UC San Diego

There is increasing recognition that the gut microbiome may influence the development and progression of neurological diseases. Microbes in the digestive tract and their metabolic products are known to regulate and modulate metabolic and immunological effectors of neurological disease through the gut microbiota-brain axis. These microbes and their chemicals are currently being researched in both human and animal models. Just as the microbiome changes throughout the lifespan, the gut-microbiota brain axis is relevant in neurological diseases affecting all ages of humans, such as neurodevelopmental disorders in young children, mood and neuropsychological disorders in young adults, and neurodegenerative disorders in older adults. This thesis addresses all three classes of neurological disorders across the lifespan. While several correlative studies have already been published in the scientific literature, this thesis addresses additional factors relevant to the gut-microbiota brain axis. Specifically, lifestyle exposures that are associated with neurological disorders including diet and medication, which are strongly intertwined with the gut microbiome, are considered. Furthermore, the scope of the gut microbiome-induced changes is augmented with the addition of an untargeted metabolomics data layer. This thesis makes use of the latest multi-omics integration technology as well as updated compositional data analysis techniques to ensure its statistics are robust. In the first part of this thesis, the typical microbiota shifts that occur across the human lifespan are outlined and biological and environmental factors that can disrupt the gut microbiome are proposed. Next, microbiome and metabolomic analyses of Pitt Hopkins Syndrome, a severe autism spectrum disorder, are presented. In chapter three, we demonstrate that while medication use has a larger effect on the microbiome than neuropsychiatric disorders, there are still reproducible microbial markers of neuropsychiatric disorders in young adults. Finally, we examine changes to the microbiome and metabolome after adults at risk of cognitive impairment are put through both a low-fat and ketogenic dietary intervention. Signals for both dietary change and risk of cognitive impairment are identified using a linear mixed effects model with Bayesian Inferential regression.