Yuan et al. European Journal of Medical Research 2014, 19:31 http://www.eurjmedres.com/content/19/1/31 EUROPEAN JOURNAL OF MEDICAL RESEARCH

RESEARCH Open Access Reanalysis of the expression profile in chronic pancreatitis via bioinformatics methods Hang Yuan, Bin Wu, Senlin Ma, Xiaoyu Yang, Lei Yin and Aijun Li*

Abstract Background: Diagnosis at an early stage of chronic pancreatitis (CP) is challenging. It has been reported that microRNAs (miRNAs) are increasingly found and applied as targets for the diagnosis and treatment of various cancers. However, to the best of our knowledge, few published papers have described the role of miRNAs in the diagnosis of CP. Method: We downloaded profile data from the Genee Expression Omnibus and identified differentially expressed (DEGs) between CP and normal samples of Harlan mice and Jackson Laboratory mice. Common DEGs were filtered out, and the semantic similarities of gene classes were calculated using the GOSemSim software package. The gene class with the highest functional consistency was selected, and then the Lists2Networks web-based system was used to analyse regulatory relationships between miRNAs and gene classes. The functional enrichment of the gene classes was assessed based on (GO) and Kyoto Encyclopedia of Genes and Genomes pathway annotation terms. Results: A total of 405 common upregulated DEGs and 7 common downregulated DEGs were extracted from the two kinds of mice. Gene cluster D was selected from the common upregulated DEGs because it had the highest semantic similarity. miRNA 124a (miR-124a) was found to have a significant regulatory relationship with cluster D, and DEGs such as CHSY1 and ABCC4 were found to be regulated by miR-124a. The GO term of response to DNA damage stimulus and the pathway of Escherichia coli infection were significantly enriched in cluster D. Conclusion: DNA damage and E. coli infection might play important roles in CP pathogenesis. In addition, miR-124a might be a potential target for the diagnosis and treatment of CP. Keywords: Chronic pancreatitis, Differentially expressed genes, Gene cluster, miRNA

Background miRNAs inhibit the transcription levels of mRNA, in- Chronic pancreatitis (CP) is characterized by pancreatic duce degradation of the regulation of gene expression inflammation and fibrosis, and it arises when pancreatic [7] and have been proved to be involved in many dis- injury is followed by a sustained immune activation in ease processes. Therefore, the identification of miRNA which fibrosis dominates [1]. Environmental triggers of changes might explain the pathology of CP in another pancreatic inflammation and disease susceptibility (such way and provide a new method for diagnosing CP. as alcohol use, smoking, pancreatic duct obstruction and A number of miRNAs that have been studied have a drugs) or modifying genes (including PRSS1, SPINK1 role in pancreatic diseases. By comparing pancreatic and CFTR) act synergistically to cause CP [1,2]. It has cancer tissue to CP tissue and normal pancreas, Bloom- also been indicated that CP is often an underlying cause ston and colleagues identified 21 miRNAs with increased of pancreatic cancer [3]. Meanwhile, in recent years, re- expression and 4 with decreased expression, which sug- searchers in a growing number of studies have suggested gests that the miRNAs likely play an important regulatory that microRNAs (miRNAs) play an important role in the role in pancreatic cancer [3]. It has also been demon- diagnosis and prognosis of pancreatic cancers [3-6]. strated that the expression of miRNA-196a (miR-196a) is high in pancreatic ductal adenocarcinoma (PDAC) but * Correspondence: [email protected] low in CP and normal tissues, whereas miR-217 exhibits Department of the 2nd Special Treatment, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, No 225, Changhai Road, Shanghai 200438, China

© 2014 Yuan et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Yuan et al. European Journal of Medical Research 2014, 19:31 Page 2 of 6 http://www.eurjmedres.com/content/19/1/31

