Gut microbial factors predict disease severity in a mouse model of multiple sclerosis
Alex Steimle1, Mareike Neumann1,2, Erica T Grant1,2
1Department of Infection and Immunity, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg.
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.
Insights
Microbiota composition alone does not predict neuroinflammation severity in multiple sclerosis (MS) models. Instead, the immunoglobulin A coating index of gut bacteria before disease onset is a key predictor of disease development.
Area of Science:
- Microbiology
- Neuroimmunology
- Gastroenterology
Background:
- Gut bacteria are increasingly linked to neurodegenerative diseases like multiple sclerosis (MS).
- However, risk factors beyond simple microbiota composition for predicting disease severity remain limited.
- Existing research often overlooks the complex interplay between host immunity and microbial communities.
Purpose of the Study:
- To identify specific microbial risk factors for neuroinflammation using a pre-clinical model of MS.
- To investigate the predictive power of microbial composition and functional characteristics on disease severity.
- To explore host-specific interactions with the gut microbiota in the context of neuroinflammation.
Main Methods:
- Utilized a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis - EAE).
- Analyzed mice with diverse genetic backgrounds and complex or synthetic human microbiotas.
- Evaluated microbial functional characteristics and host immune responses, including immunoglobulin A (IgA) coating indices.
Main Results:
- Microbiota composition and the abundance of specific bacteria like Akkermansia muciniphila did not reliably predict EAE severity.
- Significant variations in disease course were observed even among mice with identical microbiotas.
- The immunoglobulin A coating index of specific bacteria prior to disease onset emerged as a robust predictor of neuroinflammation severity.
Conclusions:
- Microbiota composition alone is insufficient for predicting neuroinflammation severity in MS models.
- Host-specific interactions and microbial functional attributes, such as IgA coating, are crucial for predicting disease development.
- Future research should focus on microbial community networks and bidirectional host-microbe interactions for accurate prediction of neuroinflammatory diseases.
