Spastin, a microtubule-severing enzyme, is important for neurite outgrowth. However, the mechanisms underlying the post-transcriptional regulation of spastin during microtubule-related processes are largely unknown. We demonstrated that the spastin expression level is controlled by a long non-coding RNA (lncRNA) metastasisassociated lung adenocarcinoma transcript 1 (MALAT1)/microRNA-30 (miR-30) axis during neurite outgrowth. The miR-30 expression level decreased in hippocampal neurons with increasing days in culture, and miR-30 overexpression suppressed while miR-30 inhibition promoted neurite outgrowth in hippocampal neurons. Spastin was validated as a target gene of miR-30 using the luciferase reporter assay. The protein expression, microtubule severing activity, and neurite promoting effect of spastin were suppressed by the overexpression of miR-30 mimics and increased by miR-30 inhibitors. MALAT1 expression increased during neurite outgrowth and MALAT1 silencing impaired neurite outgrowth. miR-30 was a sponge target of MALAT1 and MALAT1/miR-30 altered neurite outgrowth in hippocampal neurons. MALAT1 overexpression reversed the inhibitory effect of miR-30 on the activity of a luciferase reporter construct containing spastin, as well as spastin mRNA and protein expression, indicating that spastin was a downstream effector of MALAT1/miR-30. The MALAT1/miR-30 cascade also modulated spastin-induced microtubule severing, and the MALAT1/miR-30/spastin axis regulated neurite outgrowth in hippocampal neurons. This study suggests a new mechanism governing neurite outgrowth in hippocampal neurons involving MALAT1/miR-30regulated spastin expression.

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BackgroundIntracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.MethodsICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).ResultsA total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.ConclusionThis study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.