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- All Subjects: metabolomics
- Creators: Gu, Haiwei
Description
Metabolomics focuses on the study of metabolic changes occurring in varioussystems and utilizes quantitative and semi-quantitative measurements of multiple metabolites in parallel. Mass spectrometry (MS) is the most ubiquitous platform in this field, as it provides superior sensitivity regarding measurements of complex metabolic profiles in biological systems. When combined with MS, multivariate statistics and advanced machine learning algorithms provide myriad opportunities for bioinformatics insights beyond simple univariate data comparisons. In this dissertation, the application of MS-based metabolomics is introduced with an emphasis on biomarker discovery for human disease detection. To advance disease diagnosis using MS-based metabolomics, numerous statistical techniques have been implemented in this research including principal component analysis, factor analysis, partial least squares-discriminant analysis (PLS-DA), orthogonal PLS-DA, random forest, receiver operating characteristic analysis, as well as functional pathway/enzyme enrichment analyses. These approaches are highly useful for improving classification sensitivity and specificity related to disease-induced biological variation and can help identify useful biomarkers and potential therapeutic targets. It is also shown that MS-based metabolomics can distinguish between clinical and prodromal disease as well as similar diseases with related symptoms, which may assist in clinical staging and differential diagnosis, respectively. Additionally, MS-based metabolomics is shown to be promising for the early and accurate detection of diseases, thereby improving patient outcomes, and advancing clinical care. Herein, the application of MS methods and chemometric statistics to the diagnosis of breast cancer, coccidioidomycosis (Valley fever), and senile dementia (Alzheimer's disease) are presented and discussed. In addition to presenting original research, previous efforts in biomarker discovery will be synthesized and appraised. A Comment will be offered regarding the state of the science, specifically addressing the inefficient model of repetitive biomarker discovery and the need for increased translational efforts necessary to consolidate metabolomics findings and formalize purported metabolic markers as laboratory developed tests. Various factors impeding the translational throughput of metabolomics findings will be carefully considered with respect to study design, statistical analysis, and regulation of biomedical diagnostics. Importantly, this dissertation will offer critical insights to advance metabolomics from a scientific field to a practical one including targeted detection, enhanced quantitation, and direct-to-consumer considerations.
ContributorsJasbi, Paniz (Author) / Johnston, Carol S (Thesis advisor) / Gu, Haiwei (Thesis advisor) / Lake, Douglas F (Committee member) / Sweazea, Karen (Committee member) / Tasevska, Natasha (Committee member) / Arizona State University (Publisher)
Created2022
Description
In the United States, two-thirds of adults are considered hypertensive orprehypertensive. In addition, chronic illness, such as hypertension, cardiovascular disease, and type II diabetes, results in $3.5 trillion in annual healthcare cost and is the primary cause of disability and death. As a result, many individuals seek cheaper and simpler alternatives to combat their conditions. In this exploratory analysis, a study assessing nitrate intake and its effects on vascular function in 39 young adult males was investigated for underlying metabolic variations through a liquid chromatography – mass spectrometry-based large-scale targeted metabolomics approach. A two-way repeated measures ANOVA was used, and 18 significant metabolites were discovered across the time, treatment, and time & treatment groups, including prostaglandin E2 (p<0.001), stearic acid (p=0.002), caprylic acid (p=0.016), pentadecanoic acid (p=0.027), and heptadecanoic acid (p=0.005). In addition, log-transformed principal component analysis and orthogonal partial least squares – discriminant analysis models demonstrated distinct separation among the treatment, control, and time variables. Moreover, pathway and enrichment analyses validated the effect of nitrate intake on the metabolite sets and its possible function in fatty acid oxidation. This better understanding of altered metabolic pathways may help explicate the benefits of nitrate on vascular function and reveal any unknown mechanisms of its supplementation.
ContributorsPatterson, Jeffrey (Author) / Gu, Haiwei (Thesis advisor) / Johnston, Carol (Committee member) / Sweazea, Karen (Committee member) / Arizona State University (Publisher)
Created2020
DescriptionSulforaphane(SFN)isanisothiocyanate(ITC)derivedfromcruciferousvegetables,suchas
broccoli,thatisgrowinginpopularityforitsantioxidantandanti-inflammatorycapabilities.
