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* = Presenting author

P759 MiRNA expression patterns in colon of active and inactive ulcerative colitis

Juzenas S.*1, Skieceviciene J.1, Salteniene V.1, Kupcinskas J.1,2, Hemmrich-Stanisak G.3, Du Z.3, Hübenthal M.3, Kiudelis G.1,2, Jonaitis L.1,2, Franke A.3, Kupcinskas L.1,2

1Lithuanian University of Heath Sciences, Institute for Digestive Research, Kaunas, Lithuania 2Lithuanian University of Heath Sciences, Department of Gastroenterology, Kaunas, Lithuania 3Christian-Albrechts-University Kiel, Institute for Clinical Molecular Biology, Kiel, Germany

Background

MicroRNAs (miRNAs) are highly tissue-specific, small non-coding RNAs that post-transcriptionally regulate gene expression. These molecules are strongly implicated in the pathogenesis of various immune-related diseases, including ulcerative colitis (UC). Recent studies identified numerous frequently deregulated miRNAs in UC, but there is a lack of information on miRNAs which are deregulated in different forms of the disease's severity. To get further insight into the pathogenesis of UC, the aim of this study was to examine miRNA profiles in active and inactive forms of UC.

Methods

In the discovery phase, small RNA transcriptomes of 76 individuals (HC =32, UCa =23, UCi =21) were sequenced using Illumina HiSeq 2500 NGS platform. Small RNA-seq data pre-processing and quantification were performed using miRDeep2 package (reference database miRBase v20). Normalization, quality control, statistical analysis, and assessment of miRNA differential expression were performed using DESeq2 package. Validation of the most deferentially expressed miRNAs was determined in the independent cohort of 122 individuals (HC =38, UCa =38, UCi =36) using Custom TaqMan® Low Density Array (TLDA). The TLDA expression data was normalized using the ΔΔCT method to the expression values of U6 snRNA, statistical analysis was performed by using HTqPCR package. In order to identify the overall similarity structure of the miRNA expression profiles, a multidimensional scaling (MDS) analysis using Spearman's correlation distance (1-correlation coefficient) was performed.

Results

The comparative analysis of small RNA-seq data identified 108 differentially expressed miRNAs between active UC and normal controls. In contrast, in inactive UC vs. normal controls, 31 miRNAs were found to be differentially expressed. Comparison of the miRNA expression profiles between active UC and inactive UC identified 74 differentially expressed miRNAs. To further validate the findings of small RNA-seq data, 22 highly differentially expressed miRNAs were selected for TLDA analysis in the independent cohort. The expression levels of 11 miRNAs showed significant differential expression in the same direction as in the sequencing data. The MDS analysis either on small RNA-seq or TLDA data revealed two clearly resolved clusters corresponding to active UC and healthy controls and one intermediate cluster corresponding to the inactive UC.

Conclusion

The expression profiles of miRNAs differ among active UC, inactive UC and healthy controls. The patients with inactive UC have an intermediate miRNA expression profile that has similarities to both healthy and active UC-affected individuals.