MicroRNA-21 and MicroRNA-148a Contribute to DNA Hypomethylation in Lupus CD4+ T Cells by Directly and Indirectly Targeting DNA Methyltransferase 1
Bibliographic record
Abstract
Systemic lupus erythematosus is a complex autoimmune disease caused by genetic and epigenetic alterations. DNA methylation abnormalities play an important role in systemic lupus erythematosus disease processes. MicroRNAs (miRNAs) have been implicated as fine-tuning regulators controlling diverse biological processes at the level of posttranscriptional repression. Dysregulation of miRNAs has been described in various disease states, including human lupus. Whereas previous studies have shown miRNAs can regulate DNA methylation by targeting the DNA methylation machinery, the role of miRNAs in aberrant CD4+ T cell DNA hypomethylation of lupus is unclear. In this study, by using high-throughput microRNA profiling, we identified that two miRNAs (miR-21 and miR-148a) overexpressed in CD4+ T cells from both patients with lupus and lupus-prone MRL/lpr mice, which promote cell hypomethylation by repressing DNA methyltransferase 1 (DNMT1) expression. This in turn leads to the overexpression of autoimmune-associated methylation-sensitive genes, such as CD70 and LFA-1, via promoter demethylation. Further experiments revealed that miR-21 indirectly downregulated DNMT1 expression by targeting an important autoimmune gene, RASGRP1, which mediated the Ras-MAPK pathway upstream of DNMT1; miR-148a directly downregulated DNMT1 expression by targeting the protein coding region of its transcript. Additionally, inhibition of miR-21 and miR-148a expression in CD4+ T cells from patients with lupus could increase DNMT1 expression and attenuate DNA hypomethylation. Together, our data demonstrated a critical functional link between miRNAs and the aberrant DNA hypomethylation in lupus CD4+ T cells and could help to develop new therapeutic approaches.
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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 | 0.000 |
| 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 | 0.001 |
| 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".