bioinformatics (Chou and Shen, 2009; Qiu et al., 2014) as demonstrated by recent publications (Chen et al., 2012, 2013; Feng et al., 2013; Qiu et al., 2014; Xu et al., 2013). We are still expanding our database by adding more public microarray data sets. All samples in our current database were profiled with Affymetrix Human Genome U133 Plus 2.0 GeneChip Array. Next stage we would like to include more IBS samples from different platforms such as Agilent microarrays (http://www.genomics.agilent.com/). In the next version we plan to expand to up to 500 samples. Analyzed results from the TIBS database will be more reliable. New functions such as gene network analysis will also be added in the future.
Acknowledgment 4. Conclusions and prospective The TIBS database is very important resource for IBS research based on two reasons. First, previous transcriptome studies of IBS have used relatively small numbers of samples and only focused on one tissue or some specific genes. TIBS is the first research database that combined several public microarray data and systematically analyzed the gene expression patterns in IBS patients at the transcriptome level. The collection we used in this study was more than 300 samples (four data sets) from several tissues. There was no such database in this field. Second, researchers can freely access the database to check the gene expression profiles of more than 40,000 gene probe sets. TIBS will help scientists with little experience in bioinformatics to identify which
This work was supported by Specialized Research Fund for the Doctoral Program of Higher Education (No. 20133237110008), Jiangsu College Graduate Research and Innovation Projects (No. CXZZ12_0618) and, in part, by the University of South Florida System Internal Awards Program under USF Women’s Health Collaborative Grant No. 0095058. References Aerssens, J, Camilleri, M, Talloen, W, Thielemans, L, Gohlmann, HW, Van Den Wyngaert, I, Thielemans, T, De Hoogt, R, Andrews, CN, Bharucha, AE, Carlson, PJ, Busciglio, I, Burton, DD, Smyrk, T, Urrutia, R, Coulie, B, 2008. Alterations in mucosal immunity identified in the colon of patients with irritable bowel
J. Yan et al. / Journal of Theoretical Biology 354 (2014) 48–53
syndrome. Clin. Gastroenterol. Hepatol. 6, 194–205, http://dx.doi.org/10.1016/j. cgh.2007.11.012. Anastasi, JK, McMahon, DJ, Kim, GH, 2009. Symptom management for irritable bowel syndrome: a pilot randomized controlled trial of acupuncture/moxibustion. Gastroenterol. Nurs. 32, 243–255, http://dx.doi.org/10.1097/SGA.0b013e3181b2c920. Andresen, V, Camilleri, M, 2006. Irritable bowel syndrome: recent and novel therapeutic approaches. Drugs 66, 1073–1088. Camilleri, M, Andresen, V, 2009. Current and novel therapeutic options for irritable bowel syndrome management. Dig. Liver Dis. 41, 854–862, http://dx.doi.org/ 10.1016/j.dld.2009.07.009. Chen, W, Feng, PM, Lin, H, Chou, KC, 2013. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res. 41, e68, http://dx.doi.org/10.1093/nar/gks1450. Chen, W, Lin, H, Feng, PM, Ding, C, Zuo, YC, Chou, KC, 2012. iNuc-PhysChem: a sequencebased predictor for identifying nucleosomes via physicochemical properties. PLoS One 7, e47843, http://dx.doi.org/10.1371/journal.pone.0047843. Cheng, F, 2012. Exploring the mysteries of traditional chinese medicine systematically by expression microarrays. Drug Dev. Res. 73, 499–503. Cheng, F, 2013. Bioinformatics Algorithms and Genomics The Open Bioinformatics Journal 7, 25–26. Cheng, F, Cho, SH, Lee, JK, 2010. Multi-gene expression-based statistical approaches to predicting patients' clinical outcomes and responses. Methods Mol. Biol. 620, 471–484, http://dx.doi.org/10.1007/978-1-60761-580-4_16. Cheng, F, Theodorescu, D, Schulman, IG, Lee, JK, 2011. in vitro transcriptomic prediction of hepatotoxicity for early drug discovery. J. Theor. Biol. 290, 27–36, http://dx.doi.org/10.1016/j.jtbi.2011.08.009. Chintapalli, VR, Wang, J, Dow, JA, 2007. Using FlyAtlas to identify better Drosophila melanogaster models of human disease. Nat. Genet. 39, 715–720, http://dx.doi. org/10.1038/ng2049. Chou, KC, 1996. Prediction of human immunodeficiency virus protease cleavage sites in proteins. Anal. Biochem. 233, 1–14, http://dx.doi.org/10.1006/abio.1996.0001. Chou, KC, Shen, HB, 2008. Cell-PLoc: a package of web servers for predicting subcellular localization of proteins in various organisms. Nat. Protoc. 3, 153–162, http://dx.doi.org/10.1038/nprot.2007.494. Chou, KC, Shen, HB, 2009. Review: recent advances in developing web-servers for predicting protein attributes. Nat. Sci. 2, 63–92. Chou, KC, Wu, ZC, Xiao, X, 2012. iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol. Biosyst. 8, 629–641, http://dx.doi.org/10.1039/c1mb05420a. El Hassani, RA, Benfares, N, Caillou, B, Talbot, M, Sabourin, JC, Belotte, V, Morand, S, Gnidehou, S, Agnandji, D, Ohayon, R, Kaniewski, J, Noel-Hudson, MS, Bidart, JM, Schlumberger, M, Virion, A, Dupuy, C, 2005. Dual oxidase2 is expressed all along the digestive tract. Am. J. Physiol. Gastrointest. Liver Physiol. 288, G933–G942, http://dx.doi.org/10.1152/ajpgi.00198.2004. Feng, PM, Chen, W, Lin, H, Chou, KC, 2013. iHSP-PseRAAAC: identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem. 442, 118–125, http://dx.doi.org/10.1016/j.ab.2013.05.024.
