Genome-wide maps of circulating miRNA biomarkers for ulcerative colitis

Abstract

Inflammatory Bowel Disease--comprised of Crohn's Disease and Ulcerative Colitis (UC)--is a complex, multi-factorial inflammatory disorder of the gastrointestinal tract. In this study we have explored the utility of naturally occurring circulating miRNAs as potential blood-based biomarkers for non-invasive prediction of UC incidences. Whole genome maps of circulating miRNAs in micro-vesicles, Peripheral Blood Mononuclear Cells and platelets have been constructed from a cohort of 20 UC patients and 20 normal individuals. Through Significance Analysis of Microarrays, a signature of 31 differentially expressed platelet-derived miRNAs has been identified and biomarker performance estimated through a non-probabilistic binary linear classification using Support Vector Machines. Through this approach, classifier measurements reveal a predictive score of 92.8% accuracy, 96.2% specificity and 89.5% sensitivity in distinguishing UC patients from normal individuals. Additionally, the platelet-derived biomarker signature can be validated at 88% accuracy through qPCR assays, and a majority of the miRNAs in this panel can be demonstrated to sub-stratify into 4 highly correlated intensity based clusters. Analysis of predicted targets of these biomarkers reveal an enrichment of pathways associated with cytoskeleton assembly, transport, membrane permeability and regulation of transcription factors engaged in a variety of regulatory cascades that are consistent with a cell-mediated immune response model of intestinal inflammation. Interestingly, comparison of the miRNA biomarker panel and genetic loci implicated in IBD through genome-wide association studies identifies a physical linkage between hsa-miR-941 and a UC susceptibility loci located on Chr 20. Taken together, analysis of these expression maps outlines a promising catalog of novel platelet-derived miRNA biomarkers of clinical utility and provides insight into the potential biological function of these candidates in disease pathogenesis.

Figure 1. Workflow for biomarker derivation and computation of predictive estimates.

(A) Differentially expressed miRNAs were first derived by SAM and those exhibiting a FDR of <1% and frequency of occurrence >90% in 100 iterations, were selected as biomarkers. (B) The decision rules were obtained by SVM classification and the predictive estimates of the selected biomarkers determined by 10-fold cross-validation.

Figure 2. Characteristics and performance measures of biomarkers derived from Platelets, Micro-vesicles or Platelet/Micro-vesicle combined fraction.

(A) Cumulative distribution frequency (CDF) plots of biomarkers derived from the different fractions. The dotted line represents the 90% cut-off frequency across the study population (B) Counts and overlaps of miRNA biomarkers derived from the platelet, micro-vesicular and combined fraction (C) Performance measures of biomarkers from each fraction selected at the 90% frequency threshold.

Figure 3. Comparison of expression levels of the platelet-derived miRNA biomarkers in the patient and control cohorts.

Unsupervised hierarchical clustering of samples (controls in blue: C1–C20 and patients in red: P2–P21) based on summarized intensity values from the 31 differentially expressed miRNA biomarkers. The log₂ intensity values are shown in the Color Key bar scale.

Figure 4. Validation of expression levels of miRNA biomarkers derived from the platelet fraction.

miRNA expression levels of 7 biomarkers (n = 4, *P values<0.001) were validated by qPCR. The p values are calculated based on a Student's t-test of the replicate 2∧ (−ΔCt) values for each miRNA in the control group (normal individuals) and test groups (patients).

Figure 5. Sub-clusters of platelet-derived miRNA biomarkers.

Unsupervised hierarchical clustering of miRNA biomarkers based on the Pearson correlation coefficients among the miRNA expression profiles from 20 patients and 20 controls. The coefficient values are shown in the bar scale. Each of the 4 main clusters with significant correlation values are indicated in boxes.