Furthermore,SFNhasbeendemonstratedtoimproverenalcancercarcinoma(RCC)treatment
outcomesinconjunctionwithmultipleotherformsoftherapy,whichisespeciallyimportant
consideringRCC’spoortherapeuticoutcomeswithchemotherapy.Theaimofthisstudywasto
determinetheeffectsofSFNonRCC invitro utilizingcellviabilityanalysisandLC/MS-MS
targetedmetabolicprofilingtorevealpathwaysresponsibleforSFN’spossibleenhancementof
chemotherapytreatmentinRCC.CCK-8resultsshowthat15 μMofSFNcausedasignificant(p
<0.05)increaseinRCCproliferation.Kruskal-Wallistestsrevealed16metabolitesinourcell,
and28inthemediumtobesignificant(p<0.05).Anorthogonalpartialleastsquares-discriminant
analysis,OPLS-DA,ofsignificantmetaboliteswasusedtocomparedtreatedandnon-treated
samplesforbothdatasetsandshoweda100%predictiveaccuracy(AUC=1).Enrichment
analysisdeterminedthatatotalof7metabolicpathwaysweresignificantlyenriched(VLCFA
β-oxidation,glutamatemetabolism,theureacycle,ammoniarecycling,glycine/serine,alanine,
andglucose-alaninecycle).Pathwayanalysisshowedhistidinemetabolismtobetheonly
significantlyaffectedpathwaybetweenbothdatasets.SFN-inducedmetaboliccharacteristics
foundinRCCwereconsistentwithknownantioxidantandanti-inflammatorypathways.Ourdata
suggeststhatthetherapeuticmechanismsofSFNarelikelyduetointeractionswithTandNKT
cellsthatprotectthemfromoxidativestress.Futureexperimentsregardingantioxidantresearch
incancershouldbecompletely invivo,asopposedto invitro, inordertomaintainthenatural
physiology of cancer cells in the presence of host immune cells.
broccoli,thatisgrowinginpopularityforitsantioxidantandanti-inflammatorycapabilities.
Furthermore,SFNhasbeendemonstratedtoimproverenalcancercarcinoma(RCC)treatment
outcomesinconjunctionwithmultipleotherformsoftherapy,whichisespeciallyimportant
consideringRCC’spoortherapeuticoutcomeswithchemotherapy.Theaimofthisstudywasto
determinetheeffectsofSFNonRCC invitro utilizingcellviabilityanalysisandLC/MS-MS
targetedmetabolicprofilingtorevealpathwaysresponsibleforSFN’spossibleenhancementof
chemotherapytreatmentinRCC.CCK-8resultsshowthat15 μMofSFNcausedasignificant(p
<0.05)increaseinRCCproliferation.Kruskal-Wallistestsrevealed16metabolitesinourcell,
and28inthemediumtobesignificant(p<0.05).Anorthogonalpartialleastsquares-discriminant
analysis,OPLS-DA,ofsignificantmetaboliteswasusedtocomparedtreatedandnon-treated
samplesforbothdatasetsandshoweda100%predictiveaccuracy(AUC=1).Enrichment
analysisdeterminedthatatotalof7metabolicpathwaysweresignificantlyenriched(VLCFA
β-oxidation,glutamatemetabolism,theureacycle,ammoniarecycling,glycine/serine,alanine,
andglucose-alaninecycle).Pathwayanalysisshowedhistidinemetabolismtobetheonly
significantlyaffectedpathwaybetweenbothdatasets.SFN-inducedmetaboliccharacteristics
foundinRCCwereconsistentwithknownantioxidantandanti-inflammatorypathways.Ourdata
suggeststhatthetherapeuticmechanismsofSFNarelikelyduetointeractionswithTandNKT
cellsthatprotectthemfromoxidativestress.Futureexperimentsregardingantioxidantresearch
incancershouldbecompletely invivo,asopposedto invitro, inordertomaintainthenatural
physiology of cancer cells in the presence of host immune cells.