53
Hulisz, D, 2004. The burden of illness of irritable bowel syndrome: current challenges and hope for the future. J. Manag. Care Pharm. 10, 299–309. Lipinski, S, Till, A, Sina, C, Arlt, A, Grasberger, H, Schreiber, S, Rosenstiel, P, 2009. DUOX2-derived reactive oxygen species are effectors of NOD2-mediated antibacterial responses. J. Cell Sci. 122, 3522–3530, http://dx.doi.org/10.1242/ jcs.050690. Lydiard, RB, 2001. Irritable bowel syndrome, anxiety, and depression: what are the links? J. Clin. Psychiatry 62 (Suppl 8), 38–45 (discussion 46-7). Qiu, WR, Xiao, X, Chou, KC, 2014. iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int. J. Mol. Sci. 15, 1746–1766, http://dx.doi.org/10.3390/ijms15021746. Quigley, EM, Bytzer, P, Jones, R, Mearin, F, 2006. Irritable bowel syndrome: the burden and unmet needs in Europe. Dig. Liver Dis. 38, 717–723, http://dx.doi. org/10.1016/j.dld.2006.05.009. Quinn, MT, 2013. NADPH Oxidases: Structure and Function. Molecular Basis of Oxidative Stress: Chemistry, Mechanisms, and Disease Pathogenesis. John Wiley & Sons, Inc., Hoboken, New Jersey, pp. 137–178, http://dx.doi.org/ 10.1002/9781118355886.ch7. Rokutan, K, Kawahara, T, Kuwano, Y, Tominaga, K, Nishida, K, Teshima-Kondo, S, 2008. Nox enzymes and oxidative stress in the immunopathology of the gastrointestinal tract. Semin. Immunopathol. 30, 315–327, http://dx.doi.org/ 10.1007/s00281-008-0124-5. Saito, YA, Schoenfeld, P, Locke 3rd, GR, 2002. The epidemiology of irritable bowel syndrome in North America: a systematic review. Am. J. Gastroenterol. 97, 1910–1915, http://dx.doi.org/10.1111/j.1572-0241.2002.05913.x. Shen, HB, Chou, KC, 2008. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem. 373, 386–388, http://dx.doi.org/10.1016/j.ab.2007.10.012. Swan, C, Duroudier, NP, Campbell, E, Zaitoun, A, Hastings, M, Dukes, GE, Cox, J, Kelly, FM, Wilde, J, Lennon, MG, Neal, KR, Whorwell, PJ, Hall, IP, Spiller, RC, 2013. Identifying and testing candidate genetic polymorphisms in the irritable bowel syndrome (IBS): association with TNFSF15 and TNFalpha. Gut 62, 985–994, http://dx.doi.org/10.1136/gutjnl-2011-301213. Wu, C, Orozco, C, Boyer, J, Leglise, M, Goodale, J, Batalov, S, Hodge, CL, Haase, J, Janes, J, Huss 3rd, JW, Su, AI, 2009. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 10, R130, http://dx.doi.org/10.1186/gb-2009-10-11-r130. Xia, Y, Campen, A, Rigsby, D, Guo, Y, Feng, X, Su, EW, Palakal, M, Li, S, 2007. DGEM– a microarray gene expression database for primary human disease tissues. Mol. Diagn. Ther. 11, 145–149. Xiao, X, Min, JL, Wang, P, Chou, KC, 2013. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One 8, e72234, http://dx.doi.org/10.1371/journal.pone.0072234. Xu, Y, Shao, XJ, Wu, LY, Deng, NY, Chou, KC, 2013. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine Snitrosylation sites in proteins. PeerJ 1, e171, http://dx.doi.org/10.7717/peerj.171.
