Oral Microbiota Profile in a Group of Anti-AChR Antibody–Positive Myasthenia Gravis Patients

Mendelian randomization study revealed a gut microbiota-neuromuscular junction axis in myasthenia gravis

A growing number of studies have implicated that gut microbiota abundance is associated with myasthenia gravis (MG). However, the causal relationship underlying the associations is still unclear. Here, we aim to investigate the causal effect of gut microbiota on MG using Mendelian randomization (MR) method. Publicly available Genome-wide association study (GWAS) summary-level data for gut microbiota and for MG were extracted. Inverse variance weighted was used as the main method to analyze causality. The robustness of the results was validated with sensitivity analyses. Our results indicated that genetically predicted increased phylum Lentisphaerae (OR = 1.319, p = 0.026), class Lentisphaerae (OR = 1.306, p = 0.044), order Victivallales (OR = 1.306, p = 0.044), order Mollicutes (OR = 1.424, p = 0.041), and genus Faecalibacterium (OR = 1.763, p = 0.002) were potentially associated with a higher risk of MG; while phylum Actinobacteria (OR = 0.602, p = 0.0124), class Gammaproteobacteria (OR = 0.587, p = 0.036), family Defluviitaleaceae (OR = 0.695, p = 0.047), family Peptococcaceae (OR = 0.698, p = 0.029), and family Family XIII (OR = 0.614, p = 0.017) were related to a lower risk of MG. The present study provides genetic evidence for the causal associations between gut microbiota and MG, thus suggesting novel insights into the gut microbiota-neuromuscular junction axis in the pathogenesis of MG.

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis

Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.

The Role of Human Microbiota in Myasthenia Gravis: A Narrative Review

Myasthenia gravis (MG) is an autoimmune neuromuscular disease characterized by fluctuating weakness of the skeletal muscles. Although antibodies against the neuromuscular junction components are recognized, the MG pathogenesis remains unclear, even if with a well-known multifactorial character. However, the perturbations of human microbiota have been recently suggested to contribute to MG pathogenesis and clinical course. Accordingly, some products derived from commensal flora have been demonstrated to have anti-inflammatory effects, while other have been shown to possess pro-inflammatory properties. In addition, patients with MG when compared with age-matched controls showed a distinctive composition in the oral and gut microbiota, with a typical increase in Streptococcus and Bacteroides and a reduction in Clostridia as well as short-chain fatty acid reduction. Moreover, restoring the gut microbiota perturbation has been evidenced after the administration of probiotics followed by an improvement of symptoms in MG cases. To highlight the role of the oral and gut microbiota in MG pathogenesis and clinical course, here, the current evidence has been summarized and reviewed.