MicroRNA chemical modifications in post-transcriptional gene silencing and human diseases
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small, non-coding RNAs that influence various cellular activities through post-transcriptional gene silencing. Recent research has shown that miRNA modifications, including N6-methyladenosine (m6A), 5-methylcytidine (m5C), 2'-O-methylation (Nm), N7-methylguanosine (m7G), pseudouridylation (Ψ), phosphorylation, RNA editing (adenosine to inosine [A to I]), acetylation, and oxidation, play crucial roles in fine-tuning miRNA expression and function. This review examines the impact of nucleotide modifications on miRNA biogenesis, particularly their role in regulating RNA interactions with the Drosha-DiGeorge syndrome critical region 8 (DGCR8) and Dicer complexes, thereby influencing primary miRNA (pri-miRNA) processing, pre-miRNA export, and miRNA maturation. It also examines whether these modifications assist miRNA recognition by RNA-binding proteins (RBPs) in controlling miRNA processing and stability, as well as their impact on miRNA strand selection, target recognition, and the recruitment of regulatory proteins to the miRNA-induced silencing complex (miRISC), which facilitates the silencing of miRNA-targeted messenger RNAs (mRNAs). Additionally, the review discusses the role of miRNA modifications in various human diseases and considers how advanced sequencing technologies and chemical biology approaches enable detailed mapping of these modifications. Furthermore, it provides new insights into the challenges of understanding the dynamic nature of miRNA modifications and their context-dependent effects. It also highlights future directions, including innovative detection methods and epigenetic crosstalk with potential therapeutic applications in human diseases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".