ASV tables of Myasthenia gravis (MG) and non-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 used to
establishe a sequencing library by constructing amplicon sequence variants
(ASVs) and completing taxonomic classification of each representative DNA
sequence. Four ML methods 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. 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. 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.