Contents lists available at ScienceDirect
Journal of Theoretical Biology journal homepage: www.elsevier.com/locate/yjtbi
TIBS: A web database to browse gene expression in irritable bowel syndrome Jing Yan a, Yan Xu b, Brian Hu c, Sammy Alnajm d, Lina Liu a, Yin Lu e, Zhiguang Sun a,n, Feng Cheng b,f,nn a
Institute of First Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing 210046, Jiangsu, People Republic of China Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa 33612, USA c Essenology Software Inc., Wuxi 214187, People Republic of China d Department of Biology, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne 50674, Germany e Department of Pharmacology, College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, People Republic of China f Department of Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa 33612, USA b
H I G H L I G H T S
A web database to browse gene expression in irritable bowel syndrome patients. Spatial difference in gene expression across four different tissues. Gene expression in IBS patients compared to healthy volunteers or UC patients. Sex difference in gene expression in IBS patients.
art ic l e i nf o
a b s t r a c t
Article history: Received 18 January 2014 Received in revised form 19 February 2014 Accepted 14 March 2014 Available online 24 March 2014
Irritable bowel syndrome (IBS) is a chronic functional gastrointestinal disorder. Its symptoms include chronic abdominal pain, bloating gas, diarrhea and constipation. Many IBS patients also have psychological symptoms like depression or anxiety. These unpleasant symptoms significantly lower patients' quality of life. The prevalence of IBS in Europe and North America is about 10–15% of the population, which makes IBS a disorder with a high social cost. The pathophysiology of IBS is considered to be multifactorial and the exact cause of the disease remains poorly understood. Recently, a genome-wide expression microarray technique has been applied to investigate the possible mechanisms of IBS. However, a user-friendly database that allows scientists without bioinformatics background to query gene expression levels in these data sets and compare gene expression patterns across different tissues has not yet been established. Therefore, we have integrated four public expression microarray data (320 samples) from the Gene Expression Omnibus (GEO) and ArrayExpress databases into an online database called Transcriptome of Irritable Bowel Syndrome (TIBS). The gene expression change in IBS patients compared to healthy volunteers or UC patients in jejunum, sigmoid colon, rectum, and descending colon can be queried by gene symbols. Users can compare gene expression levels of IBS patients across these tissues. Sex difference of gene expression in IBS patients was also shown in the database. The current version of TIBS database contains 42,400 annotated gene probe sets represented on the Affymetrix Human Genome U133 plus 2.0 platform. TIBS will be an invaluable resource for a better understanding of the pathogenesis of IBS at the molecular level and for drug development. The TIBS database is available online at http://www.chengfeng.info/tibs_database.html. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Microarray Online server IBS Ulcerative colitis Bioinformatics
n Corresponding author at: Institute of First Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, 210046, Jiangsu, People Republic of China. Tel.: þ 86 25 8362 0504; fax: þ 86 25 8632 0532. nn Corresponding author at: Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa 33612, USA. Tel.: þ1 813 974 4288; fax: þ1 813 905 9890. E-mail addresses: [email protected] (J. Yan), [email protected] (Y. Xu), [email protected] (B. Hu), [email protected] (S. Alnajm), [email protected] (L. Liu), [email protected] (Y. Lu), [email protected] (Z. Sun), [email protected] (F. Cheng).
http://dx.doi.org/10.1016/j.jtbi.2014.03.026 0022-5193/& 2014 Elsevier Ltd. All rights reserved.
