Dissertations / Theses: 'Mass spectrometry metabolomic'

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Yang, Zhiyi. "Mass spectrometry-based metabolomic and lipidomic characterization of esophageal cancer and lung cancer." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/819.

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Esophageal cancer and lung cancer are among the most common cancers worldwide with millions of new cases annually. Esophageal cancer patients at an advanced stage suffer from a poor five-year survival rate. However, only fewer than 30% of esophageal cancer cases were diagnosed at an early stage. For lung cancer, malignant pleural effusion (MPE) is an important hallmark for late-stage patients with metastasis. However, other causes of pleural effusions including tuberculosis bring difficulties in the diagnosis of MPE. It is necessary to develop novel diagnostic biomarkers and elucidate the pathological mechanism of esophageal cancer and lung cancer. Metabolic reprogramming is an emerging hallmark of cancer. It has been clear that metabolites play a critical role in cancer development and impose vulnerabilities that could be targeted for cancer therapy. The overall objective of this study is to comprehensively characterize the metabolic dysregulation in esophageal cancer and lung cancer for biomarker discovery and pathological elucidation, by using liquid chromatography--mass spectrometry (LC-MS)-based metabolomics and lipidomics. Paired tumors and normal adjacent tissues from esophageal squamous-cell carcinoma (ESCC) patients were first analyzed through global metabolomic and lipidomic profiling. Tumors were clearly separated from the normal tissues based on the partial least-square discriminant analysis (PLS-DA) model (R2Y >0.85 and Q2Y >0.79 in metabolomic profiling and R2Y >0.70 and Q2Y >0.67 in lipidomic profiling). A preliminary list of 41 polar metabolites and 65 lipids were identified to be significantly perturbed in tumor tissues. Kynurenine, spermidine, citicoline, as well as several glucosylceramides and phosphatidylcholines (PC) showed excellent predictive potential with area under curve (AUC) values better than 0.95 in receiver operating characteristic (ROC) models. Major elevated metabolic pathways were polyamine biosynthesis, glycerophospholipid metabolism, methionine mechanism, arginine and proline mechanism, and kynurenine metabolism, suggesting active amino acid biosynthesis and lipid biosynthesis in ESCC. The potential biomarkers and dysregulated pathways discovered above in ESCC tissue was further validated using targeted metabolomic, lipidomic and proteomic profiling. Polyamine biosynthesis was found to be activated in ESCC through the overexpression of tumor promoting ornithine decarboxylase and spermidine/spermine synthases. Upregulated levels of S-adenosylmethionine and DNA (cytosine-5)-methyltransferase 1 implied DNA hypermethylation in ESCC. Elevated purines in tumors were generated through the overexpression of methylenetetrahydrofolate dehydrogenases. Active phospholipid biosynthesis in tumors was promoted by overexpression of choline transporters and synthase of citicoline, which may accelerate the tumor growth. Dysregulation of coenzyme A species with different fatty acyl chains showed the same trend as of phospholipids, implying the specific activation of relevant acyltransferases in the phospholipid remodeling pathway. Moreover, essential amino acids exhibited a higher upregulation trends in patients with high-grade tumor or with cancer recurrence. Collectively, this study revealed the detailed metabolic dysregulations in ESCC tumor tissues, discovered potential metabolite biomarkers and identified therapeutic targets of ESCC. In order to explore the clinical application of the discovered biomarkers, metabolomic and lipidomic profiling was further performed on ESCC plasma samples. Eight metabolites were found to be simultaneously upregulated in ESCC tumors and plasma samples, indicating their potential as tumor-derived plasma biomarkers. Among them, a panel of five tumor-derived plasma biomarkers consisting of arginine, acetylspermidine, methylguanosine, dimethylguanosine and cystine showed good diagnostic potential in the cross validation. These biomarkers are related with polyamine biosynthesis and purine metabolism, which are critical to support tumor growth. For lung cancer, MPE from lung adenocarcinoma patients were investigated by LC-MS/MS-based metabolomic and lipidomic profiling. In PLS-DA models, the MPE samples were clearly separated from benign pleural effusion samples from pulmonary tuberculosis patients. A group of 17 polar metabolites and 45 lipids were identified to be significantly perturbed in MPE. For diagnostic purposes, ether lipid biomarkers, including PCs, lyso-PCs and phosphatidylethanolamines, showed an excellent predictive ability with the highest AUC value of 0.953 in ROC models. Furthermore, downregulated ether lipids and upregulated oxidized polyunsaturated fatty acids in MPE reflected the elevated oxidative stress and peroxisome disorder in lung cancer patients, which offers deeper understanding in lung cancer pathology.

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Orlowsky, Andrea. "Development of an ambient ionisation mass spectrometry method for metabolomic analysis of blood microsamples." Thesis, Orlowsky, Andrea (2021) Development of an ambient ionisation mass spectrometry method for metabolomic analysis of blood microsamples. Masters by Research thesis, Murdoch University, 2021. https://researchrepository.murdoch.edu.au/id/eprint/63786/.

