Vilnius University, Lithuania
Solveiga Samulėnaitė, PhD
Scientist
TITLE: Gut Microbiota and miRNAs in Food Addiction: From Signatures to Functional Validation
Food addiction is characterized by compulsive consumption of highly palatable foods and loss of control over food intake, sharing behavioral and neurobiological features with substance use disorders. It has been suggested that food addiction arises from the dynamic interactions among multiple gene networks and environmental factors. Some of these factors might be the gut microbiota and circulating miRNAs, which have already been described to play a role in obesity, addictions, and other disorders.
Using a translational approach combining human studies with an operant food self-administration model in mice, we investigated gut microbiota and miRNA signatures associated with food addiction. Cross-species analyses identified Blautia and miR-29c-3p and miR-665-3p as potential protective factors.
Their functional relevance was subsequently investigated in mice. Supplementation with Blautia wexlerae led to decreased addictive-like behavior, with lower persistence of response and motivation; meanwhile, potential prebiotics lactulose and rhamnose, which were linked to increased Blautia abundance, prevented food addiction development and decreased compulsivity-like behavior. On the other hand, inhibition of miRNA-29c-3p and miRNA-665-3p resulted in enhanced vulnerability towards food addiction and increased persistence of response and compulsivity towards palatable food, respectively, suggesting that their absence might facilitate the development of food addiction.
Together, these findings identify convergent gut microbiota and miRNA signatures associated with food addiction across humans and mice and provide experimental evidence for their functional relevance. They highlight microbiota-brain interactions and miRNA-mediated mechanisms as potential targets for preventing maladaptive eating behaviors.
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A202-A203
Gut Microbiota and miRNAs in Food Addiction: From Signatures to Functional Validation
Solveiga Samulėnaitė, PhD (Vilnius university, Lithuania)
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