the opposite expression pattern [8]. The ratio of miR-196a Gene cluster analysis of common differentially expressed to miR-217 has been found to indicate whether tissue genes samples contain PDAC [9]. More and more miRNAs have Gene cluster analysis can be used to divide genes into been found to be related to pancreatic cancers, and CP several classes based on certain similarity criteria, such specimens are often used as a second control [3,9]. How- as the Pearson correlation coefficient or Euclidean dis- ever, few published papers have specifically described the tance [13,14]. It has been proved that genes in the same relationship between CP and its miRNAs. cluster have a high degree of homogeneity. In our In the present study, we analysed the gene expression present study, we used the second-order tolerance ana- profile of CP and normal mice to screen for differentially lysis (SOTA) method [15], a toolset of gene expression expressed genes (DEGs). We identified the related miR- profile analysis [16], to perform cluster analysis on the NAs, which might provide further insights into the mo- common DEGs based on the gene expression values. lecular mechanisms of CP. Understanding the molecular The Euclidean distance was employed as the clustering mechanisms of CP might aid in diagnosing and treating indicator. Next, we calculated the semantic similarity of CP patients. gene classes using the GOSemSim software package [17], and the class of genes with the highest functional consistency was selected as the optimal gene cluster for Methods further study. Data sources We downloaded a gene data set [GEO:GSE41418] [10] Related microRNAs of optimal gene cluster and GO and from the Gene Expression Omnibus (GEO) database (http:// KEGG pathway analysis www.ncbi.nlm.nih.gov/geo/). Gene expression analysis was In organisms, highly coexpressed genes are likely to share performed on a GeneChip Mouse Genome 430 Plus 2.0 common regulatory patterns and to participate in the Array platform (Affymetrix, Santa Clara, CA, USA). The data same or similar biological processes and pathways [18]. In set contains two different kinds of mice: Harlan mice order to study the regulatory mechanisms of the optimal (C57BL/6NHsd; Harlan Laboratories, Indianapolis, IN, gene cluster, we used the Lists2Networks web-based sys- USA) and Jackson Laboratory mice (C56BL/6 J; The tem [19] to analyse the possible relationship between the Jackson Laboratory, Bar Harbor, ME, USA). A frequently miRNAs and the optimal gene cluster. The functional en- used experimental model of CP recapitulating human dis- richment of the target genes of two regulators (transcrip- ease is repeated injections of cerulein into mice. We found tion factors and miRNAs) was assessed based on the Gene that two common substrains of C57BL/6 mice (C56BL/6 J Ontology (GO) and Kyoto Encyclopedia of Genes and and C57BL/6NHsd) exhibit different degrees of CP, with Genomes (KEGG) pathway annotation terms. GO and C57BL/6 J mice being more susceptible to repetitive KEGG signalling pathway analyses were performed using cerulean-induced CP. The goal of this study was to iden- the GOstats R package software package (http://www.r- tify genes associated with CP and to identify differentially project.org/), with which we carried out the standard regulated genes between two substrains as candidates for hypergeometric test. We was also performed GO and KEGG the CP progression. We included six mice of each type, in- enrichment analysis on the gene cluster, with P-values cluding three CP samples and three normal samples [10]. less than 0.05 considered statistically significant.

Results Identification of differentially expressed genes Identification of differentially expressed genes Expression profile data were normalized with GeneChip According to the predetermined FDR threshold ≤0.05, robust multiarray analysis [11]. Next, we preprocessed the 962 DEGs of Harlan mice, including 911 upregulated data derived from 12 samples for subsequent analysis. We genes and 51 downregulated genes, were screened out. annotated expression profiling probes to gene symbols. If In Jackson mice, a total of 1,545 genes were differentially there were multiple probe sets that corresponded to one expressed, and these DEGs comprised 1,423 upregulated gene, the expression values of those probe sets were aver- genes and 122 downregulated genes. Next, we extracted aged. Using this method, we obtained an expression data overlapping DEGs in both mice, which consisted of 405 set comprising 21,389 genes. Afterward, Significance Ana- upregulated genes and 7 downregulated genes (Figure 1). lysis of Microarrays 4.0 software [12] was used to screen We clearly observed that the number of upregulated the DEGs between the CP samples and normal controls of genes was significantly greater than that of downregu- the two kinds of mice, respectively. The overlapping DEGs lated genes. We speculate that these upregulated genes were denoted as common DEGs and were used for further might play a major role in CP disease. In the experimen- analysis. A fold discovery rate (FDR) ≤0.05 was selected as tal work following this observation, we analysed only the the threshold for screening DEGs. upregulated common DEGs. Yuan et al. European Journal of Medical Research 2014, 19:31 Page 3 of 6 http://www.eurjmedres.com/content/19/1/31

Figure 1 Common differentially expressed genes of the two mouse breeds studied. The red and blue parts represent, respectively, the upregulated common differentially expressed genes (DEGs) and downregulated common DEGs.

