4.3 Article

Microbiota stratification identifies disease-specific alterations in neuro-Behcet's disease and multiple sclerosis

期刊

CLINICAL AND EXPERIMENTAL RHEUMATOLOGY
卷 37, 期 6, 页码 58-66

出版社

CLINICAL & EXPER RHEUMATOLOGY

关键词

Behcet's disease; multiple sclerosis; gut microbiota; 16S ribosomal RNA; dysbiosis

资金

  1. Research Fund of University of Istanbul [31110]
  2. National Institute of Diabetes and Digestive and Kidney Diseases [UH3DK083990]
  3. National Institute of Allergy and Infectious Diseases [U011AI24290, R01AI10091401]
  4. NIH [P30 DK56338]
  5. Texas Children's Hospital PSO Research Support for Texas Children's Microbiome Center

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Objective. Altered gut microbiota community dynamics are implicated in diverse human diseases including inflammatory disorders such as neuro-Behcet's disease (NBD) and multiple sclerosis (MS). Traditionally, microbiota communities are analysed uniformly across control and disease groups, but recent reports of subsample clustering indicate a potential need for analytical stratification. The objectives of this study are to analyse and compare faecal microbiota community signatures of ethno-geographical, age and gender matched adult healthy controls (HC), MS and NBD individuals. Methods. Faecal microbiota community compositions in adult HC (n=14), NBD patients (n=13) and MS (n=13) were analysed by 16S rRNA gene sequencing and standard bioinformatics pipelines. Bipartite networks were then used to identify and re-analyse dominant compositional clusters in respective groups. Results. We identified Prevotella and Bacteroides dominated subsample clusters in HC, MS, and NBD cohorts. Our study confirmed previous reports that Prevotella is a major dysbiotic target in these diseases. We demonstrate that subsample stratification is required to identify significant disease-associated microbiota community shifts with increased Clostridiales evident in Prevotella-stratified NBD and Bacteroides-stratified MS patients. Conclusion. Patient cohort stratification may be needed to facilitate identification of common microbiota community shifts for causation testing in disease.

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