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Determining the impact of the microbiome on health and disease: usingstandard and advanced bioinformatics
Dissertation   Open access

Determining the impact of the microbiome on health and disease: usingstandard and advanced bioinformatics

Rachel L Fitzjerrells
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Autumn 2024
DOI: 10.25820/etd.007593
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Abstract

The microbiome consists of all of the microorganisms found in and on our bodies. Advancements in sequencing technologies in the last two decades have identified the microbiome as a key player in maintaining our health, or homeostasis. The microbiome helps with several physiological processes including food digestion, immune system regulation, and pathogen defense. However, when the microbiome is in a perturbed or a “dysbiotic” state, homeostasis is lost, and this often predisposes and/or propagates disease. The presence of dysbiosis in diseases such as cancers and autoimmune disorders, highlights the importance of the microbiome in overall human health. In the introduction of this thesis, I discuss prior studies on the microbiome, bioinformatic analysis, and the knowledge gaps in the field. The following chapters discuss our published work on dysbiosis in patients with breast cancer and multiple sclerosis as well as factors influencing the microbiome, such as dietary intervention. Additionally, I present a novel bioinformatics tool that utilizes unsupervised machine learning to address limitations in microbiome analysis. Finally, I discuss our unpublished recent work, an investigation of both the oral microbiome and metabolome compositions in people with MS (pwMS). There is substantial evidence that the gut microbiome influences MS pathobiology, however there is limited data on the role of the oral microbiome (the second most diverse microbiome community) in MS. Additionally, the salivary metabolome is altered in patients with other neurological diseases but has not been studied in pwMS. Therefore, I performed studies to address these knowledge gaps and identified an oral microbiome and metabolome signature unique to pwMS. My results provide potential biomarkers for MS diagnostics, new targets for individualized therapies, and evidence that the oral microbiome is pivotal in understanding the pathobiology of MS.
Bioinformatics Machine Learning Multiple Sclerosis Microbiology Metabolome Metagenomics Microbiome

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