<p dir="ltr">Dietary fibers have been observed to modulate the gut microbiome in ways that prevent and moderate human diseases and confer health benefits onto their human host. How dietary fibers do this is through their structure; gut microbes are equipped with a variety of differ- ent carbohydrate-active enzymes (CAZymes) that allow some to hydrolyze glycosidic bonds, thereby utilizing the dietary fiber. The more complex the dietary fiber, the more diverse the maintained gut microbiota may be, as specialist species may be required for complete hydrol- ysis. Therefore, increasing structural complexity of dietary fibers may increase gut microbial diversity and help prevent diseases. To understand if structural features impact the gut mi- crobiome, a set of resistant glucans varying in structures, including mixed-linkage -glucans, resistant maltodextrins (similar to type IV resistant starch) and polydextroses, which are comprised entirely of glucose, were used as substrates in an in vitro sequential batch fermen- tation using fecal microbiota form three healthy donors as inocula. I measured metabolic outputs, growth curves, and community structures by 16S rRNA amplicon sequencing, which I analyzed for through alpha and beta diversity differences and taxa that overrepresented and increased in each treatment. My results show that, depending on the donor and the resistant glucan, structure does significantly impact the concentrations of short-chain fatty acids (SCFAs) and other metabolites that are produced. Resistant glucan structure also impacts alpha and beta diversity to a degree and linear discriminant analysis (by LEfSe) results also support that specific species have preference towards substrates as well. Next, resistant glucans were supplemented into a high-fat diet, and compared these diets to a low- fat diet (LFD), high-fat diet with cellulose (HFD), and high-fat without cellulose (HWC) in a mouse study using C57BL/6J mice over 4 weeks. Increasing microbial diversity will not only increase diversity in the gut microbiome, but it will also provide protective effects in behavior such as helping to prevent anxiety. I measured weight, metabolic outputs, 16S community structure, changes in alpha and beta diversity, and differential abundances of OTUs and taxa by discriminant analysis Effect Size (LEfSe) and Metastats, and anxiety behaviors using open field and light/dark box tests. Microbial community structure was significantly different in treatment groups from controls. Anxiety for mice in tapioca dextrin 01 (TD01), tapioca dextrin 03 (TD03), and resistant maltodextrin (RMF) treatment groups were gen- erally increased, suggesting that the chemical structure of these resistant dextrins may alter the gut microbiome in ways that may influence behavior.</p><p dir="ltr">My overall results support the hypothesis that the fine structural features of dietary fibers do significantly impact the gut microbiome by selecting for specific microbiota, and may even impact cognition and behavior.</p>

There has been tremendous advancement in sequencing technologies; the rate at which sequencing data can be generated has increased multifold while the cost of sequencing continues on a downward descent. Sequencing data provide novel insights into the ecological environment of microbes as well as human health and disease status but challenge investigators with a variety of computational issues. This thesis focuses on three common problems in the analysis of high-throughput data. The goals of the first project are to (1) develop a statistical framework and a complete software pipeline for metagenomics that identifies microbes to the strain level and thus facilitating a personalized drug treatment targeting the strain; and (2) estimate the relative content of microbes in a sample as accurately and as quickly as possible. The second project focuses on the analysis of the microbiome variation across multiple samples. Studying the variation of microbiomes under different conditions within an organism or environment is the key to diagnosing diseases and providing personalized treatments. The goals are to (1) identify various statistical diversity measures; (2) develop confidence regions for the relative abundance estimates; (3) perform multi-dimensional and differential expression analysis; and (4) develop a complete pipeline for multi-sample microbiome analysis. The third project is focused on batch effect analysis. When analyzing high dimensional data, non-biological experimental variation or “batch effects” confound the true associations between the conditions of interest and the outcome variable. Batch effects exist even after normalization. Hence, unless the batch effects are identified and corrected, any attempts for downstream analyses, will likely be error prone and may lead to false positive results. The goals are to (1) analyze the effect of correlation of the batch adjusted data and develop new techniques to account for correlation in two step hypothesis testing approach; (2) develop a software pipeline to identify whether batch effects are present in the data and adjust for batch effects in a suitable way. In summary, we developed software pipelines called PathoScope, PathoStat and BatchQC as part of these projects and validated our techniques using simulation and real data sets.