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Atmospheric Solids Analysis Probe mass spectrometry (ASAP-MS) has applications in food science, pharmaceuticals and toxicology but has not been applied to global metabolic profiling of biofluids. Coupled with dried blood microsampling techniques, ASAP-MS may provide rapid blood sample analyses benefitting medical and forensic sciences. This study aimed to develop a methodology utilising ASAP-MS for the metabolomic analyses of blood samples and comparison of venipuncture (serum and plasma) and capillary dried blood microsamples (DBM) using dried blood spot (DBS) and volumetric absorptive microsampling (VAM) matrices. Method development and optimisation on the Waters RADIAN-ASAP® assessed desolvation gas temperature, cone voltage, and corona current. The optimised parameters were applied to caffeine and lipid standards to determine the reproducibility of the instrument. The sample extraction methods (solvent, dilution, storage vial) and sampling regime for introducing samples into the ion source (sample volume, cooling mechanisms between acquisition) were assessed. The optimised methods were applied to venipuncture and capillary microsamples, and results were compared to ultra-high performance liquid chromatography-mass spectrometry {UPLC-MS). The coefficient of variation {CV) and principal component analysis (PCA) was used to assess analytical reproducibility. ASAP-MS parameters of 600 °C gas temperature, cone voltage of 15 V and corona current of 4 µA generate optimal signal intensity. Methanol extractions of caffeine in polypropylene microcentrifuge tubes produced the most reproducible data {CV = 4.7%), compared to extractions with acetonitrile {CV = 13.5%), isopropyl alcohol {CV = 15.2%) or methanol extractions in glass {CV= 8.1%). A methanol quench-bath to cool the glass sampler between acquisitions shortened the analytical run time while improving reproducibility {CV = 2.3%). UP LC-MS analysis of the lipid compound mixture detected 52 lipid species, of which ASAP-MS reproducibly detected 47 across both polarities. ASAP-MS detected fewer molecular features with a CV<30% (n = 583 positive, n = 571 negative) than UPLC-MS (n = 937 positive, n = 1392 negative) when analysing venipuncture and capillary DBM. ASAP-MS detects more molecular features, with higher CV%, in capillary DBM than venipuncture samples. A simplified field extraction technique yields results equivalent to laboratory-based extractions for DBM. While early experimental results are promising, further evaluations should focus on reducing the variability in QC samples through appropriate data pre-processing pipelines, and the inclusion of an internal standard for data normalisation.

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Delabrière, Alexis. "New approaches for processing and annotations of high-throughput metabolomic data obtained by mass spectrometry." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS359/document.

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La métabolomique est une approche de phénotypage présentant des perspectives prometteuses pour le diagnostic et le suivi de plusieurs pathologies. La technique d'observation la plus utilisée en métabolomique est la spectrométrie de masse (MS). Des développements technologiques récents ont considérablement accru la taille et la complexité des données. Cette thèse s'est concentrée sur deux verrous du traitement de ces données, l'extraction de pics des données brutes et l'annotation des spectres. La première partie de la thèse a porté sur le développement d'un nouvel algorithme de détection de pics pour des données d'analyse par injection en flot continue (Flow Injection Analysis ou FIA), une technique haut-débit. Un modèle dérivé de la physique de l'instrument de mesure prenant en compte la saturation de l'appareil a été proposé. Ce modèle inclut notamment un pic commun à tous les métabolites et un phénomène de saturation spécifique pour chaque ion. Ce modèle a permis de créer une workow qui estime ce pic commun sur des signaux peu bruités, puis l'utilise dans un filtre adapté sur tous les signaux. Son efficacité sur des données réelles a été étudiée et il a été montré que proFIA était supérieur aux algorithmes existants, avait une bonne reproductibilité et était très proche des mesures manuelles effectuées par un expert sur plusieurs types d'appareils. La seconde partie de cette thèse a porté sur le développement d'un outil de détection des similarités structurales d'un ensemble de spectre de fragmentation. Pour ce faire une nouvelle représentation sous forme de graphe a été proposée qui ne nécessite pas de connaître la composition atomique du métabolite. Ces graphes sont de plus une représentation naturelle des spectres MS/MS. Certaines propriétés de ces graphes ont ensuite permis de créer un algorithme efficace de détection des sous graphes fréquents (FSM) basé sur la génération d'arbres couvrants de graphes. Cet outil a été testé sur deux jeux de données différents et a prouvé sa vitesse et son interprétabilité comparé aux algorithmes de l'état de l'art. Ces deux algorithmes ont été implémentés dans des package R, proFIA et mineMS2 disponibles à la communauté
Metabolomics is a phenotyping approach with promising prospects for the diagnosis and monitoring of several diseases. The most widely used observation technique in metabolomics is mass spectrometry (MS). Recent technological developments have significantly increased the size and complexity of data. This thesis focused on two bottlenecks in the processing of these data, the extraction of peaks from raw data and the annotation of MS/MS spectra. The first part of the thesis focused on the development of a new peak detection algorithm for Flow Injection Analysis (FIA) data, a high-throughput metabolomics technique. A model derived from the physics of the mass spectrometer taking into account the saturation of the instrument has been proposed. This model includes a peak common to all metabolites and a specific saturation phenomenon for each ion. This model has made it possible to create a workflow that estimates the common peak on well-behaved signals, then uses it to perform matched filtration on all signals. Its effectiveness on real data has been studied and it has been shown that proFIA is superior to existing algorithms, has good reproducibility and is very close to manual measurements made by an expert on several types of devices. The second part of this thesis focused on the development of a tool for detecting the structural similarities of a set of fragmentation spectra. To do this, a new graphical representation has been proposed, which does not require the metabolite formula. The graphs are also a natural representation of MS/MS spectra. Some properties of these graphs have then made it possible to create an efficient algorithm for detecting frequent subgraphs (FSM) based on the generation of trees covering graphs. This tool has been tested on two different data sets and has proven its speed and interpretability compared to state-of-the-art algorithms. These two algorithms have been implemented in R, proFIA and mineMS2 packages available to the community

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Cardoso, Patrícia [UNESP]. "Metabolomic evaluation of interactions in rhizosphere between Senna spectabilis and associated microorganisms." Universidade Estadual Paulista (UNESP), 2015. http://hdl.handle.net/11449/135954.