1. Introduction Irritable bowel syndrome (IBS) is a chronic functional gastrointestinal disorder. The symptoms of IBS include chronic abdominal pain, bloating gas, diarrhea and constipation (Anastasi et al., 2009). Based on the symptoms, IBS could be mainly classified into two subgroups: IBS with constipation (IBS-C) and IBS with diarrhea (IBS-D). Many IBS patients also have psychological
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Table 1 Four microarray data sets integrated in the TIBS database. Data set
Cell or tissue type
#IBS
#Non-IBS
IBS vs HV GSE14841 GSE36701
Jejunal biopsies Rectal biopsies
5 (IBS-D) 87 (34 IBS-C, 53 IBS-D) 76 (32 IBS-C, 44 IBS-D)
4 HV 40 HV
10
17 UC
10
17 UC
E-TABM-176 IBS vs UC GSE13367 GSE13367
Sigmoid colon mucosal biopsies Mucosal colonic biopsy from the descending colon Colonocytes from the descending colon
55 HV
HV: healthy volunteers; UC: ulcerative colitis; IBS-C: IBS with constipation; IBS-D: IBS with diarrhea.
symptoms like depression and anxiety (Lydiard, 2001). These symptoms significantly lower patients' quality of life. The prevalence of IBS in North America and Europe is around 10–15% of the population (Quigley et al., 2006; Saito et al., 2002). The disease affects significantly more women than men. It was estimated that 60–75% of IBS patients are women (Hulisz, 2004). The IBS is costly and the annual medical costs for IBS are over $20 billion in the United States (Hulisz, 2004). The high prevalence and medical expense make IBS a disorder with a high social cost. However, there is still no effective cure for IBS clinically because the pathophysiology of IBS is not well understood (Andresen and Camilleri, 2006). The only treatments that attempt to relieve symptoms such as dietary modification or psychotherapies are often chosen (Camilleri and Andresen, 2009). Additionally, some inflammatory bowel diseases such as ulcerative colitis (UC) have similar symptoms as IBS, which can make diagnosis of IBS difficult. Precise detection of gene expression changes in IBS patients is important to the understanding of the pathophysiology of IBS at molecular level (Andresen and Camilleri, 2006; Camilleri and Andresen, 2009). Recently, expression microarray has been applied to detect gene expression of IBS patients in different tissues such as the jejunum, the sigmoid colon, the rectum, and the descending colon. The expression microarray technique is a high-throughput genomic method that could detect the gene expression levels of thousands of genes simultaneously and investigate genome-wide transcriptional patterns (Cheng, 2012; Cheng et al., 2010, 2011). These projects generated valuable data for IBS research. However, a user-friendly database that allows scientists without bioinformatics background to query gene expression levels in these data sets and compare gene expression patterns across different tissues has not yet been established. The development of biological web database or server is a core area of bioinformatics (Cheng, 2013; Chou and Shen, 2008; Shen and Chou, 2008). Many studies have indicated that user-friendly web-servers can timely provide very useful information and insights for biological systems such as showing mRNA expression levels (Chintapalli et al., 2007; Wu et al., 2009; Xia et al., 2007), predicting the heat shock protein families (Feng et al., 2013), identifying nucleosomes (Xiao et al., 2013), identifying DNA recombination spots (Chen et al., 2013), protein subcellular location prediction (Chou et al., 2012), protein cleavage site prediction (Chou, 1996), and hence are widely welcome by the science community (Chou and Shen, 2009). The present study is attempted to establish a web database called Transcriptome of Irritable Bowel Syndrome (TIBS) for browsing gene expression in IBS in hopes that it can become a useful tool for drug development.
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The TIBS database was generated from 320 samples collected from four public expression microarray data sets, E-TABM-176 from the ArrayExpress database, GSE14841, GSE36701(Swan et al., 2013), and GSE13367 (Aerssens et al., 2008) from the NCBI GEO database. Gene expression in IBS patients compared to healthy volunteers or UC patients in jejunum, sigmoid colon, rectum and descending colon can be queried by gene symbols. Users can compare gene expression levels of IBS patients across these tissues. Sex difference in gene expression of IBS patients could also be shown in the database. The current version of TIBS contains 42,400 annotated gene probe sets represented on the Affymetrix Human Genome U133 Plus 2.0 platform. TIBS will be an invaluable tool and resource for scientists with little experience in bioinformatics to identify reliable genes for IBS pathophysiology studies or drug discovery. The TIBS database is available online at http://www. chengfeng.info/tibs_database.html.
