Erythrocyte microRNA sequencing reveals differential expression in relapsing-remitting multiple sclerosis
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".