期刊
FRONTIERS IN MICROBIOLOGY
卷 11, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2020.517797
关键词
epilepsy; human; fecal microbiota; 16S RNA sequence; ROC
类别
资金
- National Science Foundation of China [81671291, 81420108014, 81971213]
- National Key R&D Program of China [2018YFC1312300]
Objective To explore the structure and composition of the fecal microbiota of patients with epilepsy. Methods Variations in the fecal microbiota between patients with epilepsy and healthy controls (HCs) from the same household were investigated and validated by utilizing 16S ribosomal RNA sequencing in two independent cohorts [exploration cohort (N= 55 patients andN= 46 HCs) and validation cohort (N= 13 patients andN= 10 HCs)]. Results The alpha diversity indexes of the specimens from patients with epilepsy were much lower than those from the HCs (p< 0.05). The structure and composition of the fecal microbiota differed between patients with different clinical prognoses and between patients and HCs (Adonis:p< 0.05). Microbiome alterations in patients with epilepsy included increases inActinobacteriaandVerrucomicrobiaand decreases inProteobacteriaat the phylum level and increases inPrevotella_9,Blautia,Bifidobacterium, and others at the genus level [linear discriminant analysis (LDA): 3.5] Patients with drug-resistant epilepsy showed enrichment of bacterial taxa inActinobacteria,Verrucomicrobia, andNitrospiraeand the generaBlautia,Bifidobacterium,Subdoligranulum,Dialister, andAnaerostipes(Kruskal-Wallis test:p< 0.05). Analysis of gut microbiome indicated predictive ability for disease diagnosis, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.97 (95% CI, 0.84-0.98). Applying the model to our validation cohort resulted in an AUC of 0.96 (95% CI, 0.75-0.97). Notably, the model could distinguish drug-resistant from drug-sensitive epilepsy (AUC = 0.85, 95% CI: 0.69-0.94). Conclusion Patients with epilepsy exhibit substantial alterations of fecal microbiota composition, and specific gut commensal strains are altered depending on different clinical phenotypes and thus could serve as potential biomarkers for disease diagnosis.
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