Back

Pharmacological tuning of microRNA in FAP-derived Extracellular Vesicles by HDAC inhibitors promotes regeneration and reduces fibrosis in dystrophic muscles.

Sandona, M.; Consalvi, S.; Tucciarone, L.; De Bardi, M.; Scimeca, M.; Angelini, D. F.; Buffa, V.; D'Amico, A.; Bertini, E. S.; Cazzaniga, S.; Bettica, P.; Bouche, M.; Bongiovanni, A.; Puri, P. L.; Saccone, V.

2020-02-15 cell biology
10.1101/2020.02.15.947325 bioRxiv
Show abstract

Functional interactions between cellular components of the muscle stem cell (MuSC) niche regulate the regenerative ability of skeletal muscles in physiological and pathological conditions; however, the identity of the mediators of these interactions remains largely unknown. We show here that fibro-adipogenic progenitor (FAP)-derived Extracellular Vesicles (EVs) mediate microRNA transfer to MuSCs, and that exposure of dystrophic FAPs to HDAC inhibitors (HDACi) increases the intra-EV levels of a subset of microRNAs (miRs), which cooperatively target biological processes of therapeutic interest, including regeneration, fibrosis and inflammation. In particular, we found that increased levels of miR206 in EVs released from FAPs of muscles from Duchenne dystrophic patients or mice (mdx) exposed to HDACi were associated with enhanced regeneration and inhibition of fibrosis of dystrophic muscles. Consistently, EVs from HDACi-treated dystrophic FAPs could stimulated MuSC activation and expansion ex vivo, and promoted regeneration, while inhibiting fibrosis and inflammation of dystrophic muscles, upon intramuscular transplantation, in vivo. These data reveal a potential for pharmacological modulation of FAP-derived EVs content as novel strategy for focal therapeutic interventions in Duchenne Muscular Dystrophy (DMD) and possibly other muscular diseases. Brief SummaryExtracellular Vesicles from HDACi-treated dystrophic FAPs promote regeneration, while inhibiting fibrosis and inflammation of dystrophic muscles

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.