The microbiome has emerged as a major contributor to human health and disease. Numerous sources implicate shifts in the gut microbiome as potentially pathologic for a variety of autoimmune diseases. For many of these diseases, changes in the microbiome are subtle and descriptive in nature. Further, limitations in 16S rRNA sequencing make inter-study comparisons and identification of causative microbes difficult. This heterogeneity can be traced to the evolution of sequencing technology and variability in sampling methodology. For example, in multiple sclerosis, numerous research groups have described shifts in gut microbes among patients, but identified taxa have been inconsistent across studies. To address these challenges, we have implemented long-amplicon sequencing of the 16S-ITS-23S rRNA operon, which, when combined with machine learning-based denoising, allows reliable taxonomic resolution down to the species and strain levels, facilitating more definitive comparison between clinical cohorts and possible identification of causative microbes. Using these technologies, we set out to study the microbiomes of new-onset treatment-naïve relapsing-remitting multiple sclerosis at baseline and post B cell depletion therapy, with healthy donors as a control group. We identify Bacteroides emerge as being enriched in the gut microbiota of MS patients. This trend reverses upon initiation of effective immunomodulatory therapy for MS. Manipulation of the gut microbiome is a putative mechanism of action by which immunomodulatory therapies may impact MS and other immune-medicated diseases. Further exploration of these phenomena may open new avenues for understanding and treating MS.

Learning Objectives:

1. Understand the pros/cons of generating and processing long-read microbiome data

2. Identify differences between the microbiome of healthy subjects and MS patients at baseline

3. Understand how immunotherapy may influence the microbiome of MS patients