Postoperative cognitive dysfunction (POCD) is a disturbing neurological complication in patients undergoing anesthesia and surgical procedures. Brain-derived neurotrophic factor (BDNF) and its precursor proBDNF binding to their corresponding receptors tyrosine kinase (TrkB) and p75 neurotrophin receptor (p75NTR) exert quite an opposite biological function in neuron survival and synaptic function. This study aimed to demonstrate the critical role of the BDNF/proBDNF ratio in modulating synaptic plasticity, which further leads to anesthesia-/surgery-induced POCD. It also showed that the exogenous BDNF or p75NTR inhibitor could ameliorate cognitive dysfunction. In detail, 16-month-old C57BL/6 mice were subjected to a stabilized tibial fracture surgery with isoflurane anesthesia to establish the POCD animal model. The mice were then microinjected with either p75NTR inhibitor or exogenous BDNF into the dorsal hippocampus. Behavioral experiments were performed by open field and fear conditioning tests (FCTs). Western blotting was also used to measure the expression levels of BDNF, proBDNF, TrkB, p-TrkB, p75NTR, and synapse proteins. Golgi staining and electrophysiology were applied to evaluate the neuronal synaptic plasticity. Here, we demonstrated that anesthesia/surgery induced a reduction of BDNF/proBDNF, which negatively regulates the synaptic function in hippocampus, subsequently leading to cognitive impairment in aged mice. P75NTR inhibitor and exogenous BDNF could attenuate cognitive deficits by rescuing the dendritic spine loss and long-term potentiation (LTP) via altering the BDNF/proBDNF ratio. This study unveiled that the BDNF/proBDNF ratio in the hippocampus played a key role in anesthesia-/surgery-induced POCD. Thereby, tuning the ratio of BDNF/proBDNF is supposed to be a promising therapeutic target for POCD.

Zhang (2021) Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential

BackgroundIschemic cerebral infarction is the most common type of stroke with high rates of mortality, disability, and recurrence. However, the known diagnostic biomarkers and therapeutic targets for ischemic stroke (IS) are limited. In the current study, we aimed to identify novel inflammation-related biomarkers for IS using machine learning analysis and to explore their relationship with the levels of immune-related cells in whole blood samples.MethodsGene expression profiles of healthy controls and patients with IS were download from the Gene Expression Omnibus. Analysis of differentially expressed genes (DEGs) was performed in healthy controls and patients with IS. Single-sample gene set enrichment analysis was performed to calculate inflammation scores, and weighted gene co-expression network analysis was used to analyze genes in significant modules associated with inflammation scores. Key DEGs in significant modules were then analyzed using LASSO regression analysis for constructing a diagnostic model. The effectiveness and specificity of the diagnostic model was verified in healthy controls and patients with IS and with cerebral hemorrhage (CH) using qRT-PCR. The relationship between diagnostic score and the levels of immune-related cells in whole blood were analyzed using Pearson correlations.ResultsA total of 831 DEGs were identified. Both chronic and acute inflammation scores were higher in patients with IS, while 54 DEGs were also clustered in the gene modules associated with chronic and acute inflammation scores. Among them, a total of 9 genes were selected to construct a diagnostic model. Interestingly, RT-qPCR showed that the diagnostic model had better diagnostic value for IS but not for CH. The levels of lymphocytes were lower in blood of patients with IS, while the levels of monocytes and neutrophils were increased. The diagnostic score of the model was negatively associated with the levels of lymphocytes and positively associated with levels of monocytes and neutrophils.ConclusionsTaken together, the diagnostic model constructed using the inflammation-related genes TNFSF10, ID1, PAQR8, OSR2, PDK4, PEX11B, TNIP1, FFAR2, and JUN exhibited high and specific diagnostic value for IS and reflected the condition of lymphocytes, monocytes, and neutrophils in the blood. The diagnostic model may contribute to the diagnosis of IS.