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Description
Valley Fever (VF), is a potentially lethal fungal pneumonia caused by Coccidioides spp., which is estimated to cause ~15-30% of all community-acquired pneumonias in the highly endemic Greater Phoenix and Tucson areas of Arizona. However, an accurate antigen-based diagnostic is still lacking. In order to identify protein and glycan antigen biomarkers of infection, I used a combination of genomics, proteomics and glycomics analyses to provide evidence of genus-specific proteins and glycosylations. The next goal was to determine if Coccidioides-specific glycans were present in biological samples from VF patients. Urine collected from 77 humans and 63 dogs were enriched for glycans and evaluated by mass spectrometry for Coccidioides-specific glycans and evaluated against a panel of normal donor urines, urines from patients infected with other fungi, and fungal cultures from closely related pneumonia-causing fungi. A combination of 6 glycan biomarkers was 100% sensitive and 100% specific in the diagnosis of human VF subjects, while only 3 glycan biomarkers were needed for 100% sensitivity and 100 specificity in the diagnosis of dog VF subject. Additionally, a blinded trial of 23 human urine samples was correctly able to classify urine samples with 93.3% sensitivity and 100% specificity. The results of this research provides evidence that Coccidioides genus-specific glycosylations have potential as antigens in diagnostic assays.
ContributorsMitchell, Natalie M (Author) / Lake, Douglas F (Thesis advisor) / Bean, Heather D (Committee member) / Grys, Thomas E (Committee member) / Magee, Dewey M (Committee member) / Arizona State University (Publisher)
Created2019
Description
Coccidioidomycosis, or valley fever (VF), is a fungal infection caused by Coccidioides that is highly endemic in southern Arizona and central California. The antibody response to infection in combination with clinical presentation and radiographic findings are often used to diagnose disease, as a highly sensitive and specific antigen-based assay has yet to be developed and commercialized. In this dissertation, a panel of monoclonal antibodies (mAbs) was generated in an attempt to identify circulating antigen in VF-positive patients. Despite utilizing a mixture of antigens, almost all mAbs obtained were against chitinase 1 (CTS1), a protein previously identified as a main component in serodiagnostic reagents. While CTS1 was undoubtedly a dominant seroreactive antigen, it was not successfully detected in circulation in patient samples prompting a shift toward further understanding the importance of CTS1 in antibody-based diagnostic assays. Interestingly, depletion of this antigen from diagnostic antigen preparations resulted in complete loss of patient IgG reactivity by immunodiffusion. This finding encouraged the development of a rapid, 10-minute point-of-care test in lateral flow assay (LFA) format to exclusively detect anti-CTS1 antibodies from human and non-human animal patients with coccidioidal infection. A CTS1 LFA was developed that demonstrated 92.9% sensitivity and 97.7% specificity when compared to current quantitative serologic assays (complement fixation and immunodiffusion). A commercially available LFA that utilizes a proprietary mixture of antigens was shown to be less sensitive (64.3%) and less specific (79.1%). This result provides evidence that a single antigen can be used to detect antibodies consistently and accurately from patients with VF. The LFA presented here shows promise as a helpful tool to rule-in or rule-out a diagnosis of VF such that patients may avoid unnecessary antibacterial treatments, improving healthcare efficiency.
ContributorsGrill, Francisca J (Author) / Lake, Douglas F (Thesis advisor) / Magee, D Mitch (Committee member) / Grys, Thomas (Committee member) / Chen, Qiang (Committee member) / Arizona State University (Publisher)
Created2023
Description
For untargeted volatile metabolomics analyses, comprehensive two-dimensional gas chromatography (GC×GC) is a powerful tool for separating complex mixtures and can provide highly specific information about the chemical composition of a variety of samples. With respect to human disease, the application of GC×GC in untargeted metabolomics is contributing to the development of diagnostics for a range of diseases, most notably bacterial infections. Pseudomonas aeruginosa, in particular, is an important human pathogen, and for individuals with cystic fibrosis (CF), chronic P. aeruginosa lung infections significantly increase morbidity and mortality. Developing non-invasive tools that detect these infections earlier is critical for improving patient outcomes, and untargeted profiling of P. aeruginosa volatile metabolites could be leveraged to meet this challenge. The work presented in this dissertation serves as a case study of the application of GC×GC in this area.Using headspace solid-phase microextraction and time-of-flight mass spectrometry coupled with GC×GC (HS-SPME GC×GC-TOFMS), the volatile metabolomes of P. aeruginosa isolates from early and late chronic CF lung infections were characterized. Through this study, the size of the P. aeruginosa pan-volatilome was increased by almost 40%, and differences in the relative abundances of the volatile metabolites between early- and late-infection isolates were identified. These differences were also strongly associated with isolate phenotype. Subsequent analyses sought to connect these metabolome-phenome trends to the genome by profiling the volatile metabolomes of P. aeruginosa strains harboring mutations in genes that are important for regulating chronic infection phenotypes. Subsets of volatile metabolites that accurately distinguish between wild-type and mutant strains were identified. Together, these results highlight the utility of GC×GC in the search for prognostic volatile biomarkers for P. aeruginosa CF lung infections.
Finally, the complex data sets acquired from untargeted GC×GC studies pose major challenges in downstream statistical analysis. Missing data, in particular, severely limits even the most robust statistical tools and must be remediated, commonly through imputation. A comparison of imputation strategies showed that algorithmic approaches such as Random Forest have superior performance over simpler methods, and imputing within replicate samples reinforces volatile metabolite reproducibility.
ContributorsDavis, Trenton James (Author) / Bean, Heather D (Thesis advisor) / Haydel, Shelley E (Committee member) / Lake, Douglas F (Committee member) / Runger, George C (Committee member) / Arizona State University (Publisher)
Created2022