Gene clustering of upregulated common differentially Lists2Networks, we found that cluster D could be signifi- expressed genes cantly regulated by miR-124a, which might play an im- Using the Euclidean distances as the clustering indicators portant role in the development of CP. in SOTA, we obtained four clearly separated gene classes miR-124a was first identified by cloning studies in (Figure 2) of the upregulated common DEGs. Next, we mice [20]. Studies have shown that miR-124a plays an calculated the semantic similarity scores of gene classes important role in the control of cell survival, prolifera- (Table 1). As a result, gene cluster D was found to have tion, differentiation and metabolism and whose dysfunc- the highest average semantic similarity score (0.2868) and tion is a potential cause of disease [21-23]. In addition, was selected for further analysis. published data have demonstrated that miR-124a expres- sion level was increased in the mouse pancreas at the em- Related microRNAs and functional analysis of the optimal bryonic stage and have indicated its important role in gene cluster pancreas development [23]. Therefore, we hypothesized According to the enrichment analysis of Lists2Networks, miR-124a might play an important pathogenic role in CP. miR-124a was found to have a significant regulation rela- CHSY1 encodes a member of the chondroitin N- tionship with cluster D (Table 2). And genes such as CHSY acetylgalactosaminyltransferase family, possesses dual glu- (chondroitin sulphate synthase 1) and ABCC4 (ATP-bind- curonyltransferase and galactosaminyltransferase activity ing cassette, subfamily C (CFTR/MRP), member 4) were and plays critical roles in the biosynthesis of chondroitin enriched and in correlation with miR-124a. According to sulphate, a glycosaminoglycan involved in many biological GO and KEGG pathway enrichment on gene cluster D, processes, including cell proliferation and morphogenesis we found that the most significant biological process was [24-26]. CHSY1 was one of the significant genes in cluster response to DNA damage stimulus (Table 3), and PAPR3 D and was enriched and regulated by miR-124a. Re- was one of the significant DEGs enriched in the GO term. searchers in a previous study demonstrated that CHSY1 The observed significant pathways were associated with regulated its downstream target CASP1 (caspase 1, also the and Escherichia coli infection (Table 4). known as interleukin 1β–converting ), which could cleave interleukin 1β precursors into mature cyto- Discussion kines and contribute to inflammation [27]. Surprisingly, In the present study, we screened out 405 common upreg- increased expression of CASP1 has been reported to be a ulated DEGs of the two kinds of mice used, and GOSem- frequent event in CP [28]. Thus, miR-124a might partici- Sim was used to calculate the semantic similarity of the pate in CP manifestation and development by regulating gene clusters of the DEGs. Cluster D was selected as the expression levels of CHSY1 or CASP1. optimal gene class for further investigation because of it ABCC4 is another significant gene regulated by miR- had the highest average semantic similarity. Using the 124a. It is a member of the ATP-binding cassette

Figure 2 Dendrogram used for clustering analysis of the common upregulated differentially expressed genes. As shown in the diagram, the genes are divided into four categories (A, B, C and D). Yuan et al. European Journal of Medical Research 2014, 19:31 Page 4 of 6 http://www.eurjmedres.com/content/19/1/31

