Objective Effect of a probiotic on the gut microbiome and peripheral immune function in healthy controls and relapsing-remitting multiple sclerosis (RRMS) patients. Methods MS patients (N=9) and controls (N=13) were orally administered a probiotic containing Lactobacillus, Bifidobacterium and Streptococcus twice daily for two months. Blood and stool specimens were collected at baseline, after completion of the 2-month treatment, and 3 months after discontinuation of therapy. Frozen peripheral blood mononuclear cells (PBMCs) were used for immune cell profiling. Stool samples were used for 16S rRNA profiling and metabolomics. Results Probiotic administration increased the abundance of several taxa known to be depleted in MS such as Lactobacillus. We found that probiotic use decreased the abundance of taxa previously associated with dysbiosis in MS including Akkermansia and Blautia. Predictive metagenomic analysis revealed a decrease in the abundance of several KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathways associated with altered gut microbiota function in MS patients such as methane metabolism following probiotic supplementation. At the immune level, probiotic administration induced an anti-inflammatory peripheral immune response characterized by decreased frequency of inflammatory monocytes, decreased mean fluorescence intensity (MFI) of CD80 on classical monocytes as well as decreased HLA-DR MFI on dendritic cells. Probiotic administration was also associated with decreased expression of MS risk allele HLA-DQA1 in controls. Probiotic induced increased in the abundance of Lactobacillus and Bifidobacterium were associated with decreased expression of MS risk allele HLA.DPB1 in controls. Interpretation Our results suggest that probiotic could have a synergistic effect with current MS therapies.

The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients’ clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A “threshold” was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course.

ObjectiveTo understand the role of gut microbiome in influencing the pathogenesis of neuromyelitis optica spectrum disorders (NMOSDs) among patients of south Indian origin.MethodsIn this case-control study, stool and blood samples were collected from 39 patients with NMOSD, including 17 with aquaporin 4 IgG antibodies (AQP4+) and 36 matched controls. 16S ribosomal RNA (rRNA) sequencing was used to investigate the gut microbiome. Peripheral CD4+ T cells were sorted in 12 healthy controls, and in 12 patients with AQP4+ NMOSD, RNA was extracted and immune gene expression was analyzed using the NanoString nCounter human immunology kit code set.ResultsMicrobiota community structure (beta diversity) differed between patients with AQP4+ NMOSD and healthy controls (p < 0.001, pairwise PERMANOVA test). Linear discriminatory analysis effect size identified several members of the microbiota that were altered in patients with NMOSD, including an increase in Clostridium bolteae (effect size 4.23, p 0.00007). C bolteae was significantly more prevalent (p = 0.02) among patients with AQP4-IgG+ NMOSD (n = 8/17 subjects) compared with seronegative patients (n = 3/22) and was absent among healthy stool samples. C bolteae has a highly conserved glycerol uptake facilitator and related aquaporin protein (p59-71) that shares sequence homology with AQP4 peptide (p92-104), positioned within an immunodominant (AQP4 specific) T-cell epitope (p91-110). Presence of C bolteae correlated with expression of inflammatory genes associated with both innate and adaptive immunities and particularly involved in plasma cell differentiation, B cell chemotaxis, and Th17 activation.ConclusionOur study described elevated levels of C bolteae associated with AQP4+ NMOSD among Indian patients. It is possible that this organism may be causally related to the immunopathogenesis of this disease in susceptible individuals.