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Senna spectabilis é uma planta medicinal detentora de inumeras aplicações terapêuticas tradicionais. Como uma fonte rica de metabólitos com atividades biológicas a rizosfera de S. spectabilis foi o objeto de estudo neste trabalho. O conhecimento da população da microbiota da rizosfera contribuiu para a compreensão das interações que poderiam ocorrer nessa região dinâmica e rica do solo. Um número de métodos moleculares têm sido desenvolvidos nos últimos anos para o estudo da comunidade microbiana utilizando PCR no sequenciamento das regiões de rRNA 16S e ITS. Os objetivos deste estudo foram identificar a população microbiana da rizosfera de mudas de S. spectabilis e em sequência selecionar algumas linhagens e submetê-las a cultivos mistos em meio sólido. A identificação foi conseguida através do 454-pyrosequenciamento das raízes e sequenciamento por iluminaseq das soluções nutritivas em que as plantas foram cultivadas. Os resultados mostraram uma população rica e variada de bactérias e fungos, com destaque aos filos Verrucomicrobia, Proteobacterias, Firmicutes para as bactérias e para os fungos os filos Basidiomycota e Ascomycota. Espécies selecionadas de fungos foram cultivados em meio Czapek-dox líquido e uma análise do perfil metabólico foi conduzido utilizando HPLC-DAD e HPLC-DAD/MS. Entre os metabólitos detectados nessa pré-avaliação estão os derivados policetidicos produzidos por Fusarium: zearalenonas, conhecidas micotoxinas produzidas por uma grande quantidade de espécies deste gênero. Estes resultados induziram a seleção de cinco fungos de Fusarium, Paecilomyces e uma espécie de bactéria, Burkholderia sp para o cultivo misto em meio de agar sólido. O objetivo foi detectar mudanças na produção metabólica e analisar as alterações esperadas por RMN de 1H associados aos métodos quimiométricos de análise PCA e PLS e tambem por LC/MS. Uma série de...
Senna spectabilis is a medicinal plant with innumeral traditional therapeutic applications. As a source of rich metabolites with biological activities S. spectabilis' rhizosphere was the object of study in this work. The knowledge of the population of the microbiota of the rhizosphere contributes to the understanding of the interactions that may occur in this dynamic and rich region of the soil. A number of molecular methods have been developed in recent years to study the microbial community using PCR associated in the sequencing of 16S and ITS regions of rRNA. The objectives of this study were to identify the microbial population of rhizosphere seedlings of S. spectabilis and in sequence to select a few strains and submit them to co-cultures in a solid medium. Identification was achieved with 454- pyrosequencing of roots and iluminaseq sequencing of the hydroponic solutions, in which the seedlings were cultivated. Results showed a rich and varied population of bacteria and fungi notably the bacteria phyla Verrucromiobia, Proteobacteria, Firmicutes and for the fungi the phyla Basidiomycota and Ascomycota. Selected species of fungi were cultivated in liquid Czapek-dox medium and a screening of the metabolic profile was conducted using HPLC-DAD and HPLC-DAD/MS. Among the metabolites detected in this previous evaluation are the polyketides derivates produced by Fusarium: zearalenones known as mycotoxins produced by many species of this genus. These results have induced to the selection of five fungi from Fusarium, Paecilomyces and one specie of bacteria, Burkholderia sp for mix cultivation in agar solid medium. The aim was to detect changes in the metabolic production and analyse the expected alterations by 1H NMR associated with chemometric method PCA and PLS and LC/MS. A series of enniatin could be detected by LC/MS indicating that co-culturing induced the modifications in the...

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Cardoso, Patrícia. "Metabolomic evaluation of interactions in rhizosphere between Senna spectabilis and associated microorganisms /." Araraquara, 2015. http://hdl.handle.net/11449/135954.

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Orientador: Ian Castro-Gamboa
Banca: Luis Vitor Silva do Sacramento
Banca: Cíntia Duarte de Freitas Milagre
Banca: Mônica Tallarico Pupo
Banca: Christopher Scott Jeffrey
Resumo: Senna spectabilis é uma planta medicinal detentora de inumeras aplicações terapêuticas tradicionais. Como uma fonte rica de metabólitos com atividades biológicas a rizosfera de S. spectabilis foi o objeto de estudo neste trabalho. O conhecimento da população da microbiota da rizosfera contribuiu para a compreensão das interações que poderiam ocorrer nessa região dinâmica e rica do solo. Um número de métodos moleculares têm sido desenvolvidos nos últimos anos para o estudo da comunidade microbiana utilizando PCR no sequenciamento das regiões de rRNA 16S e ITS. Os objetivos deste estudo foram identificar a população microbiana da rizosfera de mudas de S. spectabilis e em sequência selecionar algumas linhagens e submetê-las a cultivos mistos em meio sólido. A identificação foi conseguida através do 454-pyrosequenciamento das raízes e sequenciamento por iluminaseq das soluções nutritivas em que as plantas foram cultivadas. Os resultados mostraram uma população rica e variada de bactérias e fungos, com destaque aos filos Verrucomicrobia, Proteobacterias, Firmicutes para as bactérias e para os fungos os filos Basidiomycota e Ascomycota. Espécies selecionadas de fungos foram cultivados em meio Czapek-dox líquido e uma análise do perfil metabólico foi conduzido utilizando HPLC-DAD e HPLC-DAD/MS. Entre os metabólitos detectados nessa pré-avaliação estão os derivados policetidicos produzidos por Fusarium: zearalenonas, conhecidas micotoxinas produzidas por uma grande quantidade de espécies deste gênero. Estes resultados induziram a seleção de cinco fungos de Fusarium, Paecilomyces e uma espécie de bactéria, Burkholderia sp para o cultivo misto em meio de agar sólido. O objetivo foi detectar mudanças na produção metabólica e analisar as alterações esperadas por RMN de 1H associados aos métodos quimiométricos de análise PCA e PLS e tambem por LC/MS. Uma série de...
Abstract: Senna spectabilis is a medicinal plant with innumeral traditional therapeutic applications. As a source of rich metabolites with biological activities S. spectabilis' rhizosphere was the object of study in this work. The knowledge of the population of the microbiota of the rhizosphere contributes to the understanding of the interactions that may occur in this dynamic and rich region of the soil. A number of molecular methods have been developed in recent years to study the microbial community using PCR associated in the sequencing of 16S and ITS regions of rRNA. The objectives of this study were to identify the microbial population of rhizosphere seedlings of S. spectabilis and in sequence to select a few strains and submit them to co-cultures in a solid medium. Identification was achieved with 454- pyrosequencing of roots and iluminaseq sequencing of the hydroponic solutions, in which the seedlings were cultivated. Results showed a rich and varied population of bacteria and fungi notably the bacteria phyla Verrucromiobia, Proteobacteria, Firmicutes and for the fungi the phyla Basidiomycota and Ascomycota. Selected species of fungi were cultivated in liquid Czapek-dox medium and a screening of the metabolic profile was conducted using HPLC-DAD and HPLC-DAD/MS. Among the metabolites detected in this previous evaluation are the polyketides derivates produced by Fusarium: zearalenones known as mycotoxins produced by many species of this genus. These results have induced to the selection of five fungi from Fusarium, Paecilomyces and one specie of bacteria, Burkholderia sp for mix cultivation in agar solid medium. The aim was to detect changes in the metabolic production and analyse the expected alterations by 1H NMR associated with chemometric method PCA and PLS and LC/MS. A series of enniatin could be detected by LC/MS indicating that co-culturing induced the modifications in the...
Doutor