2. Material and methods 2.1. Data sources 320 samples in four published microarray sets (shown in Table 1) from the GEO (http://www.ncbi.nlm.nih.gov/geo/) and the ArrayExpress (http://www.ebi.ac.uk/arrayexpress/) database were integrated in the TIBS. Samples in these four sets were profiled using the Affymetrix Human Genome U133 Plus 2.0 GeneChip Array containing 54,000 probe sets that assay expression of about 38,500 human gene transcripts. Three microarray data sets, GSE14841, GSE36701(Swan et al., 2013), and E-TABM-176, were chosen to compare gene expression from healthy volunteers and IBS patients. The first data set, GSE14841, included jejunal biopsy samples from four healthy people and five IBS-D patients. The second data set, GSE36701, evaluated the gene expression levels in rectal biopsies from 87 IBS patients (34 IBS-C and 53 IBS-D) and 40 healthy volunteers. The third data set, E-TABM-176 from the ArrayExpress database, contains transcription profiling of sigmoid colon mucosal biopsies from 76 IBS patients (32 IBS-C, 44 IBS-D) and 55 healthy volunteers. Microarray data set, GSE13367 (Aerssens et al., 2008), was chosen to investigate the gene expression of IBS patients compared to patients with ulcerative colitis. The data set contained mucosal colonic biopsies and isolated colonocytes from the descending colon in patients with active UC (n ¼8), quiescent UC (n ¼9), and with IBS (n ¼10). 2.2. Data normalization and analysis Raw Affymetrix CEL files of these four microarray data sets were downloaded from the GEO and the ArrayExpress database. The RMAExpress program (http://rmaexpress.bmbolstad.com/) was chosen to normalize all CEL files in these four data sets and to summarize expression of gene probe sets. Default settings including RMA background correction, quantile normalization, and log2-transformation were chosen. In the current version of TIBS database, 42,400 annotated gene probe sets with official gene symbols were included. A boxplot has been plotted to check whether the distributions of samples from different data sets are comparable. Comparisons of the mean expression levels between two groups and among three groups were carried out by the t test and anova functions, respectively, in the R program (http://www. r-project.org/). The gene expression levels were plotted using the barplot function in R.
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Fig. 1. The query interface of the TIBS database. The input to the database is official gene symbols. All probe sets representing the queried gene will be shown; each probe set is reported in one pdf file. DUOX2 gene was used as an example to query the TIBS database in this paper. The gene has one probe set in the Affymetrix Gene-Chip Human Genome U133 plus 2.0 platform, 219727_at.
3. Results
2.0 platform, 219727_at (shown in Fig. 1). The output results of this probe set are shown in Figs. 2 and 3.
3.1. Query interface of the TIBS database TIBS is a php-based online database powered by the iPage incorporation (http://www.ipage.com). Analyzed microarray data are stored in a mySQL relational database. As shown in Fig. 1, the input to the system is official gene symbols (for example, DUOX2 or PPARG), which could be looked up from the NCBI gene bank (http://www.ncbi.nlm.nih.gov/gene) or the GeneCards database (http://www.genecards.org). All probe sets representing the queried gene will be shown; each probe set is reported in one Acrobats pdf file. The pdf file includes two graphs. The first graph provides gene expression levels in IBS patients compared to healthy volunteers or UC patients in four different tissues including jejunum, sigmoid colon, rectum, and descending colon. The second graph shows sex difference in gene expression of IBS patients in rectum and descending colon. TIBS provides some user-friendly functions to query the database. Users can enter a list of multiple gene symbols separated by spaces for the batch searching (for example, “DUOX2 PPARG HSPA6”). Results of all genes in the list will be shown. Users can also enter a query string for the fuzzy searching (For example, “DUOX” or “PAR”). Any gene symbols containing the query string will be displayed. 3.2. Output of the TIBS database DUOX2 gene was used as an example to query the TIBS database in this paper. The gene encodes dual oxidase 2, an enzyme prominent in the gastrointestinal tract (El Hassani et al., 2005). Dual oxidase 2 contains both an NADPH oxidase domain and a heme peroxidase domain. The function of the enzyme is to regulate the production of reactive oxygen species, which plays an important role in host defense mechanisms (Rokutan et al., 2008). DUOX2 has been proved to be an IBS related gene (Aerssens et al., 2008). The gene has one probe set in the Affymetrix Gene-Chip Human Genome U133 plus
