282 research outputs found
Utilizing untargeted metabolomics to characterize microbial communities and identify biomarkers of an unhealthy state
The metabolism of microbial communities is extremely complex, having contributions from multiple species as well as the host. The metabolome (the complete set of detectable small molecules in a given environment) offers a window into the culmination of these events. The goal of this thesis was to apply metabolomics to improve our understanding of the metabolism of microbial communities, with specific focus on the vaginal microbiota.
A combination of analytical chemistry techniques were employed to profile the vaginal metabolome of women with a dysbiotic vaginal microbiota, termed Bacterial Vaginosis (BV). The vaginal metabolome was closely associated with bacterial diversity and women with BV had a distinct metabolic profile compared to healthy women (N= 131). A number of novel biomarkers were identified, the most sensitive and specific being gamma-hydroxybutyrate (GHB) and 2-hydroxyisovalerate (2HV). These biomarkers were validated in three independent cohorts of diverse geographical locations and ethnicities. Correlations between the microbiota and metabolome identified putative microbe-product relationships, including production of GHB by Gardnerella vaginalis which was confirmed in vitro. Combining these data with meta-transcriptome information, metabolites could be linked to specific transcripts and microbes with increased confidence. The fibronectin binding capabilities of Lactobacillus iners, the most prevalent species in the vagina, was also investigated and confirmed.
To extend the tools developed during investigations of the vaginal microbiota to other systems, a study of stool and plasma samples from children with severe acute malnutrition (SAM) was conducted. Although the stool microbiota and metabolome did not discriminate children with SAM from controls, a number of metabolites differed significantly in plasma. Most of these metabolites had not been associated with SAM previously, including oxylipins, 2C6-disaccharides, truncated fibrinopepetides, and heme. These metabolic perturbations provide novel insight into the pathogenesis of SAM, and could serve as predictors of mortality/recovery and enteropathy. This study also led to the development of a novel method to filter out salt cluster artefacts in LC-MS metabolomics data using mass defect filtering.
Collectively, these studies have demonstrated how analytical chemistry, computational biology and microbiology can be integrated to advance our understanding of the metabolism of the microbiome and identify novel biomarkers of disease
Role of Asparagine as a Nitrogen Signal and Characterization of a Nitrogen Responsive Glutamine Amidotransferase, GAT1_2.1 in Arabidopsis thaliana
Maintaining the proper balance between carbon (C) and nitrogen (N) metabolism is critical to the sustained growth of organisms. In plant leaves, this balance is achieved by photoperiod dependent cross-talk between the processes of photosynthesis, respiration, and amino acid metabolism. A crucial mechanism in maintaining C/N balance is the GS/GOGAT cycle, which is well known to serve as a cross-road between C and N metabolism. Importantly, non-photosynthetic tissues (e.g. roots, germinating seeds) lack a sufficient supply of carbon skeletons under high N conditions and hence may resort to other mechanisms, along with the GS/GOGAT cycle, to achieve proper C/N balance. Our understanding of the pathways involved in this aspect of plant regulation is limited. Considering the importance of asparagine as a major storage form of nitrogen, this study examines C and N partitioning within Arabidopsis roots upon asparagine treatment. Based on this work, I propose a role for the enzyme GAT1_2.1 in hydrolyzing excess glutamine to glutamic acid (Glu), which may serve as a carbon skeleton for channeling C to the TCA cycle under high N conditions. GAT1_2.1, a gene coding for a class I glutamine amidotransferase of unknown substrate specificity, was shown to be highly responsive to N status and has a root specific expression in Arabidopsis. The protein localizes to the mitochondria and the gene is found to be highly co-expressed with Glutamate Dehydrogenase 2 (GDH2). Metabolite profiling data using a gat1_2.1 mutant of Arabidopsis suggests that, in the absence of GAT1_2.1, the GABA shunt pathway is activated to replenish the depleted levels of Glu. This Glu may then be deaminated to 2-oxoglutarate by GDH2 and channeled into the TCA cycle, thus providing a cross-roads between C and N metabolism in root mitochondria. In addition to this work, I also elucidate optimal methods for reliable metabolomics experiments and propose the use of isotopic labelling for the detection of unknown pathways
