Fig. 6: IgA coating index of reporter species to predict neuroinflammation-promoting properties of complex microbiotas.
From: Gut microbial factors predict disease severity in a mouse model of multiple sclerosis
a, Microbiota from SPF-housed Muc2−/− (KO) and Muc2+/+ (WT) mice was transferred to GF C57BL/6J (BL/6) and Muc2−/− mice. After 21 d of colonization with the donor microbiota, EAE was induced and disease course monitored for 30 d. b, Venn diagram of microbiota compositions by full-length 16S rRNA gene sequencing across the four donor–recipient combinations. Numbers reflect shared species for the specified donor–recipient combination, with core microbiota, shared by all, highlighted in grey. c, β-diversity of pre-EAE microbiota compositions as determined from a Bray–Curtis distance matrix of arcsine square root-transformed relative abundance data, ordinated using principal coordinate analysis. d, AUC (left) and maximum EAE disease score (Max, right) of individual disease courses depicted in Extended Data Fig. 9d. AUC, one-way ANOVA with donor–recipient combinations as tested variable (AUC) or Kruskall–Wallis test with donor–recipient combinations as tested variable (Max). Individual EAE phenotype classification (‘mild’ vs ‘severe’) based on Extended Data Fig. 9f. Boxplots show median, quartiles and 1.5× IQR. e, Individual-based Pearson and Spearman correlations of EAE-associated readouts (AUC, Max) with ICIs of Eubacterium coprostanoligenes and Phocaeicola dorei before EAE induction. Mice of all donor–recipient combinations were included in this analysis. Significant correlations are depicted in shades of blue or red. Grey bars represent mean relative abundance across all mice before EAE induction. f, Correlation of individual ICIs for E. coprostanoligenes and P. dorei with individual values for AUC (top) and Max (bottom) by linear regression. Only mice where E. coprostanoligenes or P. dorei were detectable in both fractions after sorting are shown. Dashed line represents linear regression, with the confidence interval shaded in grey. g, Binomial regression model to predict probability of severe disease based on classification by either AUC (top panels) or Max (bottom panels) using ICI of E. coprostanoligenes or P. dorei. For definition of disease severity, see Extended Data Fig. 9f. h, Graphical summary of results. Microbiota composition alone fails to predict individual EAE susceptibility or development. However, individual host–microbiota interactions, which are reflected by the ICI of certain reporter species, were suitable for predicting individual EAE disease courses.