3.2.1. Spatial difference in gene expression across four tissues The first graph of the output pdf file (Fig. 2) compares the gene expression profiles of IBS patients with healthy volunteers or UC patients in jejunum, sigmoid colon, rectum, and descending colon. Users can compare gene expression levels of IBS patients across these tissues. As shown in Fig. 2, there is a spatial difference in gene expression levels among four tissues. DUOX2 shows low expression levels (signal intensity is lower than 4) in jejunal biopsies from both healthy volunteers and IBS-D patients. The expression levels of DUOX2 are higher in rectal biopsies, sigmoid colon mucosal biopsies and descending colons. This finding agrees with the conclusions from Mark T. Quinn that DUOX2 is highly expressed in distal gastrointestinal tract, such as sigmoidal colon and rectal glands (Quinn, 2013). 3.2.2. Gene expression in IBS patients compared to healthy volunteers or UC patients As shown in Fig. 2, DUOX2 shows significant difference in expression levels among healthy volunteers, IBS-C and IBS-D patients in sigmoid colon mucosal (P-value ¼0.00059) and in rectal biopsies (P-value ¼0.003674). The findings are consistent with the PCR results reported by Aerssens et al. (2008) that DUOX2 mRNA expression levels in the colon mucosal biopsies of IBS patients increased. DUOX2 shows higher expression levels in UC patients than in IBS patients in descending colons. The DUOX2 gene is also more upregulated in active UC than in quiescent UC patients. The expression patterns are consistent with the findings from Linpinski, et al. that colonic epithelial cells express high levels of DUOX2 under proinflammatory conditions (Lipinski et al., 2009). Although UC has very similar symptoms as IBS, the pathophysiology of these two diseases is quite different. UC is a type of inflammatory bowel disease while
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Fig. 2. The first graph of the output pdf file from the TIBS database (the query gene: DUOX2). The graph shows gene expression levels in IBS patients compared to healthy volunteers or UC (including active UC (UC_inflamed) and quiescent UC (UC_noninflamed)) patients in jejunum, sigmoid colon, rectum, and descending colon. Comparisons of the mean expression levels between two groups and among three groups were carried out by the t.test and anova functions, respectively, in the R program. Barplots of the gene expression levels were plotted using the barplot function in R.
Fig. 3. The second graph of the output pdf file from the TIBS database (the query gene: DUOX2). The graph compared gene expression levels in male samples (blue) to female samples (pink) in rectum and descending colon. Comparisons of the mean expression levels between two groups and among three groups were carried out by the t.test and anova functions, respectively, in the R program. Barplots of the gene expression levels were plotted using the barplot function in R. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
IBS does not cause obvious inflammation. DUOX2 plays an important role in host defense mechanisms during inflammation, which can explain why the gene shows higher expression levels in UC patients than in IBS patients.
3.2.3. Sex difference in gene expression Sex difference was shown in IBS patients. IBS affects significantly more women than men. It was estimated that 60–75% of IBS patients are women (Hulisz, 2004). The second graph of the
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Fig. 4. The second graph of the output pdf file from the TIBS database (the query gene: EIF1AY). The graph compared gene expression levels in male samples (blue) to female samples (pink) in rectum and descending colon. Comparisons of the mean expression levels between two groups and among three groups were carried out by the t.test and anova functions, respectively, in the R program. Barplots of the gene expression levels were plotted using the barplot function in R. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
output pdf file (Fig. 3) from the TIBS database could help researchers to identify sex difference in gene expression of IBS patients. Sex differences in DUOX2 expression levels of IBS patients are observed in rectal biopsies. As shown in Fig. 3, male IBS patients did not show a significant difference from healthy people (P-value ¼0.0898), however female IBS patients did (P-value ¼0.00525). A Y-chromosome gene, EIF1AY, was also chosen to validate the sex difference shown in the TIBS database. The EIF1AY gene encodes eukaryotic translation initiation factor 1A that stabilizes the binding of the initiator Met-tRNA to 40S ribosomal subunits in the initiation phase of eukaryotic translation. The gene has two probe sets in the Affymetrix Gene-Chip Human Genome U133 plus 2.0 platform, 204409_s_at and 204410_at. The output results of the first probe set are shown in Fig. 4. As shown in the figure, the gene EIF1AY shows much higher expression levels in male than in female samples, which is consistent with the knowledge that the Y chromosome is only present in males and that genes on the Y chromosome are expressed only in males. The results proved the reliability of data in the TIBS database.
genes in which tissue would be important for IBS development. The TIBS database provides useful information regarding the study of molecular mechanism of IBS and aid in the discovery of novel drugs. The difference of males and females shown in the database might provide the basis for understanding the molecular mechanisms underlying the sex differences in the incidence, prevalence and severity of IBS. User-friendly and publicly accessible web-servers represent the future direction in bioinformatics (Chou and Shen, 2009; Qiu et al., 2014) as demonstrated by recent publications (Chen et al., 2012, 2013; Feng et al., 2013; Qiu et al., 2014; Xu et al., 2013). We are still expanding our database by adding more public microarray data sets. All samples in our current database were profiled with Affymetrix Human Genome U133 Plus 2.0 GeneChip Array. Next stage we would like to include more IBS samples from different platforms such as Agilent microarrays (http://www.genomics.agilent.com/). In the next version we plan to expand to up to 500 samples. Analyzed results from the TIBS database will be more reliable. New functions such as gene network analysis will also be added in the future.
