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Martone, J.

Publications and source records attributed to Martone, J..

2 recordsLinked to original sources

Neuronal late endosomes serve as selective RNA hubs disrupted by ALS-linked FUS mutation

Neurons depend on tightly regulated positioning of cellular components to maintain long-distance signaling, but the mechanisms guiding specific mRNAs to distant regions remain unclear. Here we show that late endosomes function as selective RNA carriers in human motor neurons and uncover the molecular logic guiding their loading. Using APEX2-mediated proximity labeling, we identify the external transcriptome of RAB7A-positive endosomes and find a specific population of mRNAs enriched for endosomal, axonal and synaptic functions. We find that mRNAs enriched in RAB7A endosomes contain evolutionarily conserved 5'UTRs which act as localization signals, and that the RNA-binding protein GEMIN5 interacts with these regions to promote endosomal RNA recruitment. Finally, we show that in ALS-associated conditions there is a conspicuous loss of endosome-associated transcripts and the mislocalization of GEMIN5 from endosomes. These findings uncover fundamental principles of RNA compartmentalization and highlight endosomal mRNA loading as a vulnerable axis in neuronal homeostasis.

molecular biology↗

Decoding RNA-RNA Interactions: The Role of Low-Complexity Repeats and a Deep Learning Framework for Sequence-Based Prediction

RNA-RNA interactions (RRIs) are fundamental to gene regulation and RNA processing, yet their molecular determinants remain unclear. In this work, we analyze several large-scale RRI datasets and identify low-complexity repeats (LCRs), including simple tandem repeats, as key drivers of RRIs. Our findings reveal that LCRs enable thermodynamically stable interactions with multiple partners, positioning them as key hubs in RNA-RNA interaction networks. These RRIs appear to be important for several aspects of RNA metabolism. Sequencing-based analysis of the lncRNA Lhx1os interactors validates the importance of LCRs in shaping contacts potentially involved in neuronal development. Recognizing the pivotal role of sequence determinants, we develop RIME, a deep learning model that predicts RRIs by leveraging embeddings from a nucleic acid language model. RIME outperforms traditional thermodynamics-based tools, successfully captures the role of LCRs and prioritizes high-confidence interactions, including those established by lncRNAs. RIME is freely available at https://tools.tartaglialab.com/rna_rna.

biochemistry↗