Minocycline Promotes Neurite Outgrowth of PC12 Cells Exposed to Oxygen-Glucose Deprivation and Reoxygenation Through Regulation of MLCP/MLC Signaling Pathways.

Minocycline, a semi-synthetic second-generation derivative of tetracycline, has been reported to exert neuroprotective effects both in animal models and in clinic trials of neurological diseases. In the present study, we first investigated the protective effects of minocycline on oxygen-glucose deprivation and reoxygenation-induced impairment of neurite outgrowth and its potential mechanism in the neuronal cell line, PC12 cells. We found that minocycline significantly increased cell viability, promoted neurite outgrowth and enhanced the expression of growth-associated protein-43 (GAP-43) in PC12 cells exposed to oxygen-glucose deprivation/reoxygenation injury. In addition, immunoblots revealed that minocycline reversed the overexpression of phosphorylated myosin light chain (MLC) and the suppression of activated extracellular signal-regulated kinase 1/2 (ERK1/2) caused by oxygen-glucose deprivation/reoxygenation injury. Moreover, the minocycline-induced neurite outgrowth was significantly blocked by Calyculin A (1 nM), an inhibitor of myosin light chain phosphatase (MLCP), but not by an ERK1/2 inhibitor (U0126; 10 µM). These findings suggested that minocycline activated the MLCP/MLC signaling pathway in PC12 cells after oxygen-glucose deprivation/reoxygenation injury, which resulted in the promotion of neurite outgrowth.

Caffeine Inhibits Activation of the NLRP3 Inflammasome via Autophagy to Attenuate Microglia-Mediated Neuroinflammation in Experimental Autoimmune Encephalomyelitis.

The activation of microglia is an important cause of central nervous system (CNS) inflammatory cell infiltration and inflammatory demyelination in experimental autoimmune encephalomyelitis (EAE). Furthermore, the proinflammatory response induced by the NLR family pyrin domain containing 3 (NLRP3) inflammasome can be amplified in microglia after NLRP3 inflammasome activation. Autophagy is closely related to the inflammatory response. Caffeine exerts anti-inflammatory and autophagy-stimulating effects, but the specific mechanism remains unclear. This study examined the mechanism underlying the anti-inflammatory effect of caffeine on EAE. In this study, C57BL/6 mice were immunized to induce EAE and treated with caffeine to observe its effect on prognosis. The effects of caffeine on autophagy and inflammation were also analysed in mouse primary microglia (PM) and the BV2 cell line. The data demonstrated that caffeine reduced the clinical score, the infiltration of inflammatory cells, the demyelination level, and the activation of microglia in EAE mice. Furthermore, caffeine increased the LC3-II/LC3-I levels and decreased the NLRP3 and P62 levels in EAE mice, whereas the autophagy inhibitor 3-methylamine (3-MA) blocked these effects. In vitro, caffeine promoted autophagy by suppressing the mechanistic target of rapamycin (mTOR) pathway and inhibited activation of the NLRP3 inflammasome. However, autophagy-related gene 5 (ATG5)-specific siRNA abolished the anti-inflammatory effect of caffeine treatment in PM and BV2 cells. Taken together, these data suggest that caffeine exerts a newly discovered effect on EAE by reducing NLRP3 inflammasome activation via the induction of autophagy in microglia.

MBNL1 reverses the proliferation defect of skeletal muscle satellite cells in myotonic dystrophy type 1 by inhibiting autophagy via the mTOR pathway.

Skeletal muscle atrophy is one of the clinical symptoms of myotonic dystrophy type 1 (DM1). A decline in skeletal muscle regeneration is an important contributor to muscle atrophy. Skeletal muscle satellite cells (SSCs) drive skeletal muscle regeneration. Increased autophagy can reduce the proliferative capacity of SSCs, which plays an important role in the early regeneration of damaged skeletal muscle in DM1. Discovering new ways to restore SSC proliferation may aid in the identification of new therapeutic targets for the treatment of skeletal muscle atrophy in DM1. In the pathogenesis of DM1, muscleblind-like 1 (MBNL1) protein is generally considered to form nuclear RNA foci and disturb the RNA-splicing function. However, the role of MBNL1 in SSC proliferation in DM1 has not been reported. In this study, we obtained SSCs differentiated from normal DM1-04-induced pluripotent stem cells (iPSCs), DM1-03 iPSCs, and DM1-13-3 iPSCs edited by transcription activator-like (TAL) effector nucleases (TALENs) targeting CTG repeats, and primary SSCs to study the pathogenesis of DM1. DM1 SSC lines and primary SSCs showed decreased MBNL1 expression and elevated autophagy levels. However, DM1 SSCs edited by TALENs showed increased cytoplasmic distribution of MBNL1, reduced levels of autophagy, increased levels of phosphorylated mammalian target of rapamycin (mTOR), and improved proliferation rates. In addition, we confirmed that after MBNL1 overexpression, the proliferative capability of DM1 SSCs and the level of phosphorylated mTOR were enhanced, while the autophagy levels were decreased. Our data also demonstrated that the proliferative capability of DM1 SSCs was enhanced after autophagy was inhibited by overexpressing mTOR. Finally, treatment with rapamycin (an mTOR inhibitor) was shown to abolish the increased proliferation capability of DM1 SSCs due to MBNL1 overexpression. Taken together, these data suggest that MBNL1 reverses the proliferation defect of SSCs in DM1 by inhibiting autophagy via the mTOR pathway.

Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries.

PURPOSE: To compare twenty-two machine learning (ML) models against logistic regression on survival prediction in severe traumatic brain injury (STBI) patients in a single center study. MATERIALS AND METHODS: Data was collected from STBI patients admitted to the Sichuan Provincial People's Hospital between December 2009 and November 2011. Twenty-two machine learning (ML) models were tested, and their predictive performance compared with logistic regression (LR) model. Receiver operating characteristics (ROC), area under curve (AUC), accuracy, F-score, precision, recall and Decision Curve Analysis (DCA) were used as performance metrics. RESULTS: A total of 117 patients were enrolled. AUC of all ML models ranged from 86.3% to 94%. AUC of LR was 83%, and accuracy was 88%. The AUC of Cubic SVM, Quadratic SVM and Linear SVM were higher than that of LR. The precision ratio of LR was 95% and recall ratio was 91%, both were lower than most ML models. The F-Score of LR was 0.93, which was only slightly better than that of Linear Discriminant and Quadratic Discriminant. CONCLUSIONS: The twenty-two ML models selected have capabilities comparable to classical LR model for outcome prediction in STBI patients. Of these, Cubic SVM, Quadratic SVM, Linear SVM performed significantly better than LR.

Optimization of brain metabolism using metabolic-targeted therapeutic hypothermia can reduce mortality from traumatic brain injury.

BACKGROUND: Therapeutic hypothermia is widely used to treat traumatic brain injuries (TBIs). However, determining the best hypothermia therapy strategy remains a challenge. We hypothesized that reducing the metabolic rate, rather than reaching a fixed body temperature, would be an appropriate target because optimizing metabolic conditions especially the brain metabolic environment may enhance neurologic protection. A pilot single-blind randomized controlled trial was designed to test this hypothesis, and a nested metabolomics study was conducted to explore the mechanics thereof. METHODS: Severe TBI patients (Glasgow Coma Scale score, 3-8) were randomly divided into the metabolic-targeted hypothermia treatment (MTHT) group, 50% to 60% rest metabolic ratio as the hypothermia therapy target, and the body temperature-targeted hypothermia treatment (BTHT) control group, hypothermia therapy target of 32°C to 35°C body temperature. Brain and circulatory metabolic pool blood samples were collected at baseline and on days 1, 3, and 7 during the hypothermia treatment, which were selected randomly from a subgroup of MTHT and BTHT groups. The primary outcome was mortality. Using H nuclear magnetic resonance technology, we tracked and located the disturbances of metabolic networks. RESULTS: Eighty-eight severe TBI patients were recruited and analyzed from December 2013 to December 2014, 44 each were assigned in the MTHT and BTHT groups (median age, 42 years; 69.32% men; mean Glasgow Coma Scale score, 6.17 ± 1.02). The mortality was significantly lower in the MTHT than the BTHT group (15.91% vs. 34.09%; p = 0.049). From these, eight cases of MTHT and six cases from BTHT group were enrolled for metabolomics analysis, which showed a significant difference between the brain and circulatory metabolic patterns in MTHT group on day 7 based on the model parameters and scores plots. Finally, metabolites representing potential neuroprotective monitoring parameters for hypothermia treatment were identified through H nuclear magnetic resonance metabolomics. CONCLUSION: MTHT can significantly reduce the mortality of severe TBI patients. Metabolomics research showed that this strategy could effectively improve brain metabolism, suggesting that reducing the metabolic rate to 50% to 60% should be set as the hypothermia therapy target. LEVEL OF EVIDENCE: Therapeutic study, Level I.