Mounting evidence has suggested that modulating microglia polarization from pro-inflammatory M1 phenotype to anti-inflammatory M2 state might be a potential therapeutic approach in the treatment of subarachnoid hemorrhage (SAH) injury. Our previous study has indicated that sirtuin 1 (SIRT1) could ameliorate early brain injury (EBI) in SAH by reducing oxidative damage and neuroinflammation. However, the effects of SIRT1 on microglial polarization and the underlying molecular mechanisms after SAH have not been fully illustrated. In the present study, we first observed that EX527, a potent selective SIRT1 inhibitor, enhanced microglial M1 polarization and nod-like receptor pyrin domain-containing 3 (NLRP3) inflammasome activation in microglia after SAH. Administration of SRT1720, an agonist of SIRT1, significantly enhanced SIRT1 expression, improved functional recovery, and ameliorated brain edema and neuronal death after SAH. Moreover, SRT1720 modulated the microglia polarization shift from the M1 phenotype and skewed toward the M2 phenotype. Additionally, SRT1720 significantly decreased acetylation of forkhead box protein O1, inhibited the overproduction of reactive oxygen species (ROS) and suppressed NLRP3 inflammasome signaling. In contrast, EX527 abated the upregulation of SIRT1 and reversed the inhibitory effects of SRT1720 on ROS-NLRP3 inflammasome activation and EBI. Similarly, in vitro, SRT1720 suppressed inflammatory response, oxidative damage, and neuronal degeneration, and improved cell viability in neurons and microglia co-culture system. These effects were associated with the suppression of ROS-NLRP3 inflammasome and stimulation of SIRT1 signaling, which could be abated by EX527. Altogether, these findings indicate that SRT1720, an SIRT1 agonist, can ameliorate EBI after SAH by shifting the microglial phenotype toward M2 via modulation of ROS-mediated NLRP3 inflammasome signaling.
Keywords: NLRP3; early brain injury; microglia polarization; sirtuin 1; subarachnoid hemorrhage.
Copyright © 2021 Xia, Yuan, Jiang, Qi, Lai, Wu and Zhang.