ObjectiveThis study is to investigate the role of gut dysbiosis in triggering inflammation in the brain and its contribution to Alzheimer’s disease (AD) pathogenesis.DesignWe analysed the gut microbiota composition of 3×Tg mice in an age-dependent manner. We generated germ-free 3×Tg mice and recolonisation of germ-free 3×Tg mice with fecal samples from both patients with AD and age-matched healthy donors.ResultsMicrobial 16S rRNA sequencing revealed Bacteroides enrichment. We found a prominent reduction of cerebral amyloid-β plaques and neurofibrillary tangles pathology in germ-free 3×Tg mice as compared with specific-pathogen-free mice. And hippocampal RNAseq showed that inflammatory pathway and insulin/IGF-1 signalling in 3×Tg mice brain are aberrantly altered in the absence of gut microbiota. Poly-unsaturated fatty acid metabolites identified by metabolomic analysis, and their oxidative enzymes were selectively elevated, corresponding with microglia activation and inflammation. AD patients’ gut microbiome exacerbated AD pathologies in 3×Tg mice, associated with C/EBPβ/asparagine endopeptidase pathway activation and cognitive dysfunctions compared with healthy donors’ microbiota transplants.ConclusionsThese findings support that a complex gut microbiome is required for behavioural defects, microglia activation and AD pathologies, the gut microbiome contributes to pathologies in an AD mouse model and that dysbiosis of the human microbiome might be a risk factor for AD.
BackgroundGlioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma.MethodWeighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients.ResultsWe performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21 + MED10 + PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC = 0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n = 156).ConclusionWe developed a promising mRNA signature for estimating overall survival in glioblastoma patients.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0407-1) contains supplementary material, which is available to authorized users.
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