Table 1 Semantic similarity scores of the gene clusters Table 3 Gene Ontology database enrichment analysis of Cluster Gene number Semantic similarity score cluster D Cluster A 51 0.2145412 Gene Ontology term Genes P-value Cluster B 91 0.2525834 Response to DNA damage stimulus PARP3, TOP2A, RAD51 0.0025 Cluster C 121 0.272545 DNA metabolic process PRIM1, RFC1, PARP3 0.0041 Cluster D 110 0.2867982 organisation CDCA8, H2AFX, RFC1 0.0047 Chromosome condensation NCAPH, TOP2A 0.0065 Chromosome segregation MIS12, TOP2A 0.0073 transporter superfamily, which has been shown to com- Cellular response to DNA damage TOPBP1, PARP3, TOP2A 0.0080 prise key mediators of drug efflux and multidrug resist- stimulus ance in many types of tumours and inflammatory mRNA export from nucleus AGFG1, RAE1 0.0089 diseases [29-31]. A previous study also been implicated DNA packaging NCAPH, TOP2A 0.0106 ABCC4 as an efflux pump of proinflammatory mediators such as LTB4 and LTC4, and ABCC4 may represent a Response to ionizing radiation TOPBP1, H2AFX 0.0115 novel target for anti-inflammatory therapies [32]. There- RNA export from nucleus AGFG1, RAE1 0.0124 fore, miR-124a might regulate the inflammatory disease Endocytosis SNX4, SFTPD, CD14 0.0150 of CP by changing the levels of proinflammatory media- Regulation of ubiquitin- BUB3, CDC23, CCNB1 0.0155 tors by ABCC4. ligase activity On the basis of the results of GO enrichment analysis RNA transport AGFG1, RAE1 0.0166 of gene cluster D, the most significant biological process DNA repair TOPBP1, PARP3, TOP2A 0.0170 we observed was the response to DNA damage stimulus. Nuclear export AGFG1, RAE1 0.0290 This suggested that DNA damage might play an import- ant role in the pathogenesis of CP. The results of our DNA replication RFC1, TOP2A, MCM5 0.0324 analysis are in line with those of a previous study [33]. Organelle organisation CDCA8, BUB3, H2AFX 0.0357 PARP3 is one significant gene that is enriched in the bio- Response to stress PARP3, PHLDA3, LSP1 0.0359 logical process of response to DNA damage stimulus. It Cellular response to stimulus PARP3, TOP2A, RAD51 0.0375 belongs to the poly(ADP-ribose) polymerase (PARP) Innate immune response TUBB2C, SFTPD 0.0441 family [34]. PARP3 catalyses the reaction of ADP ribosy- lation, a key posttranslational modification of in- Furthermore, E. coli might also lead to pancreatic abscess, volved in different signalling pathways from DNA damage which is defined as an acute inflammatory process of the to energy metabolism and organismal memory [35]. In pancreas [42]. It has been proved that E. coli organisms addition, recent studies have clearly demonstrated the role can induce polymorphonuclear leucocyte infiltration dur- of PARP activation in various forms of local inflammation ing clinical infection [43]. Therefore, we suggest that [36-38]. Information about the role of PARP3 in CP is E. coli infection might be involved in the occurrence of CP. sparse; however, it has been shown that other members of This study has some limitations. First is the small sam- the PARP family, such as PARP1, coactivate the transcrip- ple size obtained from the GEO database. Second, valid- tion factor nuclear factor κB (NF-κB) and is required for ation of the results in other data sets or samples is NF-κB-mediated inflammatory responses [39]. CP is char- lacking. Therefore, further genetic studies with larger acterized by pancreatic inflammation, thus PARP3 might sample sizes and different kinds of CP samples are potentially play a role in its inflammatory processes. needed to confirm our observations. In KEGG pathway analysis, it has been shown that E. coli infection might play an important role in CP. Karmali and colleagues reported that infection with E. coli Table 4 KEGG enrichment analysis of cluster Da produced postdiarrhoeal haemolytic uraemia syndrome KEGG_PATHWAY Genes P-value and that many patients who recovered from it had long- hsa04110 cell cycle RBL1, CDC23, MCM5 1.80E-04 term sequelae, including CP and cholelithiasis [40,41]. hsa05131 enteropathogenic TUBB2A, TUBB2C, CD14 0.007263 E. coli infection

Table 2 Regulatory microRNAs predicted for cluster D hsa05130 enteropathogenic TUBB2A, TUBB2C, CD14 0.007263 E. coli infection Term Genes Combined score P-value hsa04115 p53 signalling pathway CCNB1, CCNB2, APAF1 0.013926 TGCCTTA, mIR-124a CHSY1, ABCC4, CASC4 8.818 0.00828 hsa04640 hematopoietic cell lineage CD38, IL1R1, CD14 0.026877 TCTAGAG, mIR-517 SLC39A10, RND3 7.065 0.03707 hsa00600 sphingolipid metabolism SGMS1, B4GALT6 0.034673 TAGAACC, mIR-182 FAM107B, RBM12 6.138 0.02771 aE. coli, Escherichia coli; KEGG, Kyoto Encyclopedia of Genes and Genomes. Yuan et al. European Journal of Medical Research 2014, 19:31 Page 5 of 6 http://www.eurjmedres.com/content/19/1/31

Conclusions 10. Ulmasov B, Oshima K, Rodriguez MG, Cox RD, Neuschwander-Tetri BA: miR-124a provides some guidance for the mechanism of Differences in the degree of cerulein-induced chronic pancreatitis in C57BL/6 mouse substrains lead to new insights in identification of CP pathogenesis and is a potential target for the diagno- potential risk factors in the development of chronic pancreatitis. Am J sis and treatment of CP. miR-124a might participate in Pathol 2013, 183:692–708. CP occurrence and development by regulating expres- 11. Gharaibeh RZ, Fodor AA, Gibas CJ: Background correction using dinucleotide affinities improves the performance of GCRMA. BMC sion levels of CHSY1 or CASP1. Also, miR-124a might Bioinforma 2008, 9:452. regulate the inflammatory disease of CP by changing the 12. Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays level of proinflammatory mediators by ABCC4. In addition, applied to the ionizing radiation response. 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doi:10.1186/2047-783X-19-31 Cite this article as: Yuan et al.: Reanalysis of the gene expression profile in chronic pancreatitis via bioinformatics methods. European Journal of Medical Research 2014 19:31.

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