Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein–RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP–lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein–lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.

The RNA-binding protein FUS participates in several RNA biosynthetic processes and has been linked to the pathogenesis of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. Here we report that FUS controls back-splicing reactions leading to circular RNA (circRNA) production. We identified circRNAs expressed in in vitro-derived mouse motor neurons (MNs) and determined that the production of a considerable number of these circRNAs is regulated by FUS. Using RNAi and overexpression of wild-type and ALS-associated FUS mutants, we directly correlate the modulation of circRNA biogenesis with alteration of FUS nuclear levels and with putative toxic gain of function activities. We also demonstrate that FUS regulates circRNA biogenesis by binding the introns flanking the back-splicing junctions and that this control can be reproduced with artificial constructs. Most circRNAs are conserved in humans and specific ones are deregulated in human-induced pluripotent stem cell-derived MNs carrying the FUSP525L mutation associated with ALS.

Highlights d Specific m 6 As control accumulation of a subset of circRNAs d METTL3 and YTHDC1 are required to direct the biogenesis of a subset of circRNAs d circRNAs affected by METTL3 YTHDC1 display specific m 6 A signatures d circ-ZNF609 translation is modulated through recognition by YTHDF3 and eIF4G2

SummaryThe FUS gene has been linked to amyotrophic lateral sclerosis (ALS). FUS is a ubiquitous RNA-binding protein, and the mechanisms leading to selective motoneuron loss downstream of ALS-linked mutations are largely unknown. We report the transcriptome analysis of human purified motoneurons, obtained from FUS wild-type or mutant isogenic induced pluripotent stem cells (iPSCs). Gene ontology analysis of differentially expressed genes identified significant enrichment of pathways previously associated to sporadic ALS and other neurological diseases. Several microRNAs (miRNAs) were also deregulated in FUS mutant motoneurons, including miR-375, involved in motoneuron survival. We report that relevant targets of miR-375, including the neural RNA-binding protein ELAVL4 and apoptotic factors, are aberrantly increased in FUS mutant motoneurons. Characterization of transcriptome changes in the cell type primarily affected by the disease contributes to the definition of the pathogenic mechanisms of FUS-linked ALS.

Prediction of protein–RNA interactions is important to understand post-transcriptional events taking place in the cell. Here we introduce catRAPID omics v2.0, an update of our web server dedicated to the computation of protein–RNA interaction propensities at the transcriptome- and RNA-binding proteome-level in 8 model organisms. The server accepts multiple input protein or RNA sequences and computes their catRAPID interaction scores on updated precompiled libraries. Additionally, it is now possible to predict the interactions between a custom protein set and a custom RNA set. Considerable effort has been put into the generation of a new database of RNA-binding motifs that are searched within the predicted RNA targets of proteins. In this update, the sequence fragmentation scheme of the catRAPID fragment module has been included, which allows the server to handle long linear RNAs and to analyse circular RNAs. For the top-scoring protein–RNA pairs, the web server shows the predicted binding sites in both protein and RNA sequences and reports whether the predicted interactions are conserved in orthologous protein–RNA pairs. The catRAPID omics v2.0 web server is a powerful tool for the characterization and classification of RNA-protein interactions and is freely available at http://service.tartaglialab.com/page/catrapid_omics2_group along with documentation and tutorial.