Gut microbiome variation is associated to Multiple Sclerosis phenotypic subtypes

TLDR: Diversity, enterotypes, and specific gut microbial taxa variation between MS and HC, and between MS subgroups are studied.

Abstract: Objective Multiple sclerosis (MS) is a heterogenous, inflammatory disease of the central nervous system. Microbiota alterations in MS versus healthy controls (HC) are observed, but results are inconsistent. We studied diversity, enterotypes, and specific gut microbial taxa variation between MS and HC, and between MS subgroups. Methods Amplicon sequencing of the 16S ribosomal RNA V4 region (Illumina MiSeq) was used to evaluate alpha and beta diversity, enterotypes, and relative taxa abundances on stool samples. MS subgroups were based on phenotype, disease course modifiers, and treatment status. Results were controlled for recently identified confounders of microbiota composition. Results Ninety-eight MS patients and 120 HC were included. Microbial richness was lower in interferon-treated (RRMS_I, N = 24) and untreated relapsing-remitting MS during relapse (RRMS_R, N = 4) when compared to benign (BMS, N = 20; Z = -3.07, Pcorr = 0.032 and Z = -2.68, Pcorr = 0.055) and primary progressive MS (PPMS, N = 26; Z = -2.39, Pcorr = 0.062 and Z = -2.26, Pcorr = 0.071). HC (N = 120) and active untreated MS (RRMS_U, N = 24) showed intermediate microbial richness. Enterotypes were associated with clinical subgroups (N = 218, chi(2) = 36.10, P = 0.002), with Bacteroides 2 enterotype being more prevalent in RRMS_I. Butyricicoccus abundance was lower in PPMS than in RRMS_U (Z = -3.00, Pcorr = 0.014) and BMS (Z = -2.56, Pcorr = 0.031), lower in RRMS_I than in BMS (Z = -2.50, Pcorr = 0.034) and RRMS_U (Z = -2.91, Pcorr = 0.013), and inversely correlated with self-reported physical symptoms (rho = -0.400, Pcorr = 0.001) and disease severity (rho = -0.223, P = 0.027). Interpretation These results emphasize the importance of phenotypic subcategorization in MS-microbiome research, possibly explaining previous result heterogeneity, while showing the potential for specific microbiome-based biomarkers for disease activity and severity.