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Childs, Stephen Andrew. "Liquid chromatography and mass spectrometry based metabolomic investigations of sulphur containing metabolites in human prostate cancer." Thesis, University of Sunderland, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702720.

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Low molecular weight thiols constitute a biologically important class of metabolites, some of which play a principal role in cellular defence against oxidative stress. The aetiology of cancer is generally linked with DNA mutation; often as a result of oxidative damage when antioxidant defences are dysregulated. Accordingly, the investigation of redox metabolites within cancer models is relevant to better understand the initiation and development of the disease. Specifically, when detected at an early stage, prostate cancer treatment by androgen ablation often carries a high rate of success. After a period of 18-24 months however, the disease is characterised by a shift to androgen insensitivity, and mortality increases significantly in advanced states. Hence, early detection and improved understanding of the changes in metabolism which accommodate a shift to androgen insensitivity and increased rate of proliferation are also relevant. Metabolomic profiling is a rapidly expanding field of systems biology which combines sensitive, high resolution equipment with powerful chemometric data processing to determine alterations in metabolic pathways in response to stress factors, including internal and external stimuli; thus providing valuable insight into the mechanisms involved in disease development. Whilst this approach has been applied to cancer research in the past to discover new drug targets and putative biomarkers for early detection, the complex metabolic pathways involved in cancer progression are not fully understood. Moreover, recently reported dysregulation of redox status and glutathione content in prostate cell models suggested significantly altered metabolism in some cancers. In order to better understand the metabolic events occurring, the aim of this study was to detect, and quantify where possible the sulphur-containing metabolites in prostate cancer cell models. Targeted metabolomic based methods using derivatisation with a specific reagent (DTNB) were developed and validated to provide comprehensive quantitative measurements of reduced thiols in cell models representing androgen sensitive (LNCaP) and androgen insensitive (DU145) disease, in addition to control cells representing healthy prostate epithelium (PZ-HPV-7). Furthermore, metabolomic profiling was performed using these cell lines to identify up and down regulation of key sulphur containing metabolites including disulphides and thioethers. Measurements of glutathione and the oxidised form indicated increased oxidative stress in LNCaP cells, whilst DU145 exhibited signs of adaptation to oxidative stress by up-regulation of glutathione biosynthesis. Investigation of the metastatic, androgen insensitive cell line, LNCaP, revealed a significant disparity in total thiol content and glutathione, suggesting the presence of additional thiol metabolites. Methods were developed and refined to determine the presence of cysteine, cysteinylglycine and an additional previously unidentified thiol species in LNCaP cells. Quantitative HPLC methods were validated and used to determine the concentration of individual thiol components in each cell line, successfully accounting for the total thiol content for the first time. The control cell contained 0.5 (± 0.03) and 6.3 (± 0.14) femtomoles per cell of cysteine and glutathione respectively, DU145 cells contained 0.3 (± 0.1) and 32.3 (± 2.3) femtomoles per cell of cysteine and glutathione respectively, and LNCaP cells contained 2.7 (± 0.05), and 8.3 (± 0.73) femtomoles per cell of cysteine and glutathione respectively. LNCaP cells additionally contained 0.8 (± 0.1) femtomoles per cell of cysteinylglycine. Further investigations proved that the unknown thiol (compound x) was a molecule of cysteine and glycerate linked by a peptide bond. Through examination of metabolite databases and chemical literature it was determined that the molecule had not previously been reported. Profiling of the cells highlighted this metabolite as a key component of the LNCaP metabolic fingerprint, in addition to other metabolites with roles in cell energy production. The developed methods stand as potentially useful tools for the sensitive detection and quantitation of thiols and for metabolomic investigations in various cell lines. Detection of a new thiol, cysteinyl-glycerate, in LNCaP cells warrants further investigations into the biological role of this metabolite and the potential as a putative biomarker.

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Vallabhaneni, Prashanthi. "Metabolomic approaches to understanding the auxin and ethylene response in Arabidopsis roots." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76838.

