Original Article
Variations in gut microbial profiles in ankylosing spondylitis: disease phenotype-related dysbiosis
Abstract
Background: Microbial involvement in ankylosing spondylitis (AS) has been suggested; however, the relationship between gut microbiome and the disease phenotypes of AS remains to be established. This study was to characterize and investigate differences in the gut microbiome between AS patients and healthy controls (HCs), and to determine whether the gut microbiome profile associated with the disease phenotypes.
Methods: 16S rRNA gene V4 region sequencing was performed on fecal DNA isolated from stool samples collected from 41 patients with AS [20 axial AS (axAS) and 21 peripheral AS (pAS)] and 19 HCs. QIIME based pipeline was used to process the raw sequence data. Alpha and beta diversities were assessed using QIIME, and comparisons of gut microbiome profile were performed using linear discriminant analysis (LDA) effect size (LEfSe) to examine differences between groups and subgroups. A gut microbiota-based model for predictive diagnosis of AS was constructed using random forest algorithm and its predictive value was assessed by receiver-operating characteristic analyses.
Results: Our results showed that fecal microbial communities in patients with AS differ significantly from those in HCs, driven by a higher abundance of 7 genera (Prevotella_9, Dialister, Comamonas, Collinsella, Streptococcus, Alloprevotella and Prevotella_2) and a lower abundance of 4 genera (Eubacterium_ruminantium_ group, Ruminococcus_gnavus_group, Lachnospira and Bacteroides). In addition, pAS patients were more enriched in Comamonas, Streptococcus and Collinsella, while axAS patients were more enriched in Prevotella_2. An 8 genera-based model showed high accuracy for distinguishing AS patients from HCs with an area under the curve (AUC) up to 0.950.
Conclusions: Our results revealed specific alterations in the gut microbiome in patients with different phenotypes of AS, and the classification model based on gut microbial features might provide a new direction for future clinical diagnosis. Lastly, discovery of the associated microbes of AS in the gut microbiome may help us to seek more treatments for this disease.
Methods: 16S rRNA gene V4 region sequencing was performed on fecal DNA isolated from stool samples collected from 41 patients with AS [20 axial AS (axAS) and 21 peripheral AS (pAS)] and 19 HCs. QIIME based pipeline was used to process the raw sequence data. Alpha and beta diversities were assessed using QIIME, and comparisons of gut microbiome profile were performed using linear discriminant analysis (LDA) effect size (LEfSe) to examine differences between groups and subgroups. A gut microbiota-based model for predictive diagnosis of AS was constructed using random forest algorithm and its predictive value was assessed by receiver-operating characteristic analyses.
Results: Our results showed that fecal microbial communities in patients with AS differ significantly from those in HCs, driven by a higher abundance of 7 genera (Prevotella_9, Dialister, Comamonas, Collinsella, Streptococcus, Alloprevotella and Prevotella_2) and a lower abundance of 4 genera (Eubacterium_ruminantium_ group, Ruminococcus_gnavus_group, Lachnospira and Bacteroides). In addition, pAS patients were more enriched in Comamonas, Streptococcus and Collinsella, while axAS patients were more enriched in Prevotella_2. An 8 genera-based model showed high accuracy for distinguishing AS patients from HCs with an area under the curve (AUC) up to 0.950.
Conclusions: Our results revealed specific alterations in the gut microbiome in patients with different phenotypes of AS, and the classification model based on gut microbial features might provide a new direction for future clinical diagnosis. Lastly, discovery of the associated microbes of AS in the gut microbiome may help us to seek more treatments for this disease.
