Curcumin β-D-Glucuronide Modulates an Autoimmune Model of Multiple Sclerosis with Altered Gut Microbiota in the Ileum and Feces

Abstract

We developed a prodrug type of curcumin, curcumin monoglucuronide (CMG), whose intravenous/intraperitoneal injection achieves a high serum concentration of free-form curcumin. Although curcumin has been reported to alter the gut microbiota and immune responses, it is unclear whether the altered microbiota could be associated with inflammation in immune-mediated diseases, such as multiple sclerosis (MS). We aimed to determine whether CMG administration could affect the gut microbiota at three anatomical sites (feces, ileal contents, and the ileal mucosa), leading to suppression of inflammation in the central nervous system (CNS) in an autoimmune model for MS, experimental autoimmune encephalomyelitis (EAE). We injected EAE mice with CMG, harvested the brains and spinal cords for histological analyses, and conducted microbiome analyses using 16S rRNA sequencing. CMG administration modulated EAE clinically and histologically, and altered overall microbiota compositions in feces and ileal contents, but not the ileal mucosa. Principal component analysis (PCA) of the microbiome showed that principal component (PC) 1 values in ileal contents, but not in feces, correlated with the clinical and histological EAE scores. On the other hand, when we analyzed the individual bacteria of the microbiota, the EAE scores correlated with significant increases in the relative abundance of two bacterial species at each anatomical site: Ruminococcus bromii and Blautia (Ruminococcus) gnavus in feces, Turicibacter sp. and Alistipes finegoldii in ileal contents, and Burkholderia spp. and Azoarcus spp. in the ileal mucosa. Therefore, CMG administration could alter the gut microbiota at the three different sites differentially in not only the overall gut microbiome compositions but also the abundance of individual bacteria, each of which was associated with modulation of neuroinflammation.

Keywords: Alpha diversity; PICRUSt analysis; animal model; bacterial taxonomy; bioinformatics; confidence interval; histology; pattern matching.