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Metabolomics is a powerful science that can be applied for the discovery of disease biomarkers, and investigation of altered metabolomes due to abiotic and biotic perturbations. This dissertation is focused on untargeted metabolomic applications to investigate fungal metabolite alterations associated with pathogenicity, fungal disease propagations, and symbiosis. This dissertation employs qualitative analysis of metabolite mixtures using HS-SPME coupled GC-MS and TMS derivatization followed by GC-MS analytical platforms. In the first study, we discovered a biomarker combination to diagnose fungal soft tissue disease in sweet potato at an early stage of disease propagation. We used an HS-SPME GC-MS untargeted metabolomics workflow to analyze the VOC associated with Rhizopus stolonifer infected and healthy sweet potatoes in situ and simulated warehouse environments. A single combination of 4 biomarkers was able to diagnose R. stolonifer fungal soft tissue disease (AUC = 0.980, 95% C.I. 0.937-1) and the early stage of the fungal soft rot disease (AUC = 0.999, 95% C.I. 0.978-1). We were able to detect the biomarkers: 1- propanol, ethyl alcohol, ethyl propionate and 3-methyl-3- buten-1-ol during disease progression in a simulated warehouse environment. Therefore, this study shows the feasibility of early diagnosis of fungal soft tissue disease by a real-time screening of volatile profiles of sweet potato in post-harvest storage. When considering the study of a particular species metabolome, it is crucial to develop a metabolite extraction protocol. In the second study, the performance of the six different metabolite extraction solvents mixtures was tested with the preferred mix being: butanol:methanol:water (2:1:1, v/v at -20 °C) which was used as a single solvent mix to extract both polar and relatively non-polar metabolites simultaneously in a single extraction step. The Macrophomina phaseolina fungal metabolome was investigated using the solvent mix. Finally, fungal mutualism was studied using untargeted metabolomics. Most often mycorrhizal metabolomics workflows are based on analyzing the Arbuscular Mycorrhizae colonized root metabolome. But here, we used hyphal materials to examine the mutualistic symbiotic association of the AM fungi. All untargeted metabolomic studies included chemometric data analysis and specific biomarkers and or metabolites were determined using multivariate statistics or prediction model building and validating.

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Understanding the complex metabolic networks present in organisms, through the use of high throughput liquid chromatography coupled mass spectrometry, will give insight into the physiological changes responding to stress. However the lack of a proper work flow and robust methodology hinders verifiable biological interpretation of mass profiling data. In this study a novel workflow has been developed. A novel Kernel based feature alignment algorithm, which outperformed Agilent’s Mass profiler and showed roughly a 20% increase in alignment accuracy, is presented for the alignment of mass profiling data. Prior to statistical analysis post processing of data is carried out in two stages, noise filtering is applied to consensus features which were aligned at a 50% or higher rate. Followed by missing value imputation a method was developed that outperforms both at model recovery and false positive detection. The use of parametric methods for statistical analysis is inefficient and produces a large number of false positives. In order to tackle this three non-parametric methods were considered. The histogram method for statistical analysis was found to yield the lowest false positive rate. Data is presented which was analysed using these methods to reveal metabolomic changes during plant pathogenesis. A high resolution time series dataset was produced to explore the infection of Arabidopsis thaliana by the (hemi) biotroph Pseudomonas syringe pv tomato DC3000 and its disarmed mutant DC3000hrpA, which is incapable of causing infection. Approximately 2000 features were found to be significant through the time series. It was also found that by 4h the plants basal defence mechanism caused the significant ‘up-regulation’ of roughly 400 features, of which 240 were found to be at a 4-fold change. The identification of these features role in pathogenesis is supported by the fact that of those features found to discriminate between treatments a number of pathways were identified which have previously been documented to be active due to pathogenesis