Acknowledgment 4. Conclusions and prospective The TIBS database is very important resource for IBS research based on two reasons. First, previous transcriptome studies of IBS have used relatively small numbers of samples and only focused on one tissue or some specific genes. TIBS is the first research database that combined several public microarray data and systematically analyzed the gene expression patterns in IBS patients at the transcriptome level. The collection we used in this study was more than 300 samples (four data sets) from several tissues. There was no such database in this field. Second, researchers can freely access the database to check the gene expression profiles of more than 40,000 gene probe sets. TIBS will help scientists with little experience in bioinformatics to identify which
This work was supported by Specialized Research Fund for the Doctoral Program of Higher Education (No. 20133237110008), Jiangsu College Graduate Research and Innovation Projects (No. CXZZ12_0618) and, in part, by the University of South Florida System Internal Awards Program under USF Women’s Health Collaborative Grant No. 0095058. References Aerssens, J, Camilleri, M, Talloen, W, Thielemans, L, Gohlmann, HW, Van Den Wyngaert, I, Thielemans, T, De Hoogt, R, Andrews, CN, Bharucha, AE, Carlson, PJ, Busciglio, I, Burton, DD, Smyrk, T, Urrutia, R, Coulie, B, 2008. Alterations in mucosal immunity identified in the colon of patients with irritable bowel
J. Yan et al. / Journal of Theoretical Biology 354 (2014) 48–53
syndrome. Clin. Gastroenterol. Hepatol. 6, 194–205, http://dx.doi.org/10.1016/j. cgh.2007.11.012. Anastasi, JK, McMahon, DJ, Kim, GH, 2009. Symptom management for irritable bowel syndrome: a pilot randomized controlled trial of acupuncture/moxibustion. Gastroenterol. Nurs. 32, 243–255, http://dx.doi.org/10.1097/SGA.0b013e3181b2c920. Andresen, V, Camilleri, M, 2006. Irritable bowel syndrome: recent and novel therapeutic approaches. Drugs 66, 1073–1088. Camilleri, M, Andresen, V, 2009. Current and novel therapeutic options for irritable bowel syndrome management. Dig. Liver Dis. 41, 854–862, http://dx.doi.org/ 10.1016/j.dld.2009.07.009. Chen, W, Feng, PM, Lin, H, Chou, KC, 2013. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res. 41, e68, http://dx.doi.org/10.1093/nar/gks1450. Chen, W, Lin, H, Feng, PM, Ding, C, Zuo, YC, Chou, KC, 2012. iNuc-PhysChem: a sequencebased predictor for identifying nucleosomes via physicochemical properties. PLoS One 7, e47843, http://dx.doi.org/10.1371/journal.pone.0047843. Cheng, F, 2012. Exploring the mysteries of traditional chinese medicine systematically by expression microarrays. Drug Dev. Res. 73, 499–503. Cheng, F, 2013. Bioinformatics Algorithms and Genomics The Open Bioinformatics Journal 7, 25–26. Cheng, F, Cho, SH, Lee, JK, 2010. Multi-gene expression-based statistical approaches to predicting patients' clinical outcomes and responses. Methods Mol. Biol. 620, 471–484, http://dx.doi.org/10.1007/978-1-60761-580-4_16. Cheng, F, Theodorescu, D, Schulman, IG, Lee, JK, 2011. in vitro transcriptomic prediction of hepatotoxicity for early drug discovery. J. Theor. Biol. 290, 27–36, http://dx.doi.org/10.1016/j.jtbi.2011.08.009. Chintapalli, VR, Wang, J, Dow, JA, 2007. Using FlyAtlas to identify better Drosophila melanogaster models of human disease. Nat. Genet. 39, 715–720, http://dx.doi. org/10.1038/ng2049. Chou, KC, 1996. Prediction of human immunodeficiency virus protease cleavage sites in proteins. Anal. Biochem. 233, 1–14, http://dx.doi.org/10.1006/abio.1996.0001. Chou, KC, Shen, HB, 2008. Cell-PLoc: a package of web servers for predicting subcellular localization of proteins in various organisms. Nat. Protoc. 3, 153–162, http://dx.doi.org/10.1038/nprot.2007.494. Chou, KC, Shen, HB, 2009. Review: recent advances in developing web-servers for predicting protein attributes. Nat. Sci. 2, 63–92. Chou, KC, Wu, ZC, Xiao, X, 2012. iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol. Biosyst. 8, 629–641, http://dx.doi.org/10.1039/c1mb05420a. El Hassani, RA, Benfares, N, Caillou, B, Talbot, M, Sabourin, JC, Belotte, V, Morand, S, Gnidehou, S, Agnandji, D, Ohayon, R, Kaniewski, J, Noel-Hudson, MS, Bidart, JM, Schlumberger, M, Virion, A, Dupuy, C, 2005. Dual oxidase2 is expressed all along the digestive tract. Am. J. Physiol. Gastrointest. Liver Physiol. 288, G933–G942, http://dx.doi.org/10.1152/ajpgi.00198.2004. Feng, PM, Chen, W, Lin, H, Chou, KC, 2013. iHSP-PseRAAAC: identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem. 442, 118–125, http://dx.doi.org/10.1016/j.ab.2013.05.024.