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Non-targeted metabolite profiling by liquid chromatography-mass spectrometry (LC-MS) was used to determine the metabolite responses of Arabidopsis roots to auxin or ethylene. Crosstalk between these hormones regulates many important physiological processes in plants, including the initiation of lateral root formation and the response to gravity. These occur in part through alterations in the levels of flavonoids, specialized plant metabolites that have been shown to act as negative regulators of auxin transport. However, much remains to be learned about auxin and ethylene responses at the level of the metabolome. LC-MS analysis showed that a number of ions changed in response to both hormones in seedling roots. Although classes of specialized metabolites such as flavonols and glucosinolates change in abundance in response to both auxin and ethylene, there was little overlap with regard to the specific metabolites affected. These data will be integrated with information from transcriptomic and proteomic experiments to develop framework models that connect phytohormones and specialized metabolism with specific physiological processes. Previous studies by imaging techniques have shown that flavonols increase in response to both auxin and ethylene in the root elongation zone, but LC-MS showed that flavonols decreased in abundance in response to these hormones. Therefore a method was developed for targeted metabolite profiling of flavonols in individual root tips by flow injection electrospray mass spectrometry. This method uncovered spatial differences in metabolic profiles that were masked in analyses of whole roots or seedlings, and verified that flavonols increase in response to these hormones in root tips.
Master of Science

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Marsol, i. Vall Alexis. "Gas chromatography-mass spectrometry for the analysis of metabolomic compounds in agrifood products. New methods and applications." Doctoral thesis, Universitat de Lleida, 2017. http://hdl.handle.net/10803/403491.

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Aquesta Tesi Doctoral se centra en el desenvolupament de nous mètodes de cromatografia de gasos acoblada a tècniques d'espectrometria de masses (GC-MS) i a l'aplicació d'alguns mètodes ja existents a l'anàlisi de mostres de fruites i derivats. La tesi es divideix en tres parts segons els enfocaments estudiats. Inicialment, es va desenvolupar un mètode de cromatografia de gasos bidimensional comprensiva (GC×GC-MS) en la qual es van provar diverses configuracions de columnes. A la segona part de la Tesi, es van desenvolupar tres nous mètodes basats en la derivatització al port d'injecció. La primera va consistir en l’anàlisi selectiu de 17 polifenols glicosilats i no glicosilats en mostres de fruita i suc de fruita. El segon mètode es va destinar a l'anàlisi de HMF i patulina, dos compostos utilitzats com a marcadors de qualitat en la indústria dels sucs de fruites. L'últim mètode desenvolupat en aquesta part es va centrar en la fracció lipofílica lliure de sucs de fruita. En aquest cas, una microextracció líquid-líquid dispersiva (DLLME) va precedir a la derivatització en el port. La tercera part es va centrar en l'anàlisi dels compostos volàtils i semi-volàtils de diversos derivats de la fruita, a saber, fibres de fruita derivades de la indústria dels sucs i quatre mostres de sucs de préssec consistents en dues varietats (groc i vermell) i dos procediments d'elaboració per a cada varietat (recentment liquat i comercial).
This Doctoral Thesis focuses on the development of novel gas chromatography coupled to mass spectrometry (GC-MS) techniques and the application of some existing methods to the analysis of fruit and fruit-derived samples. The thesis is divided in three parts attending the approaches studied. Initially, a comprehensive two-dimensional gas chromatography (GC×GC-MS) method was developed by testing several column configurations to analyse apples and peaches. In the second part of the Thesis, three new methods based on injection-port derivatization were developed. The first consisted on a targeted analysis of 17 glycosylated and non-glycosylated polyphenols in fruit and fruit juice samples. The second method was devoted to the analysis of HMF and patulin, two compounds used as markers of quality in the fruit juice industry. The last method developed in this part was focused on the free lipophilic fraction of fruit juices. In this case, a dispersive liquid-liquid microextraction (DLLME) preceded in-port derivatization. The third part was devoted to the analysis of volatile and semi-volatile compounds in several fruit-derived products, namely fruit fibres deriving from the juice industry and four samples of peach juices consisting in two varieties (yellow and red-fleshed) and two distinct processing procedures for each variety (freshly blended and commercial).
Esta Tesis Doctoral se centra en el desarrollo de nuevos métodos de cromatografía de gases acoplada a técnicas de espectrometría de masas (GC-MS) y a la aplicación de algunos métodos existentes al análisis de muestras de frutas y derivados. La tesis se divide en tres partes según los enfoques estudiados. Inicialmente, se desarrolló un método de cromatografía de gases bidimensional comprensiva (GC×GC-MS) en la que se probaron varias configuraciones de columnas. En la segunda parte de la Tesis, se desarrollaron tres nuevos métodos basados en la derivatización en el puerto de inyección. La primera consistió en un análisis selectivo de 17 polifenoles glicosilados y no glicosilados en muestras de fruta y zumo de fruta. El segundo método se dedicó al análisis de HMF y patulina, dos compuestos utilizados como marcadores de calidad en la industria del zumo de frutas. El último método desarrollado en esta parte se centró en la fracción lipofílica libre de zumos de fruta. En este caso, una microextracción líquido-líquido dispersiva (DLLME) precedió a la derivatización en el puerto. La tercera parte se dedicó al análisis de los compuestos volátiles y semi-volátiles de varios derivados de la fruta, a saber, fibras de fruta derivadas de la industria de los zumos y cuatro muestras de zumos de melocotón consistentes en dos variedades (amarillo y rojo) y dos procedimientos de elaboración para cada variedad (recién licuado y comercial).

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Alonezi, Sanad M. Z. "Metabolomic profiling of the effects of melittin and cisplatin on ovarian cancer cells using high resolution mass spectrometry." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=28650.

