ArticleFrontiers in genetics2026
Bioinformatic prediction, qCLASH, and AlphaFold3 reveal non-overlapping layers of the miR-214-3p-mRNA interactome.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: MicroRNAs (miRNAs) regulate gene expression post-transcriptionally through sequence-specific targeting of messenger RNAs. Reliable mapping of miRNA-mRNA interactions remains challenging because available methods differ widely and systematic cross-method comparisons are lacking. Method: Using mouse miR-214-3p as a model, we systematically compared three approaches: bioinformatic prediction (TargetScan, miRDB, miRWalk), Argonaute-dependent experimental detection (qCLASH on mouse lung tissue), and artificial intelligence-driven structural modeling (AlphaFold3). Gene lists were harmonized to Ensembl release 116 (GRCm39) Gene IDs; overlaps were evaluated against a transcriptome background using Fisher's exact tests with Benjamini-Hochberg correction, and the three-tool consensus was assessed by 1,000,000 Monte Carlo simulations. Results: Only 95 target genes were commonly predicted by all three bioinformatic tools, and at most 6 of the 15 Ensembl-mappable qCLASH genes were captured by any prediction algorithm. Nevertheless, all observed overlaps were significantly enriched relative to random expectation (O/E ratios 24.52-80.6; adjusted q < 0.05), and the 95-gene consensus fell far outside the simulated null distribution (P ∼ MC∼ < 10 Discussion: Bioinformatic prediction, qCLASH, and AlphaFold3 thus capture distinct and complementary layers of miRNA targeting (sequence-defined potential, context-dependent occupancy, and spatial feasibility) rather than converging on a single landscape. High AlphaFold3 confidence scores, for instance, did not guarantee biologically functional conformations; even models meeting the thresholds often displayed non-canonical architectures incompatible with silencing. In our mouse lung miR-214-3p model system, our findings support an integrative approach: method selection should align with the specific research question, and multi-layer integration is key to constructing reliable miRNA-target maps in a given biological context.
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