As espécies do gênero Viguiera Kunth, recentemente transferidas para Aldama La Llave (Asteraceae, Heliantheae), ainda possuem problemas de delimitação taxonômica. Tal gênero possui 35 espécies distribuídas por todo o território brasileiro, principalmente no cerrado. Análises químicas têm demonstrado seu potencial em auxiliar na resolução de problemas em vários níveis taxonômicos com base em grupos químicos de metabólitos secundários. Além disso, análises fitoquímicas tem revelado que determinadas substâncias de Aldama possuem potencial para serem estudadas tendo vista a investigação de mecanismos moleculares anti-inflamatórios. Desta forma, foi proposto neste trabalho realizar a abordagem da metabolômica não-direcionada auxiliada por quimiometria, visando-se fornecer dados químicos tanto para contribuições quimiotaxonômicas quanto para encontrar substâncias bioativas. Por meio de tal abordagem, análises multivariadas não supervisionadas (PCA e HCA), bem como supervisionadas (OPLS-DA), com dados provenientes de UHPLC-DAD-Orbitrap, demonstraram que o gênero Aldama é quimioconsistente e, deste modo, determinadas substâncias químicas discriminantes foram sugeridas. Além disso, espécies que apresentam problemas taxonômicos tiveram os seus posicionamentos infragenéricos explicado do ponto de vista quimiotaxonômico. Quanto às análises para encontrar substâncias bioativas (fitoquímica direcionada), os alvos inflamatórios pesquisados foram as enzimas pró-inflamatórias COX-1 e 5-LOX, com as quais se realizou ensaios in vitro verificando-se a atividade positiva de extratos de espécies de Aldama. A. trichophylla foi a espécie selecionada para realizar a fitoquímica direcionada, uma vez que apresentou inibição dupla contra as enzimas e por se tratar de uma espécie muito pouco estudada do ponto de vista fitoquímico. Os melhores modelos de inibição de COX-1 e 5-LOX criados com dados químicos de Aldama foram selecionados a partir de diversas combinações de algoritmos, softwares de processamento (MZmine, MetAlign e MSClust) e técnicas-hifenadas, tais como UHPLC-Orbitrap e HPLC-TOF. Deste modo, determinados picos de íons foram apontados pela quimiometria como sendo discriminantes para a atividade de inibição dupla. Tais picos foram desreplicados com base em seus perfis de UV e padrões de fragmentação via HCD-Orbitrap e Ion-Trap. Deste modo, foi possível desreplicar três prováveis substâncias químicas inibidoras de COX-1 e 5-LOX: kaempferol-3-O-glucuronideo, quercetina-3-O-metil-7-glucuronideo e kaempferol-3-O-(6\"-malonil-glucosideo). Por fim, foi realizada a fitoquímica direcionada. O fracionamento cromatográfico permitiu isolar tais substâncias e uma análise preliminar de RMN 1H foi realizada com o intuito de realizar a identificação estrutural. Tais substâncias terão a suas atividades confirmadas na inibição das duas enzimas em um futuro próximo. / The species of the genus Viguiera Kunth, recently transferred to the genus Aldama La Llave (Asteraceae, Heliantheae), still show problems of taxonomic boundaries. This genus has 35 species distributed throughout the Brazilian territory, mostly in the cerrado. Chemical analyses has demonstrated its potential to help in the solution of problems at various taxonomic levels based on chemical groups of secondary metabolites. Furthermore, phytochemical analyses have shown that certain compounds from Aldama have the potential to be studied aiming to investigate anti-inflammatory molecular mechanisms. Thus, in this work it was proposed to carry out the untargeted metabolomic approach aided by chemometrics, aiming to provide chemical data either for chemotaxonomic contributions and to find bioactive compounds. Through this approach, both unsupervised (PCA and HCA) and supervised multivariate analysis (OPLS-DA) combined with data from UHPLC-DAD-Orbitrap analyses showed that the genus Aldama is \"chemoconsistent\" and thus certain discriminant compounds were suggested. Additionally, some species with taxonomic problems had their infrageneric positioning explained from the chemotaxonomic point of view of. With regards to the analyses carried out to find bioactive compounds (targeted phytochemistry), the inflammatory targets investigated in this study were the COX-1 and LOX-5 pro-inflammatory enzymes with which in vitro assays were made and positive activity of extracts from species of Aldama was observed. A. trichophylla was the selected species for the targeted phytochemistry because it showed dual inhibition activity against both enzymes and it is still little investigated from the chemical point of view. The best models for inhibition of COX-1 and 5-LOX obtained with chemical data of Aldama were selected by means of various combinations of algorithms, processing software (MZmine, MetAlign and MSClust) and hyphenated techniques, such as HPLC-TOF and UHPLC-Orbitrap. Thus, certain peak ions were appointed by chemometrics as discriminant for the dual inhibition activity. These peaks were dereplicated based on their UV profiles and fragmentation patterns via HCD-Orbitrap and Ion-Trap. Thus, it was possible to dereplicate three probable chemical inhibitors of COX-1 and 5-LOX: kaempferol-3-O-glucuronide, quercetin-3-O-methyl-7-glucuronide and kaempferol-3-O-(6\"-malonyl-glucoside). Finally, the targeted phytochemistry was carried out. The chromatographic fractionation allowed us to isolate these three compounds and a preliminary 1H NMR analysis was performed in order to conclude their structural identification. In the near future, these substances will have their activity evaluated by the inhibition of the two enzymes.

