ReviewBiochemistry and biophysics reports2026
RNA-protein complexes as key regulators of plant secondary metabolism: Biophysical interactions and functional significance.
Review in Biochemistry and biophysics reports, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
RNA-protein complexes (RNPs) are central regulators of post-transcriptional gene expression and are increasingly implicated in controlling when, where and to what extent specialised (secondary) metabolic pathways operate in plants. Relative to transcription factor-centred regulation, RNPs can modify pathway output rapidly by modulating mRNA processing, stability, localisation and translation, thereby enabling plants to adjust metabolite production on developmentally and environmentally relevant timescales. Many regulatory RNPs assemble into membraneless biomolecular condensates (e.g., stress granules and processing bodies) via liquid-liquid phase separation (LLPS), providing reversible mechanisms to sequester, protect or degrade transcripts, including those encoding enzymes and regulators of specialised metabolism. In addition, small RNA-guided silencing complexes (e.g., AGO-containing RISC) form well-defined RNP modules that post-transcriptionally regulate transcription factors and biosynthetic genes involved in phenylpropanoid/flavonoid and other pathways. Here, we synthesise current evidence linking RNP biology to specialised metabolism, explicitly distinguishing mechanistic evidence (e.g., direct binding with functional perturbation) from associative evidence (e.g., co-localisation or proteomic enrichment). We also summarise experimental and computational approaches for mapping plant RNA-protein interactions, including key limitations (crosslinking bias, antibody specificity and contamination) and emerging tools (enhanced RNA interactome capture, proximity labelling and deep-learning-assisted binding prediction). Finally, we highlight evidence gaps in non-model medicinal plants and propose a framework that integrates spatiotemporal transcriptomics, structural biology and RNP-centred engineering to improve plant biofactories and stress-resilient phytochemical production.
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