Erythrocyte microRNA sequencing reveals differential expression in relapsing-remitting multiple sclerosis

Abstract

Background: There is a paucity of knowledge concerning erythrocytes in the aetiology of Multiple Sclerosis (MS) despite their potential to contribute to disease through impaired antioxidant capacity and altered haemorheological features. Several studies have identified an abundance of erythrocyte miRNAs and variable profiles associated with disease states, such as sickle cell disease and malaria. The aim of this study was to compare the erythrocyte miRNA profile of relapsing-remitting MS (RRMS) patients to healthy sex- and age-matched controls.

Methods: Erythrocytes were purified by density-gradient centrifugation and RNA was extracted. Following library preparation, samples were run on a HiSeq4000 Illumina instrument (paired-end 100 bp sequencing). Sequenced erythrocyte miRNA profiles (9 patients and 9 controls) were analysed by DESeq2. Differentially expressed miRNAs were validated by RT-qPCR using miR-152-3p as an endogenous control and replicated in a larger cohort (20 patients and 18 controls). After logarithmic transformation, differential expression was determined by two-tailed unpaired t-tests. Logistic regression analysis was carried out and receiver operating characteristic (ROC) curves were generated to determine biomarker potential.

Results: A total of 236 erythrocyte miRNAs were identified. Of twelve differentially expressed miRNAs in RRMS two showed increased expression (adj. p < 0.05). Only modest fold-changes were evident across differentially expressed miRNAs. RT-qPCR confirmed differential expression of miR-30b-5p (0.61 fold, p < 0.05) and miR-3200-3p (0.36 fold, p < 0.01) in RRMS compared to healthy controls. Relative expression of miR-3200-5p (0.66 fold, NS p = 0.096) also approached significance. MiR-3200-5p was positively correlated with cognition measured by audio-recorded cognitive screen (r = 0.60; p < 0.01). MiR-3200-3p showed greatest biomarker potential as a single miRNA (accuracy = 75.5%, p < 0.01, sensitivity = 72.7%, specificity = 84.0%). Combining miR-3200-3p, miR-3200-5p, and miR-30b-5p into a composite biomarker increased accuracy to 83.0% (p < 0.05), sensitivity to 77.3%, and specificity to 88.0%.

Conclusions: This is the first study to report differences in erythrocyte miRNAs in RRMS. While the role of miRNAs in erythrocytes remains to be elucidated, differential expression of erythrocyte miRNAs may be exploited as biomarkers and their potential contribution to MS pathology and cognition should be further investigated.

Keywords: Erythrocytes; Next-generation sequencing; Relapsing-remitting multiple sclerosis; microRNA.

Fig. 1

Violin plots of differentially expressed erythrocyte microRNAs identified by next-generation sequencing. The violin plots show normalised gene counts of erythrocyte miRNAs that were found to be differentially expressed in RRMS patients (n = 9) compared to HCs (n = 9). Sequences were aligned against miRBase 21 and differential expression was computed with DESeq2

Fig. 2

Log10(fold changes) of differentially expressed erythrocyte microRNAs in relapsing-remitting Multiple Sclerosis. Log10(fold changes) of differentially expressed erythrocyte miRNAs in relapsing-remitting Multiple Sclerosis (RRMS) patients compared to healthy controls (HC) by next-generation sequencing (9 RRMS patients and 9 HC) (blue) and RT-qPCR (9 RRMS patients and 5 HC) (green)

Fig. 3

Tukey boxplot of differentially expressed erythrocyte microRNAs confirmed by reverse transcription polymerase chain reaction. Tukey boxplot of relative expression (2^-deltaCT) (y-axis on a log scale) of erythrocyte microRNAs in relapsing-remitting MS patients (n = 20; green) and healthy controls (n = 18; blue). The blue dot represents an outlier defined as deviating ≥1.5 fold from the upper/lower quartile. * p < 0.05; ** p < 0.01; NS – not significant (p > 0.05)

Fig. 4

Receiver operating characteristic (ROC) curves for confirmed erythrocyte microRNAs. ROC curves showing sensitivity and 1-specificity at different thresholds for miR-3200-3p (green), miR-3200-5p (blue), and miR-30b-5p (yellow)

Fig. 5

Linear regression for RE of miR-3200-5p and patients’ ARCS score. Relative expression (RE) of miR-3200-5p was positively correlated (correlation coefficient 0.597; p < 0.01) with patients’ audio-recorded cognitive screen (ARCS) score. Equations for the linear regression model (black line) and 95% confidence intervals (blue lines) are shown