Early Detection of Cystic Fibrosis Acute Pulmonary Exacerbations by Exhaled Breath Condensate Metabolomics
The most common cause of death in cystic fibrosis (CF) patients is progressive lung function decline, which is punctuated by acute pulmonary exacerbations (APEs). A major challenge is to discover biomarkers for detecting an oncoming APE and allow for pre-emptive clinical interventions. Metabolic profiling of exhaled breath condensate (EBC) samples collected from CF patients before, during, and after APEs and under stable conditions (n = 210) was performed using ultraperformance liquid chromatography (UPLC) coupled to Orbitrap mass spectrometry (MS). Negative ion mode MS data showed that classification between metabolic profiles from "pre-APE" (pending APE before the CF patient had any signs of illness) and stable CF samples was possible with good sensitivities (85.7 and 89.5%), specificities (88.4 and 84.1%), and accuracies (87.7 and 85.7%) for pediatric and adult patients, respectively. Improved classification performance was achieved by combining positive with negative ion mode data. Discriminant metabolites included two potential biomarkers identified in a previous pilot study: Lactic acid and 4-hydroxycyclohexylcarboxylic acid. Some of the discriminant metabolites had microbial origins, indicating a possible role of bacterial metabolism in APE progression. The results show promise for detecting an oncoming APE using EBC metabolites, thus permitting early intervention to abort such an event.Fil: Zang, Xiaoling. Georgia Institute of Techology; Estados UnidosFil: Monge, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; ArgentinaFil: Gaul, David A.. Georgia Institute of Techology; Estados UnidosFil: McCarty, Nael A.. University of Emory; Estados UnidosFil: Stecenko, Arlene. University of Emory; Estados UnidosFil: Fernández, Facundo M.. Georgia Institute of Techology; Estados Unido
Characterisation of xenometabolome signatures in complex biomatrices for enhanced human population phenotyping
Metabolic phenotyping facilitates the analysis of low molecular weight compounds in complex biological samples, with resulting metabolite profiles providing a window on endogenous processes and xenobiotic exposures. Accurate characterisation of the xenobiotic component of the metabolome (the xenometabolome) is particularly valuable when metabolic phenotyping is used for epidemiological and clinical population studies where exposure of participants to xenobiotics is unknown or difficult to control/estimate. Additionally, as metabolic phenotyping has increasingly been incorporated into toxicology and drug metabolism research, phenotyping datasets may be exploited to study xenobiotic metabolism at the population level. This thesis describes novel analytical and data-driven strategies for broadening xenometabolome coverage to allow effective partitioning of endogenous and xenobiotic metabolome signatures.
The data driven strategy was multi-faceted, involving the generation of a reference database and the application of statistical methodologies. The database contains over 100 common xenobiotics profiles - generated using established liquid chromatography-mass-spectrometry methods – and provided the basis for an empirically derived screen for human urine and blood samples. The prevalence of these xenobiotics was explored in an exemplar phenotyping dataset (ALZ; n = 650; urine), with 31 xenobiotics detected in an initial screen. Statistical based methods were tailored to extract xenobiotic-related signatures and evaluated using drugs with well-characterised human metabolism.
To complement the data-driven strategies for xenometabolome coverage, a more analytical based strategy was additionally developed. A dispersive solid phase extraction sample preparation protocol for blood products was optimised, permitting efficient removal of lipids and proteins, with minimal effect on low molecular weight metabolites. The suitability and reproducibility of this method was evaluated in two independent blood sample sets (AZstudy12; n=171, MARS; n=285).
Finally, these analytical and statistical strategies were applied to two existing large-scale phenotyping study datasets: AIRWAVE (n = 3000 urine, n=3000 plasma samples) and ALZ (n= 650 urine, n= 449 serum) and used to explore both xenobiotic and endogenous responses to triclosan and polyethylene glycol exposure. Exposure to triclosan highlighted affected pathways relating to sulfation, whilst exposure to PEG highlighted a possible perturbation in the glutathione cycle.