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Over the last few years, metabolomics has come to play an increasingly important part in many fields of research, notably medical studies. However, there is a dearth of research on metabolomics in the area of ovarian cancer and the increase in anti-cancer (platinum) drug resistance. Thus further studies on the modes of anticancer action and the mechanisms of resistance of ovarian cancer cells at the metabolome level are needed. The aim of this study was to characterise the metabolic profiles of two human ovarian cancer cell lines, A2780 (cisplatin-sensitive) and A2780CR (cisplatin-resistant), in response to their exposure to melittin, cisplatin and melittin-cisplatin combination therapy. It has been suggested that melittin may have potential as an anti-cancer therapy; combining cisplatin and melittin may increase response and tolerability in cancer treatment, as well as reducing drug resistance. The A2780 and A2780CR cell lines were treated with sub-lethal doses of melittin, cisplatin and melittin-cisplatin combination therapy for 24 hours before extraction and global metabolite analysis of cell lysates by LC-MS using a HPLC system. Phenotype MicroArray™ experiments were also applied in order to test carbon substrate utilisation or sensitivity in both cell lines after exposure to melittin and cisplatin. Data extraction was carried out with MZmine 2.10 with metabolite searching against an in-house database. The data were analysed using univariate and multivariate methods. The changes induced by melittin in the cisplatin-sensitive cells mainly resulted in reduced levels of amino acids in the proline/glutamine/arginine pathway, as well as to decreased levels of carnitines, polyamines, ATP and NAD+. It was necessary to evaluate the effect of a melittin on lipid activities of ovarian cancer cell lines. In order to do so, an LC coupled to an Orbitrap Exactive mass spectrometer using an ACE silica gel column was employed. The two cell lines had distinct lipid compositions, with the A2780CR cells having lower levels of several ether lipids than the A2780 cells. The changes induced by melittin in both cell lines mainly led to a decrease the level of PC and PE. Lipids were significantly altered in both A2780 and A2780CR cells. The observed effect was much more marked in the cisplatin-sensitive cells, suggesting that the sensitive cells undergo much more extensive membrane re-modelling in responsexviito melittin in comparison with the resistant cells. Regarding the metabolic effects of cisplatin on A2780 cells, these mainly resulted decreased levels of acetylcarnitine, phosphocreatine, arginine, proline and glutathione disulfide, as well as to increased levels of tryptophan and methionine. A number of metabolites were differently affected between the A2780 and A2780CR cells following cisplatin treatment, with A2780CR cells presenting increased levels of lysine, and decreased levels of N-acetyl-glutamate, oxoglutarate and 2-oxobutanoate compared to sensitive cells. However, when the combination treatment was applied, there were significant changes in both cell lines, mainly resulting in a reduction of levels of citrate cycle, oxidative phosphorylation, purine, pyrimidine and arginine/proline pathways. The combination of melittin with cisplatin has a synergistic effect when targeting these pathways. The melittin-cisplatin combination had stronger effect on A2780 cell lines than it had on those of A2780CR.Overall, this study suggests that melittin may have some potential as an adjuvant therapy in cancer treatment. A global metabolomics approach can be a useful tool for evaluating the pharmacological effects of anti-cancer compounds or synergetic sensitisers using mass spectrometry.

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Denbigh, Joanna. "Lipidomic and metabolomic analysis of biological response mechanisms in cancer cells : a multidisciplinary approach." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/lipidomic-and-metabolomic-analysis-of-biological-response-mechanisms-in-cancer-cells-a-multidisciplinary-approach(a1f04b8e-0f79-497a-9928-18a59a8e9cb0).html.

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The 21st Century has seen a rise in incidence of complex diseases such as cancer and in the quest to develop essential new therapeutic options, the study of drug-cell interactions can yield powerful information. Acute myeloid leukaemia (AML) is an aggressive cancer that causes life-threatening deficits of functional blood cells in humans for which current treatment options are highly toxic and often poorly tolerated. A combination of two existing drugs, bezafibrate and medroxyprogesterone acetate in a drug redeployment situation has shown promise in vitro and in vivo and further investigations are crucial to elucidate the mode of action of this treatment. This project investigated the mechanistic action of BaP at a cellular level. Orthogonal spectroscopic and mass spectrometric platforms were employed to probe the biochemical composition of two AML cell lines, HL60 and K562 in the presence and absence of this combined drug treatment. Analysis was performed on single living cells, dehydrated cells, fixed cells and cell extracts to give a large and detailed data set. A consideration of the main spectral differences obtained by Synchrotron-FTIR and ATR-FTIR in conjunction with multivariate statistical analysis revealed a significant change to the cellular lipid composition with drug treatment; furthermore, this response was not caused by cell apoptosis. In particular, the ratio of CH2:CH3 was observed to increase with BaP treatment and this was determined to be a significant change in both cell lines (p <0.05). An overall increase in lipid unsaturation suggests that BaP targets cellular lipid biosynthesis. Raman microspectroscopy added a further dimension to the spectroscopic study by providing spatial information of lipid distribution which suggested that BaP-induced saturation change is uniform across a single cell. UHPLC-MS was employed for global metabolomics analysis of AML cell extracts and revealed a number of biochemical pathways that were indicated as targets of BaP therapy in both cell lines. Univariate and multivariate analysis determined statistically significant metabolites for which putative identifications were made. Pyrimidine metabolism was the most significant pathway identified for changes consistent in both HL60 and K562 cell lines. The complementarity of ToF-SIMS and UHPLC-MS provided large coverage of the lipidome of AML cells through untargeted and targeted approaches. For data derived by both techniques, a general increase in polyunsaturated species for BaP treated cell extracts was observed which correlated well with findings from spectroscopic investigations. Adopting a multi-disciplinary approach to cell analysis can afford a powerful insight into understanding drug mode of action at a cellular level and novel information regarding BaP mechanistic action in AML cell lines was revealed. This analytical approach could be extended to the future study of drug-cell interactions for other oncological systems.

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Mohler, Rachel E. "Discovery based yeast metabolomic analysis using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry and chemometrics /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/11578.

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Jaeger, Frederick Howard. "Simplified Plant Sample Preparation for use in Gas Chromatography-Mass Spectrometry (GC-MS) Based Metabolomic Profiling and Targeted Analyte Quantitation." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-02202008-155316/.

