Back

Small Extracellular Vesicles Secreted by Region-specific Astrocytes Ameliorate the Mitochondrial Function in a Cellular Model of Parkinson's Disease

Leggio, L.; L'Episcopo, F.; Magri', A.; Ulloa-Navas, M. J.; Paterno', G.; Vivarelli, S.; Bastos, C. A. P.; Tirolo, C.; Testa, N.; Caniglia, S.; Risiglione, P.; Pappalardo, F.; Faria, N.; Peruzzotti-Jametti, L.; Pluchino, S.; Garcia-Verdugo, J. M.; Messina, A.; Marchetti, B.; Iraci, N.

2021-04-23 neuroscience
10.1101/2021.04.23.441135 bioRxiv
Show abstract

Extracellular vesicles (EVs) are emerging as powerful players in cell-to-cell communication both in health and diseased brain. In Parkinsons disease (PD) - characterized by selective dopaminergic (DAergic) neuron death in ventral midbrain (VMB) and degeneration of DAergic terminals in striatum (STR) - astrocytes (AS) exert dual harmful/protective functions. When activated by chemokine CCL3, AS promote a robust DAergic neuroprotection both in cellular and pre-clinical models of PD, with mechanisms not fully elucidated. Here we used a combination of techniques to characterize AS-EVs derived from VMB and STR, and investigated their potential to exert neuroprotection. First, we show that: (i) AS of both regions secrete small EVs of ~100 nm; (ii) VMB-AS release more EVs per cell than STR-AS under basal conditions; and (iii) only VMB-AS respond to CCL3 by producing more EVs, suggesting differential AS-EV secretion rate according to PD brain region. Next, addressing AS-EV potential against oxidative stress and mitochondrial toxicity, we found that AS-EVs, especially CCL3-AS-EVs, fully counteract H2O2-induced caspase-3 activation. Furthermore, using high resolution respirometry, we demonstrated that AS-EVs rescue the neuronal mitochondrial complex I function impaired by MPP+, with VMB-AS-EVs fully restoring ATP production in MPP+-injured neurons, highlighting a regional diversity of AS-EVs with neuroprotective implications for PD.

Matching journals

The top 7 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.