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Urologic diseases interstitial cystitis (IC), overactive bladder (OAB), and urinary tract infection (UTI) affect tens of millions of people per year in the US alone. The human microbiome consists of a diverse community of bacteria (bacteriome) and viruses (virome) harbored in each individual that contributes to health and disease. Little

Urologic diseases interstitial cystitis (IC), overactive bladder (OAB), and urinary tract infection (UTI) affect tens of millions of people per year in the US alone. The human microbiome consists of a diverse community of bacteria (bacteriome) and viruses (virome) harbored in each individual that contributes to health and disease. Little is known about how the microbiome impacts urinary disorders. Using next-generation metagenomic sequencing, we characterized the urinary bacteriome and virome of patients with urinary disorders (IC, OAB, and UTI) and healthy controls. We show that the bacteriome was distinctly altered in patients by their respective urinary disorder. IC was characterized by a distinct prevalence of the genus Lactobacillus, while OAB was characterized by the genus Bacteroides, and UTI was characterized by Comamonas. IC, OAB, and UTI all also had significantly differed virome profiles from healthy individuals. In particular, we found that Lactobacillus phages were significantly associated with IC and Corynebacterium virus was associated with UTI samples, meanwhile no particular virus was correlated with OAB samples. Overall, we show that changes in the urinary microbiome are associated with incidence and spectrum of urinary diseases. These findings could lead to new microbiome modalities of treatment.

ContributorsBains, Ajeet (Author) / Lim, Efrem (Thesis director) / Zhu, Qiyun (Committee member) / Kaelin, Emily (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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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The study of organismal adaptations oftentimes focuses on specific, constant conditions, but environmental parameters are characterized by more or less marked levels of variability, rather than constancy. This is important in environments like soils where microbial activity follows pulses of water availability driven by precipitation. Nowhere are these pulses more

The study of organismal adaptations oftentimes focuses on specific, constant conditions, but environmental parameters are characterized by more or less marked levels of variability, rather than constancy. This is important in environments like soils where microbial activity follows pulses of water availability driven by precipitation. Nowhere are these pulses more variable and unpredictable than in arid soils. Pulses constitute stressful conditions for bacteria because they cause direct cellular damage that must be repaired and they force cells to toggle between dormancy and active physiological states, which is energetically taxing. I hypothesize that arid soil microorganisms are adapted to the variability in wet/dry cycles itself, as determined by the frequency and duration of hydration pulses. To test this, I subjected soil microbiomes from the Chihuahuan Desert to controlled incubations for a total common growth period of 60 hours, but separated into treatments in which the total active time was reached with hydration pulses of different length with intervening periods of desiccation, so as to isolate pulse length and frequency as the varying factors in the experiment. Using 16S rRNA amplicon data, I characterized changes in microbiome growth, diversity, and species composition, and tracked the individual responses to treatment intensity in the 447 most common bacterial species (phylotypes) in the soil. Considering knowledge of extremophile microbiology, I hypothesized that growth yield and diversity would decline with shorter pulses. I found that microbial diversity was indeed a direct function of pulse length, but surprisingly, total yield was an inverse function of it. Pulse regime treatments resulted in progressively more significant differences in community composition with increasing pulse length, as differently adapted phylotypes became more prominent. In fact, more than 30% of the most common bacterial phylotypes demonstrated statistically significant population growth responses to pulse length. Most responsive phylotypes were apparently best adapted to short pulse regimes (known in the literature as Nimble Responders or NIRs), while fewer did better under long pulse regimes (known as TORs or Torpid Responders). Examples of extreme NIRs and TORs could be found among bacteria from different phyla, indicating that these adaptations have occurred multiple times during evolution.
ContributorsKut, Patrick John (Author) / Garcia-Pichel, Ferran (Thesis advisor) / Sala, Osvaldo (Committee member) / Zhu, Qiyun (Committee member) / Arizona State University (Publisher)
Created2023
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Current data indicates that between 10-20% of patients that receive renal transplants experience acute rejection. Acute rejection is an important predictor of future complications that can lead to complete graft loss. Serine Protease Inhibitor proteins (Serpins) have shown incredible promise in working to lessen or inhibit the onset and effects

Current data indicates that between 10-20% of patients that receive renal transplants experience acute rejection. Acute rejection is an important predictor of future complications that can lead to complete graft loss. Serine Protease Inhibitor proteins (Serpins) have shown incredible promise in working to lessen or inhibit the onset and effects of acute rejection in multiple solid organ transplant studies. However, it has been proposed that the mechanisms of Serp-1 immunomodulation can be affected by the native gut microbiome of recipient animals. This study utilizes murine renal allograft transplant from C57BL/6 to BALB/c and microbiome depletion to model the acute rejection response and to determine if there are interactions between the gut microbiome and Serp- 1 during the acute renal allograft rejection response. After antibiotic administration, expected decreases in richness and diversity in the designated group were observed. After renal subcapsular allograft transplant, histological and microbiome analyses revealed neither significant microbiome changes persistent after surgery based on pre-treatment nor changes associated with the administration of the isolated Serp-1 protein. Additionally, it was found that Serp-1 improves the histological presentation of acute renal allograft rejection regardless of the status of the microbiome.
ContributorsKokott, Kristiana (Author) / Lim, Efrem S (Thesis advisor) / Lucas, Alexandra R (Committee member) / Zhu, Qiyun (Committee member) / Arizona State University (Publisher)
Created2022
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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