Gut microbial factors predict disease severity in a mouse model of multiple sclerosis

Alex Steimle, Mareike Neumann, Erica T. Grant, Stéphanie Willieme, Alessandro De Sciscio, Amy Parrish, Markus Ollert, Eiji Miyauchi, Tomoyoshi Soga, Shinji Fukuda, Hiroshi Ohno, Mahesh S. Desai*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)

Abstract

Gut bacteria are linked to neurodegenerative diseases but the risk factors beyond microbiota composition are limited. Here we used a pre-clinical model of multiple sclerosis (MS), experimental autoimmune encephalomyelitis (EAE), to identify microbial risk factors. Mice with different genotypes and complex microbiotas or six combinations of a synthetic human microbiota were analysed, resulting in varying probabilities of severe neuroinflammation. However, the presence or relative abundances of suspected microbial risk factors failed to predict disease severity. Akkermansia muciniphila, often associated with MS, exhibited variable associations with EAE severity depending on the background microbiota. Significant inter-individual disease course variations were observed among mice harbouring the same microbiota. Evaluation of microbial functional characteristics and host immune responses demonstrated that the immunoglobulin A coating index of certain bacteria before disease onset is a robust individualized predictor of disease development. Our study highlights the need to consider microbial community networks and host-specific bidirectional interactions when aiming to predict severity of neuroinflammation.

Original languageEnglish
Pages (from-to)2244-2261
Number of pages18
JournalNature Microbiology
Volume9
Issue number9
DOIs
Publication statusPublished - Sept 2024

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