ContributorsHrovat, Jonathan Matthew (Co-author) / Bresette, William (Co-author) / Gu, Haiwei (Thesis director) / Jasbi, Paniz (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Ovarian cancer (OC) is the second most common form of gynecologic cancer and is the most fatal among all forms of gynecologic malignancies. Despite the pivotal role of metabolic processes in the molecular pathogenesis of OC, robust metabolic markers to enable effective screening, rapid diagnosis, accurate surveillance, and therapeutic monitoring of OC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolic profiling approach for the identification of metabolite biomarker candidates that could enable expedited, highly sensitive and specific OC detection. Using this targeted approach, 90 plasma metabolites from many metabolic pathways of potential biological significance were reliably detected and monitored in 218 plasma samples taken from three groups of subjects (78 OC patients, 50 benign samples, and 90 healthy controls). Univariate significance testing and receiver operating characteristic (ROC) analysis revealed 7 metabolites with high predictive accuracy [area under curve (AUC) > 0.90] for distinguishing healthy controls from OC patients. The results of our multivariate model development informed the construction of a 5-metabolite panel of potential plasma biomarkers for enhanced discrimination of OC samples from benign specimens, exhibiting roughly 75% predictive accuracy using a 50% random-split training set. ROC analysis that was generated based on a logistic regression classifier showed enhanced classification performance relative to individual metabolites, with more than 75% accuracy using a testing data set for external validation. Pathway analysis revealed significant disturbances in glycine, serine, and threonine metabolism; glyoxylate and dioxylate metabolism; the pentose phosphate pathway; and histidine metabolism. The results expand basic knowledge of the metabolome related to OC pathogenesis relative to healthy controls and benign samples, revealing potential pathways or markers that can be targeted therapeutically. This study also provides a promising basis for the development of larger multi-site projects to validate our findings across population groups and further advance the development of improved clinical care for OC patients.
ContributorsTurner, Cassidy D (Author) / Gu, Haiwei (Thesis director) / Shi, Xiaojian (Committee member) / School of Life Sciences (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Impairments to mitochondrial function and metabolism can make neurons vulnerable to stress and degeneration. Several studies have shown that aberrations in the electron transport chain (ETC) and the Krebs cycle are involved in the pathogenesis of Parkinson’s disease (PD). Therefore, targeting these pathways is becoming increasingly important in the discovery of new treatment for neurodegenerative diseases like PD. (−)-epigallocatechin-3-gallate (EGCG), the most common polyphenol found in Green tea, has been shown to exert neuroprotective effects and lower the risk of developing PD. However, the mechanism by which it accomplishes this remains to be elucidated. The purpose of this study was to shed light on these mechanisms by exploring the effects of EGCG against MPP+-induced mitochondrial dysfunction with PC12 cells being used as a PD pathological cell model. The cell viability differences between cells treated with varying combinations of MPP+ and EGCG were measured using a CCK-8 assay. The morphology changes induced by the different treatments were then identified with fluorescence microscopy. Next, a Seahorse assay was carried out to investigate mitochondrial function followed by GC-MS and LC-MS analysis to evaluate mitochondrial metabolism. 13C metabolic flux analysis was then used to trace the metabolic flux of the Krebs cycle. The results of the CCK-8 assay and fluorescence microscopy showed that EGCG helps attenuate the decreased viability of PC12 cells as well as the morphology changes induced by MPP+. The Seahorse and GC-MS assay found that the it also helps prevent impaired mitochondrial respiration caused by MPP+. The impaired mitochondrial respiration manifested as alterations to the Krebs cycle and glycolysis. In addition, 13C metabolic flux analysis revealed significant increases in Krebs cycle activity in MPP+-induced PC12 cells if treated with EGCG beforehand. Moreover, LC-MS showed a distinct metabolite profile for each group and identified 26 potent biomarkers. In conclusion, this study demonstrated that EGCG exerts a neuroprotective effect on PC12 cells and helps maintain mitochondrial metabolic balance in the presence of MPP+.
ContributorsLawrence, Kent Alexander (Author) / Gu, Haiwei (Thesis director) / Lake, Douglas (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05