The analytical and statistical strategies described in this thesis allow for a more comprehensive xenometabolome characterisation and have been used to uncover previously unreported relationships between xenobiotic and endogenous metabolism.Open Acces
Investigating mycotoxins and secondary metabolites in Canadian agricultural commodities using high-resolution mass spectrometry
Mycotoxins are secondary metabolites produced by fungi, which are harmful to humans and/or animals. Alternaria is a common fungal plant pathogen that can produce mycotoxins in agricultural commodities, such as processed wheat products, and fruit juices. Liquid chromatography mass spectrometry (LC-MS) is commonly used for the detection, characterization and quantitation of mycotoxins in agricultural products. Canadian Alternaria species were assessed to provide a risk assessment of their secondary metabolites in agricultural products by describing their global metabolome data obtained from the Orbitrap LC-MS instrument. Combination of this high mass accuracy data with post data-acquisition analysis techniques resulted in the discovery of new conjugated mycotoxins and secondary metabolites produced by Canadian species of Alternaria.
Data independent acquisition-digital archiving (DIA-DA) was applied as a non-targeted approach for metabolomic profiling of naturally-contaminated silage. DIA-DA allowed for high quality retrospective sample analysis of high resolution LC-MS data with high analyte selectivity
Computational Tools for the Processing and Analysis of Time-course Metabolomic Data
Modern, high-throughput techniques for the acquisition of metabolomic
data, combined with an increase in computational power, have provided
not only the need for, but also the means to develop and use, methods for
the interpretation of large and complex datasets. This thesis investigates
the methods by which pertinent information can be extracted from nontargeted
metabolomic data and reviews the current state of chemometric
methods. The analysis of real-world data and research questions relevant
to the agri-food industry reveals several problems for which novel solutions
are proposed. Three LC-MS datasets are studied: Medicago, Alopecurus
and aged Beef, covering stress resistance, herbicide resistance and product
misbranding. The new methods include preprocessing (batch correction,
data-filtering), processing (clustering, classification) and visualisation and
their use facilitated within a flexible data-to-results pipeline. The resulting
software suite with a user-friendly graphical interface is presented, providing
a pragmatic realisation of these methods in an easy to access workflow
The Application and Development of Metabolomics Methodologies for the Profiling of Food and Cellular Toxicity
Metabolomics is a rapidly growing field of study. Its growth reflects advancements in technology and an improved understanding of the impact of the environment on metabolism. As a result, metabolomics is now commonly employed to investigate and characterize human and plant metabolism. The first chapter of this thesis provides an introduction to metabolomics and an overview of the protocols for sample preparation, data collection and statistical analysis. The second thesis chapter describes in explicit detail the step-by-step process of extracting and analyzing metabolites collected from mammalian cells, specifically brain tissue with a focus on Parkinson’s disease. The chapter highlights important factors to consider including experiment design, sample collection, and data processing. Chapters 3 and 4 include the application of metabolomics to evaluate how the metabolome responds to the environment. Chapter 3 focuses on the neuronal response to the xenobiotic arsenic. It demonstrates how astrocytes increase glutathione production through an up regulation of the citric acid cycle and glycolytic processes. Arsenic was also observed to decreases related metabolites including citrate and lactate. These metabolites are important intermediates to ATP production and illustrate the interconnection of metabolomic processes. Chapter 4 shows how metabolite profiles can be used to evaluate the impact of environmental conditions on wines. Metabolite profiles of Pinot Noir derived from the same scion clone (Pinot noir 667) and grown in different regions along the Pacific coast were compared. NMR and a differential sensing array were used to profile the chemical composition of the samples. We observed how environmental conditions resulted in different metabolite profiles in the various wine samples. This thesis aims to highlight the application of metabolomic to various biological studies in order to evaluate the impact of external stimuli.
