Oral microbiota and serum metabolomics features in newly diagnosed anti-AchR antibody-positive myasthenia gravis patients.
Myasthenia gravis (MG) is an autoimmune disorder mediated by autoantibodies. While research regarding the microbiota-gut-brain axis has expanded significantly in recent years, the role of the oral microbiota in MG and its potential association with systemic metabolism remain poorly understood. To address this, we conducted a case-control study to investigate potential alterations in the oral microbiota and serum metabolites in newly diagnosed and treatment-naïve patients with MG.
In total, 38 newly diagnosed and treatment-naïve anti-AchR antibody-positive MG patients were recruited, including 19 with ocular myasthenia gravis (OMG) and 19 with generalized myasthenia gravis (GMG), along with 29 healthy controls (HCs). Saliva and serum samples were collected for 16S rRNA gene high-throughput sequencing and untargeted metabolomic profiling, respectively. Data processing and integration were performed using multiple bioinformatics and statistical methods, including α- and β-diversity analyses, LEfSe, Spearman correlation, and metabolic pathway enrichment analysis.
Compared with HCs, patients with OMG and GMG exhibited significant dysbiosis. At the phylum level, the abundance of Bacillota was significantly increased, whereas that of Bacteroidota and Campylobacterota was significantly decreased. At the genus level, the abundances of Streptococcus and Bifidobacterium were significantly elevated; in contrast, those of Prevotella and others were significantly reduced. Serum metabolomics analysis identified distinct metabolic disorders in MG patients, involving key pathways such as lipid metabolism, neuroactive ligand-receptor interaction, and amino acid metabolism. Correlation network analysis further revealed significant associations between specific oral genera (Streptococcus, Bifidobacterium) and differential metabolites primarily involved in lipid metabolism, amino acid metabolism, and terpenoid and polyketide metabolism in both OMG and GMG populations.
In MG patients, significant oral dysbiosis may be associated with MG-related metabolic alterations by disrupting metabolic pathways, such as those associated with lipid and amino acid metabolism.
In total, 38 newly diagnosed and treatment-naïve anti-AchR antibody-positive MG patients were recruited, including 19 with ocular myasthenia gravis (OMG) and 19 with generalized myasthenia gravis (GMG), along with 29 healthy controls (HCs). Saliva and serum samples were collected for 16S rRNA gene high-throughput sequencing and untargeted metabolomic profiling, respectively. Data processing and integration were performed using multiple bioinformatics and statistical methods, including α- and β-diversity analyses, LEfSe, Spearman correlation, and metabolic pathway enrichment analysis.
Compared with HCs, patients with OMG and GMG exhibited significant dysbiosis. At the phylum level, the abundance of Bacillota was significantly increased, whereas that of Bacteroidota and Campylobacterota was significantly decreased. At the genus level, the abundances of Streptococcus and Bifidobacterium were significantly elevated; in contrast, those of Prevotella and others were significantly reduced. Serum metabolomics analysis identified distinct metabolic disorders in MG patients, involving key pathways such as lipid metabolism, neuroactive ligand-receptor interaction, and amino acid metabolism. Correlation network analysis further revealed significant associations between specific oral genera (Streptococcus, Bifidobacterium) and differential metabolites primarily involved in lipid metabolism, amino acid metabolism, and terpenoid and polyketide metabolism in both OMG and GMG populations.
In MG patients, significant oral dysbiosis may be associated with MG-related metabolic alterations by disrupting metabolic pathways, such as those associated with lipid and amino acid metabolism.
Authors
Huang Huang, Duan Duan, Li Li, Liu Liu, Shang Shang, Zhang Zhang, Liu Liu, Wang Wang, Zhao Zhao, Zhang Zhang, Gao Gao
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