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A simple, fast, reproducible and less laborious sample preparation protocol was developed for the analysis of Arabidopsis thaliana using Gas chromatography coupled with mass spectrometry (GC-MS). In particular, a semi-automated machine tool is used to replace the traditional mortar-pestle method in tissue grinding. One-pot chemical extraction-derivatization is used to provide simplified sample preparation over the conventional multi-step liquid-liquid extraction protocol. Wild-type and transgenic Arabidopsis thaliana seedlings were used as the model system to evaluate performance of this newly developed method for use in metabolic profiling and also targeted quantitative analysis of salicylic acid for the study of systemic acquired resistance.

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Robertson, Francesca Pamela. "The application of proteomic and metabolomic mass spectrometry strategies for the characterization and detection of processed animal proteins in animal feeds." Thesis, University of London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529475.

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Abbiss, Hayley. "Gas chromatography-mass spectrometry-based untargeted metabolomic analysis of organ tissue, plasma and urine samples from a Rat Model of polycystic kidney disease." Thesis, Abbiss, Hayley (2018) Gas chromatography-mass spectrometry-based untargeted metabolomic analysis of organ tissue, plasma and urine samples from a Rat Model of polycystic kidney disease. PhD thesis, Murdoch University, 2018. https://researchrepository.murdoch.edu.au/id/eprint/41472/.

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Introduction: A predisposition to nephronophthisis (NPHP) is inherited and typically presents with cysts in the kidney and liver, leading to end-stage kidney disease. The mechanisms underlying onset and progression of cyst growth remain unknown and detection of NPHP and other polycystic kidney diseases (PKDs) is not sensitive or specific. For these reasons, management and treatment are limited to renal replacement therapy and transplantation. The LPK rat phenotype has been characterized and classified as a model of the PKD, NPHP9, caused by mutation of the nek8 gene. Using this model, the aim of this collection of studies was to use a GC-MS-based untargeted metabolomic analysis to determine key biochemical changes in kidney and liver tissue of the LPK rat and to investigate biomarkers in the blood plasma and urine. Furthermore, the study determined whether sample derivatisation could be streamlined in an automated process to improve reproducibility and investigated the use of BSTFA as a derivatisation reagent, as an alternative to MSTFA. Methods: Following a pilot study using 4 LPK and 4 Lewis controls, 11 LPK and 11 Lewis age- and sex-matched control animals aged 5 to 16 weeks were used. Blood and urine were sampled weekly and organs harvested at the conclusion of the study. Metabolites were extracted with methanol and water containing 13C6-sorbitol (IS) and derivatised with methoxyamine-HCl and MSTFA. A Shimadzu QP2010 Ultra GC-MS was used for sample analysis, and for data analysis, AnalyzerPro, The Unscrambler X and SPSS were used. Features were matched to an in-house library of metabolites and the NIST mass spectral database. Results: For metabolomic analysis of the kidney and liver tissue, principal component analysis (PCA) distinguished signal corrected metabolite profiles from Lewis and LPK rats iv for kidney (PC-1 77%) and liver (PC-1 46%) tissue. In kidney tissue, 122 metabolites were found to be significantly different between the LPK and Lewis strains and five biochemical pathways showed three or more significantly altered metabolites: transcription/translation, arginine and proline metabolism, alpha-linolenic and linoleic acid metabolism, the citric acid cycle and the urea cycle. In the liver, 30 metabolites were found to be significantly different. Urine and plasma metabolites were tested for age (Kruskal-Wallis; p < 0.05) and strain effects (Mann-Whitney U-test; p < 0.05). Fifty-nine putatively identified metabolites from the LPK plasma and urine were found to be significantly different from Lewis controls. These results were concomitant with data from kidney and liver tissue analyses. The results of these studies validate and complement the current literature and are consistent with suggestions relating to the pathobiology of PKD. Most notably, myo-inositol was suggested as an early marker of renal dysfunction in PKD. Derivatised metabolite responses were highly variable throughout the ten analytical batches of urine samples compared to the 12 batches of plasma samples, even for test mixtures, which were not affected by sample concentration or matrix. Derivatization reagent and protocol are key factors affecting the reproducibility and intensity of individual urinary metabolites, so we tested both BSTFA as an alternate to MSTFA and the use of automated protocols (batch and in-time) using a CTC CombiPAL auto sampler. Of 249 features detected in rat urine, 40 features were significantly different (p < 0.05) based upon reagent and 154 features were significantly different (p < 0.05) based upon protocol. The overall reproducibility of the methods was similar, although highly feature dependent.

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Fall, Fanta. "Étude métabolomique de la polarisation des macrophages pulmonaires humains A split-range acquisition method for the non-targeted metabolomic profiling of human plasma with hydrophilic interaction chromatography - high-resolution mass spectrometry Metabolomic changes during the M1- and M2-polarization of human lung macrophages." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLV064.

