Extended Data Fig. 1: Graphical summary of experiments, analyses and conclusion.

Step 1 (see also Fig. 1): First, we evaluated which bacterial genera were most associated with EAE disease development across genetically heterogenous mice harbouring 5 distinct complex microbiota compositions. We found that low relative abundance of Akkermansia (dark red bacteria icon) was associated with higher risk of severe EAE development (red circle), and higher abundances of this genus was associated with moderate disease and low disease risk (green circle). Step 2 (see also Figs. 2−4): Thus, we wondered whether presence or relative abundance of the Akkermansia type species Akkermansia muciniphila before induction of EAE could predict subsequent disease development. To test this, we induced EAE in mice harbouring 6 different combinations of a well-characterised 14-strain consortium under gnotobiotic conditions. In mice harbouring these microbiotas, A. muciniphila was either present or absent, and if present, A. muciniphila provided either high or low relative abundances. We found that distinct microbiota compositions resulted in ‘high risk’ and ‘low risk’ microbiota compositions, as determined by the proportion of mice developing severe disease. However, disease susceptibility was not uniform across mice harbouring the same microbiota. Step 3 (see also Fig. 5): We further determined that neither relative abundance, nor the presence or absence of A. muciniphila, or any other strain of the tested consortium, could reliably predict EAE development across distinct communities in individual mice. However, we found that the pre-EAE IgA coating index (ICI) of a consortium member strain, Bacteroides ovatus, significantly correlated with individual EAE outcome after disease induction, irrespective of the microbiota composition. Step 4 (see also Fig. 6): We then successfully verified the potential for species-specific ICIs, as determined before disease induction, to predict individual disease severity in genetically distinct mice containing various complex microbiotas. Conclusion: Making predictions on EAE development based on microbiota characteristics (that is assessing the individual disease risk) is possible, however, it must take into account inter-microbial interactions (‘networks’) within a given, individual community and host-specific responses to a certain microbiota composition.