53
Hulisz, D, 2004. The burden of illness of irritable bowel syndrome: current challenges and hope for the future. J. Manag. Care Pharm. 10, 299–309. Lipinski, S, Till, A, Sina, C, Arlt, A, Grasberger, H, Schreiber, S, Rosenstiel, P, 2009. DUOX2-derived reactive oxygen species are effectors of NOD2-mediated antibacterial responses. J. Cell Sci. 122, 3522–3530, http://dx.doi.org/10.1242/ jcs.050690. Lydiard, RB, 2001. Irritable bowel syndrome, anxiety, and depression: what are the links? J. Clin. Psychiatry 62 (Suppl 8), 38–45 (discussion 46-7). Qiu, WR, Xiao, X, Chou, KC, 2014. iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int. J. Mol. Sci. 15, 1746–1766, http://dx.doi.org/10.3390/ijms15021746. Quigley, EM, Bytzer, P, Jones, R, Mearin, F, 2006. Irritable bowel syndrome: the burden and unmet needs in Europe. Dig. Liver Dis. 38, 717–723, http://dx.doi. org/10.1016/j.dld.2006.05.009. Quinn, MT, 2013. NADPH Oxidases: Structure and Function. Molecular Basis of Oxidative Stress: Chemistry, Mechanisms, and Disease Pathogenesis. John Wiley & Sons, Inc., Hoboken, New Jersey, pp. 137–178, http://dx.doi.org/ 10.1002/9781118355886.ch7. Rokutan, K, Kawahara, T, Kuwano, Y, Tominaga, K, Nishida, K, Teshima-Kondo, S, 2008. Nox enzymes and oxidative stress in the immunopathology of the gastrointestinal tract. Semin. Immunopathol. 30, 315–327, http://dx.doi.org/ 10.1007/s00281-008-0124-5. Saito, YA, Schoenfeld, P, Locke 3rd, GR, 2002. The epidemiology of irritable bowel syndrome in North America: a systematic review. Am. J. Gastroenterol. 97, 1910–1915, http://dx.doi.org/10.1111/j.1572-0241.2002.05913.x. Shen, HB, Chou, KC, 2008. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem. 373, 386–388, http://dx.doi.org/10.1016/j.ab.2007.10.012. Swan, C, Duroudier, NP, Campbell, E, Zaitoun, A, Hastings, M, Dukes, GE, Cox, J, Kelly, FM, Wilde, J, Lennon, MG, Neal, KR, Whorwell, PJ, Hall, IP, Spiller, RC, 2013. Identifying and testing candidate genetic polymorphisms in the irritable bowel syndrome (IBS): association with TNFSF15 and TNFalpha. Gut 62, 985–994, http://dx.doi.org/10.1136/gutjnl-2011-301213. Wu, C, Orozco, C, Boyer, J, Leglise, M, Goodale, J, Batalov, S, Hodge, CL, Haase, J, Janes, J, Huss 3rd, JW, Su, AI, 2009. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 10, R130, http://dx.doi.org/10.1186/gb-2009-10-11-r130. Xia, Y, Campen, A, Rigsby, D, Guo, Y, Feng, X, Su, EW, Palakal, M, Li, S, 2007. DGEM– a microarray gene expression database for primary human disease tissues. Mol. Diagn. Ther. 11, 145–149. Xiao, X, Min, JL, Wang, P, Chou, KC, 2013. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One 8, e72234, http://dx.doi.org/10.1371/journal.pone.0072234. Xu, Y, Shao, XJ, Wu, LY, Deng, NY, Chou, KC, 2013. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine Snitrosylation sites in proteins. PeerJ 1, e171, http://dx.doi.org/10.7717/peerj.171.
