DOI: 10.5061/dryad.73n5tb32m
title: ASV tables of Myasthenia gravis (MG) and non-Myasthenia gravis

description: 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. 
description: In this prospective study, 19
 individuals with MG and 10 individuals without were consecutively
 recruited from Fu-Jen Catholic University Hospital. Individuals were
 enrolled in the MG group if they 1) were given a diagnosis of MG on the
 basis of having the combination of symptoms and signs that are
 characteristic of muscle weakness with diurnal changes and either 2a) had
 a positive test result for specific autoantibodies or 2b) had a positive
 electrophysiological diagnosis obtained using single-fiber
 electromyography and repetitive nerve stimulation (Rousseff, 2021). None of the participants had received any
 abdominal chirurgic intervention; consumed antibiotics, probiotics, or
 antacids during the previous 6 months; or reported gastrointestinal
 symptoms during the previous year. This study was approved by the Regional
 Ethics Committee of Fu-Jen Catholic University Hospital and written
 informed consent was obtained from each participant (No. FJUH109043). All
 experiments were completed in accordance with the Declaration of
 Helsinki’s Ethical Principles for Medical Research Involving Human
 Subjects and under a set of approved guidelines and regulations. The
 severity of MG was determined using quantitative MG (QMG), MG activities
 of daily living (MG-ADL), MG composition (MGC), and MG quality of life
 (MG-QoL) scores (Jaretzki
 et al., 2000). Using the
 categories of the QMG and MGC scales, we categorized the scores on these
 scales into ocular, bulbar, and limb groups. Fresh stool samples were
 collected from each participant and immediately frozen at
 −80°C until analysis. Figure 1 summarizes the
 overall study workflow. Each stage in the process, including the sample
 testing and polymerase chain reaction (PCR) and library creation and
 sequencing, can affect the quality of the data, and the accuracy of
 analytical findings is directly influenced by the quality of data.
 Therefore, quality control measures were implemented at each stage of the
 process to ensure data accuracy. DNA Extraction and 16S Metagenomics
 Sequencing Genomic DNA was extracted from the samples using
 the EasyPrep Stool Genomic DNA Kit (Biotools, New Taipei City, Taiwan).
 The DNA concentration was determined and adjusted to 5 ng/μL for
 subsequent processing. In
 accordance with the 16S Metagenomic Sequencing Library Preparation
 protocol (Illumina), the specific primer set 341F:
 5’-CCTACGGGNGGCWGCAG-3’, 806R: 5’-GACTACHVGGGTATCTAATCC-3’ was employed to
 amplify the variable regions V3 and V4 of the 16S rRNA gene. A PCR was
 conducted using KAPA HiFi HotStart ReadyMix (Roche) and 12.5 ng of genomic
 DNA (gDNA) under the following conditions: 95°C for 3 min, 25 cycles of
 95°C for 30 s, 55°C for 30 s, 72°C for 30 s, and a final extension of 72°C
 for 5 min. The reaction was subsequently maintained at 4°C. The products
 of the PCR were evaluated using 1.5% agarose gel, and samples with a
 bright main strip at approximately 500 bp were selected for further
 library preparation. The selected samples were purified using AMPure XP
 beads. A sequencing library was prepared using the 16S
 Metagenomic Sequencing Library Preparation procedure (Illumina). To
 summarize, the 16S rRNA V3–V4 region PCR amplicon was subjected to a
 secondary PCR, which was conducted using the Nextera XT Index Kit with
 dual indices and Illumina sequencing adapters from Illumina. The indexed
 PCR product was evaluated for quality by using the Qubit 4.0 Fluorometer
 (Thermo Scientific) and a Qsep100TM system. The indexed PCR products were
 mixed in equal amounts to create a sequencing library. The library was
 sequenced on an Illumina MiSeq platform, which generated 300‐bp paired
 reads.