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Related Concept Videos

MicroRNAs01:22

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
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Target-site dynamics explain a large share of apparent microRNA differential expression.

Mert Cihan1, Piyush More1, Maximilian Sprang2,3

  • 1Faculty of Biology, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz 55118, Germany.

RNA (New York, N.Y.)
|April 23, 2026
PubMed
Summary

This study introduces MIRNAPEX, a machine learning tool that accounts for alternative polyadenylation (APA) to accurately measure microRNA (miRNA) regulatory effects. It reveals that changes in target-site availability, not just miRNA levels, impact gene regulation.

Keywords:
alternative polyadenylationgene regulationmicroRNAnoncoding RNAtarget-directed microRNA degradation

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • MicroRNA (miRNA) abundance is typically assessed by measuring their expression levels, overlooking factors like target-site availability.
  • Alternative polyadenylation (APA) significantly alters 3' untranslated regions (3'UTRs), influencing miRNA binding and subsequent gene regulation.
  • Current differential expression analyses for miRNAs often fail to incorporate APA-driven changes in target-site accessibility, potentially leading to inaccurate conclusions.

Purpose of the Study:

  • To develop and validate MIRNAPEX, a novel machine learning framework for quantifying miRNA regulatory effect sizes.
  • To integrate target-gene expression with 3'UTR isoform usage to infer effective miRNA binding-site dosage.
  • To investigate the impact of APA on miRNA regulatory dynamics and apparent miRNA differential expression.

Main Methods:

  • Development of MIRNAPEX, an expression-stratification-based machine learning framework.
  • Integration of RNA-seq data, including target-gene expression and 3'UTR isoform usage.
  • Training machine learning models on pan-cancer datasets to correlate transcriptomic features with miRNA log-fold changes.
  • Application of MIRNAPEX to analyze data from knockdowns of core APA regulators.

Main Results:

  • MIRNAPEX successfully quantified miRNA regulatory effect sizes by considering APA.
  • Widespread 3'UTR shortening was observed upon knockdown of core APA regulators, with MIRNAPEX predicting miRNA-specific shifts.
  • Predicted miRNA shifts were consistent with alterations in APA-associated 3'UTR landscapes of target genes.
  • Analysis of target-directed miRNA degradation (TDMD) revealed that loss of distal decay-trigger sites correlated with increased miRNA abundance due to reduced TDMD-mediated decay.

Conclusions:

  • Apparent miRNA differential expression can arise from dynamic target-site landscapes, not solely from altered miRNA transcription.
  • Neglecting the influence of APA on target-site availability can lead to misestimation of miRNA regulatory effect sizes.
  • MIRNAPEX provides a more comprehensive approach to understanding miRNA regulatory networks by incorporating APA dynamics.