Advisor: Robert Power
Therapeutic drug monitoring, clinical metabolomics and pharmacometabolomics via solid phase microextraction (SPME): The first step towards an alternative rapid diagnostic tool
Personalized medicine is a branch of medicine that focuses on how a prescribed therapeutic treatment affects a specific individual as opposed to its general effects for the broader population. The goal of personalized medicine is to improve patient care by enabling concentrations of a therapeutic drug to be monitored in various biological compartments, while also measuring their effects in relation to the administered dose via therapeutic drug monitoring (TDM). Metabolomics—the study of all small endogenous and exogenous molecules within a cell, tissue, or organism—has recently been proposed as a method for developing patient-based metabolic profiles, which could enable clinicians to more effectively predetermine suitable courses of treatment for a variety of patients. The probability of success or failure for a given treatment is determined in large part by metabolic phenotyping, which considers several patient-based influential factors, such as age, diet, environment, and medical history. This approach allows treatment to be tailored to the needs of each individual patient, thereby avoiding under- or over-dosing or wasting time with unnecessary treatment options, which often occurs as a result of the current “trial and error” approach to personalized therapy. In this thesis, solid phase microextraction (SPME) coupled with liquid chromatography-mass spectrometry (LC-MS) is proposed as an alternative sample preparation tool for use in the field of personalized medicine. To this end, the work in this thesis presents the development of various SPME-based methods for TDM, and it explores SPME-based clinical metabolomics and proof-of-concept pharmacometabolomics for a range of biological matrices typically encountered in clinical practice, such as whole blood, serum, plasma, urine, and lung tissue. Furthermore, SPME is proposed as a practical tool for rapid diagnostics, as it can be directly coupled to sensitive detection methods like MS. While a number of preliminary steps are required before important diagnostic markers can be monitored—including the validation of these potential respective candidate biomarkers, which is already a major inherent challenge in metabolomics—the use of SPME for real-time TDM and point-of-care analysis of important metabolic markers remains feasible. This thesis consists of a brief introduction and 6 experimental chapters, with each successive chapter exploring increasingly complex samples of interest and discussing the challenges and limitations associated with their analysis. Moreover, each subsequent chapter also addresses the difficulties associated with performing solely TDM or metabolomics separately and how, particularly in vivo SPME, can overcome these challenges and be used to achieve both goals (TDM and metabolomics) simultaneously under even more complicated and dynamic circumstances. Specifically, Chapter 2 focuses on the therapeutic drug monitoring of TXA in plasma and urine samples from patients with chronic renal dysfunction who are undergoing cardiac surgery, while Chapter 3 presents a metabolomics study entailing the profiling of serum samples from various psoriatic patients. Chapters 4, 5, and 6 illustrate how SPME can be used to enable simultaneous TDM and metabolomics under more complicated and dynamic circumstances by using in vivo SPME for specifically tissue analysis. Chapter 4 explores lung tissue and perfusate metabolomics using a pre-clinical porcine model undergoing normothermic ex vivo lung perfusion (NEVLP). In contrast, Chapters 5 and 6 assess the use of in vivo SPME for the TDM of chemotherapy drugs administered via in vivo lung perfusion (IVLP) in pre-clinical porcine model (Chapter 5) and clinical human trial settings (Chapter 6), followed by proof-of-concept pharmacometabolomics. Finally, the potential use of SPME as a rapid diagnostic tool is showcased in Chapter 7—which shows the rapid analysis of TXA from plasma—concluding the thesis by further demonstrating that the dual goals of TDM and point-of-care testing for metabolic markers can be achieved with rapid analysis via the direct coupling of SPME to MS
COMPUTATIONAL AND INTEGRATIVE OMICS APPROACHES TO STUDY THE EFFECT OF PERTURBATIONS ON METABOLIC PHENOTYPES
Ph.DDOCTOR OF PHILOSOPH
Sample Preparation in Metabolomics
Metabolomics is increasingly being used to explore the dynamic responses of living systems in biochemical research. The complexity of the metabolome is outstanding, requiring the use of complementary analytical platforms and methods for its quantitative or qualitative profiling. In alignment with the selected analytical approach and the study aim, sample collection and preparation are critical steps that must be carefully selected and optimized to generate high-quality metabolomic data. This book showcases some of the most recent developments in the field of sample preparation for metabolomics studies. Novel technologies presented include electromembrane extraction of polar metabolites from plasma samples and guidelines for the preparation of biospecimens for the analysis with high-resolution μ magic-angle spinning nuclear magnetic resonance (HR-μMAS NMR). In the following chapters, the spotlight is on sample preparation approaches that have been optimized for diverse bioanalytical applications, including the analysis of cell lines, bacteria, single spheroids, extracellular vesicles, human milk, plant natural products and forest trees
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