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Metabolomics is the methodical scientific study of biochemical processes associated with the metabolome—which comprises the entire collection of metabolites in any biological entity. Metabolome changes occur as a result of modifications in the genome and proteome, and are, therefore, directly related to cellular phenotype. Thus, metabolomic analysis is capable of providing a snapshot of cellular physiology. Untargeted metabolomics is an impartial, all-inclusive approach for detecting as many metabolites as possible without a priori knowledge of their identity. Hence, it is a valuable exploratory tool capable of providing extensive chemical information for discovery and hypothesis-generation regarding biochemical processes. A history of metabolomics and advances in the field corresponding to improved analytical technologies are described in Chapter 1 of this dissertation. Additionally, Chapter 1 introduces the analytical workflows involved in untargeted metabolomics research to provide a foundation for Chapters 2 – 5. Part I of this dissertation which encompasses Chapters 2 – 3 describes the utilization of mass spectrometry (MS)-based untargeted metabolomic analysis to acquire new insight into cancer detection. There is a knowledge deficit regarding the biochemical processes of the origin and proliferative molecular mechanisms of many types of cancer which has also led to a shortage of sensitive and specific biomarkers. Chapter 2 describes the development of an in vitro diagnostic multivariate index assay (IVDMIA) for prostate cancer (PCa) prediction based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) metabolic profiling of blood serum samples from 64 PCa patients and 50 healthy individuals. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent prostate-specific antigen blood test, thus, highlighting that a combination of multiple discriminant features yields higher predictive power for PCa detection than the univariate analysis of a single marker. Chapter 3 describes two approaches that were taken to investigate metabolic patterns for early detection of ovarian cancer (OC). First, Dicer-Pten double knockout (DKO) mice that phenocopy many of the features of metastatic high-grade serous carcinoma (HGSC) observed in women were studied. Using UPLC-MS, serum samples from 14 early-stage tumor DKO mice and 11 controls were analyzed. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for early-stage HGSC detection. In the second approach, serum metabolic phenotypes of an early-stage OC pilot patient cohort were characterized. Serum samples were collected from 24 early-stage OC patients and 40 healthy women, and subsequently analyzed using UPLC-MS. Multivariate statistical analysis employing support vector machine learning methods and recursive feature elimination selected a panel of metabolites that differentiated between age-matched samples with 100% cross-validated accuracy, sensitivity, and specificity. This small pilot study demonstrated that metabolic phenotypes may be useful for detecting early-stage OC and, thus, supports conducting larger, more comprehensive studies. Many challenges exist in the field of untargeted metabolomics. Part II of this dissertation which encompasses Chapters 4 – 5 focuses on two specific challenges. While metabolomic data may be used to generate hypothesis concerning biological processes, determining causal relationships within metabolic networks with only metabolomic data is impractical. Proteins play major roles in these networks; therefore, pairing metabolomic information with that acquired from proteomics gives a more comprehensive snapshot of perturbations to metabolic pathways. Chapter 4 describes the integration of MS- and NMR-based metabolomics with proteomics analyses to investigate the role of chemically mediated ecological interactions between Karenia brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. This integrated systems biology approach showed that K. brevis allelopathy distinctively perturbed the metabolisms of these two competitors. A. glacialis had a more robust metabolic response to K. brevis allelopathy which may be a result of its repeated exposure to K. brevis blooms in the Gulf of Mexico. However, K. brevis allelopathy disrupted energy metabolism and obstructed cellular protection mechanisms including altering cell membrane components, inhibiting osmoregulation, and increasing oxidative stress in T. pseudonana. This work represents the first instance of metabolites and proteins measured simultaneously to understand the effects of allelopathy or in fact any form of competition. Chromatography is traditionally coupled to MS for untargeted metabolomics studies. While coupling chromatography to MS greatly enhances metabolome analysis due to the orthogonality of the techniques, the lengthy analysis times pose challenges for large metabolomics studies. Consequently, there is still a need for developing higher throughput MS approaches. A rapid metabolic fingerprinting method that utilizes a new transmission mode direct analysis in real time (TM-DART) ambient sampling technique is presented in Chapter 5. The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. In terms of reproducibility, TM-DART compared favorably with traditional probe mode DART analysis, with coefficients of variation as low as 16%. TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera and was adapted for exhaled breath condensate (EBC) analysis. To determine the feasibility of utilizing TM-DART for metabolomics investigations, TM-DART was interfaced with traveling wave ion mobility spectrometry (TWIMS) time-of-flight (TOF) MS for the analysis of EBC samples from cystic fibrosis patients and healthy controls. TM-DART-TWIMS-TOF MS was able to successfully detect cystic fibrosis in this small sample cohort, thereby, demonstrating it can be employed for probing metabolome changes. Finally, in Chapter 6, a perspective on the presented work is provided along with goals on which future studies may focus.