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Description

Humans and their microbiota are in a symbiotic relationship. It is known that microbiale residents within and on human bodies have the potential to impact host physiology in both healthy and disease states. To date, little is known about the potential influence of the gut microbiome on the onset of

Humans and their microbiota are in a symbiotic relationship. It is known that microbiale residents within and on human bodies have the potential to impact host physiology in both healthy and disease states. To date, little is known about the potential influence of the gut microbiome on the onset of nausea symptoms among cancer patients undergoing chemotherapy treatment. Chemotherapy-induced nausea (CIN) is a serious and common side effect. The CIN presentation is often coupled with other symptoms such as fatigue, sleep disturbance, depression, and anxiety. These symptoms both on an individual and collective level, cause negative impacts on patients’ health outcome as they challenge patients’ ability to tolerate and comply with chemotherapy. To understand the association between gut microbiome and CIN, we applied 16S rRNA amplicon sequencing to characterize the gut microbiome of breast cancer patients who reported nausea symptoms and those who reported no nausea symptoms. We hypothesize that the gut microbiome of patients who reported nausea symptoms is distinct from patients who reported no nausea. Our findings support this hypothesis, as the gut microbiome of nausea case was distinct from the no nausea cases. Specifically, we observed decreased abundance of Bacteroidetes in patients who reported nausea, while patients who reported no CIN had constant or increased abundance of Bacteroidetes. Overall, we showed that changes in the gut microbiota have an association with the occurrence of CIN symptoms among breast cancer patients receiving chemotherapy. These findings provide preliminary data for extensive research on the role of gut microbiome in CIN in the future.

ContributorsXing, Zhu (Author) / Zhu, Qiyun (Thesis director) / Singh, Komal (Committee member) / Morocho, Henry (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2023-05
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Description
Metagenomics is the study of the structure and function of microbial communities through the application of the whole-genome shotgun (WGS) sequencing method. Providing high-resolution community profiles at species or even strain levels, metagenomics points to a new direction for microbiome research in understanding microbial gene function, microbial-microbial interactions, and host-microbe

Metagenomics is the study of the structure and function of microbial communities through the application of the whole-genome shotgun (WGS) sequencing method. Providing high-resolution community profiles at species or even strain levels, metagenomics points to a new direction for microbiome research in understanding microbial gene function, microbial-microbial interactions, and host-microbe interactions. My thesis work includes innovation in metagenomic research through the application of ChatGPT in assisting beginning researchers, adopt pre-existed alpha diversity metric for metagenomic data to improve diversity calculation, and the application of metagenomic data in Alzheimer’s disease research.Since the release of ChatGPT in March 2023, the conversation regarding AI in research has promptly been debated. Through the prompted bioinformatic case study, I demonstrate the application of ChatGPT in conducting metagenomic analysis. I constructed and tested a working pipeline aimed at instructing GPT in completing shotgun metagenomic research. The pipeline includes instructions for various essential analytic steps: quality controls, host filtering, read classification, abundance estimation, diversity calculation, and data visualization. The pipeline demonstrated successful completion and reproducible results. Alpha diversity measurement is critical to understanding microbiomes. The widely used Faith’s phylogenetic diversity (PD) metric is agnostic of feature abundance and, therefore, falls short of analyzing metagenomic data. BWPDθ, an abundance weighted variant of Faith’s PD, was implemented in scikit-bio alpha diversity metrics. My analysis shows that BWPDθ does have better performance compared to Faith’s PD, revealing more biological significance, and maintaining their robustness at a lower sampling depth. The progression of Alzheimer’s disease (AD) is known to be associated with alterations in the patient’s gut microbiome. Utilizing metagenomic data from the AlzBiom study, I explored the differential abundance of bacterial pncA genes among healthy and AD participants by age group. The analysis showed that there was no significant difference in pncA abundance between the healthy and AD patients. However, when stratified by age group, within the age group 64 to 69, AD was shown to have significantly lower pncA abundance than the healthy control group. The Pearson's test showed a moderate positive association between age and pncA abundance.
ContributorsXing, Zhu (Author) / Zhu, Qiyun (Thesis advisor) / Lim, Efrem (Committee member) / Snyder-Mackler, Noah (Committee member) / Arizona State University (Publisher)
Created2024