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Les macrophages pulmonaires sont des cellules immunitaires essentielles pour la défense contre les agents pathogènes qui colonisent les voies respiratoires et sont aussi impliqués dans la physiopathologie des maladies pulmonaires inflammatoires telles que l’asthme. Les macrophages peuvent être sujets à une polarisation vers deux phénotypes appelés M1 et M2. Le phénotype M1 est induit par les agonistes des Toll-like Récepteurs et promeut la réponse inflammatoire tandis que le phénotype M2 est induit par les cytokines Th2 canoniques IL-4 et IL-13 et exerce principalement des fonctions immunorégulatrices. Cette différenciation se traduit par des modifications du phénotype (expression de protéines de membrane, production de cytokines) et des fonctions cellulaires. La polarisation a aussi un impact sur le métabolisme et la production de médiateurs intracellulaires.Notre objectif etait de caractériser les altérations métabolomiques survenant aux cours de la polarisation M1/M2 de macrophages pulmonaires humains. Dans un premier temps, des méthodes métabolomiques non-ciblées et ciblées par chromatographie liquide couplée à la spectrométrie de masse haute résolution et par chromatographie en phase gazeuse couplée à la spectrométrie de masse ont été développées. Ces approches ont ensuite permis d’identifier des voies métaboliques altérées au cours de la polarisation M1/M2 (cycle de Krebs, voie des kunurénines et de l’acide arachidonique) et de quantifier les des médiateurs impliqués dans la régulation de la réaction inflammatoire, dont les oxystérols. Ces travaux permettent une meilleure compréhension du métabolisme cellulaire au cours de la polarisation des macrophages pulmonaires humains
Lung macrophages are essential immune cells for defense against pathogens that colonize the respiratory tract and are also involved in the pathophysiology of inflammatory lung diseases such as asthma. Macrophages can be subject to polarization towards two phenotypes called M1 and M2. The M1 phenotype is induced by Toll-like Receptor agonists and promotes the inflammatory response while the M2 phenotype is induced by the canonical Th2 cytokines IL-4 and IL-13 and mainly performs immunoregulatory functions. This differentiation results in changes in phenotype (expression of membrane proteins, production of cytokines) and cellular functions. Polarization also has an impact on metabolism and the production of intracellular mediators.Our objective was to characterize the metabolomic alterations occurring during the M1/M2 polarization in human lung macrophages. Initially, non-targeted and targeted metabolomic methods by liquid chromatography coupled with high-resolution mass spectrometry and gas chromatography coupled with mass spectrometry were developed. These approaches were then applied to identify altered metabolic pathways during M1/M2 polarization (Krebs cycle, kynurenin and arachidonic acid pathways) and to quantify the mediators involved in regulating the inflammatory response, including oxysterols. This work provides a better understanding of cellular metabolism during the polarization of human lung macrophages

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Riccio, Maria Francesca. "Uso da espectrometria de massas como ferramenta metabolômica e controle de qualidade de óleos vegetais e gorduras animais." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/311827.

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Orientador: Rodrigo Ramos Catharino
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas
Made available in DSpace on 2018-08-18T04:45:53Z (GMT). No. of bitstreams: 1 Riccio_MariaFrancesca_M.pdf: 1073402 bytes, checksum: ad216531a7e88603fc492ee5c702fd1f (MD5) Previous issue date: 2011
Resumo: Este trabalho direcionou-se ao controle de qualidade de óleos vegetais e gorduras animais pelo emprego de análise de baixa massa molecular pela técnica EASI-MS utilizando a ferramenta metabolômica. As matérias-primas graxas (óleos e gorduras vegetais e animais) e os produtos sintetizados a partir destes insumos são misturas complexas, com aplicações e valores agregados variados como por exemplo para a nutrição humana, aplicações industriais na produção de lubrificantes, biodiesel, plasticidas, surfactantes, entre outros. As técnicas disponíveis como Cromatografia gasosa (GC) com detector de ionização de chama (FID) ou acoplada a um espectrômetro de massas (GC-MS) tem sido as técnicas mais utilizadas para caracterizar óleos e gorduras, através da determinação da composição de ácidos graxos. Como a caracterização da composição graxa destas matrizes são muitas vezes restritas, exigindo a necessidade de procedimentos laboriosos como extrações, purificações que requerem muito tempo utilizamos neste trabalho uma recente técnica de ionização ambiente de espectrometria de massas: EASI-MS (easy ambient sonic-spray ionization mass spectrometry) na caracterização de óleos vegetais e gorduras animais que dispensa o emprego de processos de derivatização química e a separação cromatográfica. Neste trabalho aplicamos a metabolômica para elucidação de um conjugado de marcadores taxonômicos de óleos nunca observados ou analisados em conjunto antes, principalmente ácidos graxos e bifenóis os quais podem ser extraídos de maneira simples e eficaz. Para a caracterização dos componentes existentes no metaboloma de óleos de origem vegetal e animal e para a caracterização de azeites de oliva de diferentes procedências, uma simples extração com uma solução hidroalcoólica foi utilizada. O extrato foi adicionados à uma superfície de vidro para dessa forma, serem injetados para dentro do equipamento de massas e então analisados. O equipamento utilizado foi um Q-TrapTM utilizando uma fonte de EASI construída por pesquisadores do Laboratório ThoMSon de espectrometria de massas (IQ/UNICAMP). Os espectros de massas obtidos demonstraram a presença de ácidos graxos livres além de bifenóis característicos de cada tipo de óleo ou gordura analisado. A presença na mesma análise (espectro) demonstrou que mesmo para amostras complexas, a técnica se mostra aplicável para a identificação, caracterização e controle de qualidade de maneira inequívoca para óleos e gorduras
Abstract: This work is directed to quality control of vegetable oils and animal fats by the use of low molecular analysis of the EASI-MS technique using the metabolomics tool. Raw materials greases (oils and vegetable and animal fats) and the products synthesized from these inputs are complex mixtures, with varying applications and value added such as for human nutrition, industrial applications in the lubrificants production, biodiesel, plasticity, surfactants, among others. The available techniques such as chromatography (GC) with flame ionization detector (FID) and coupled to a mass spectrometer (GC-MS) has been the most widely used techniques to characterize oils and fats by determining the fatty acid composition. To characterize the grease composition of these matrices are often restricted, requiring the need of laborious procedures such as extractions, purifications that require much time, in this work we used a recent technique ambient ionization mass spectrometry: EASI-MS (Easy ambient sonic-spray ionization mass spectrometry) in the characterization of vegetable oils and animal fats does not require the use of chemical derivatization procedures and chromatographic separation. In this work we have applied metabolomics to elucidate a combination of taxonomic markers oils never observed or analyzed together before, mainly fatty acids and biphenols which can be extracted in a simple and effective process. To characterize the metabolome of existing components in vegetable oils and animal and for the characterization of olive oils from different sources, a simple extraction with a water-methanol solution was used. The extract was added to a glass surface to thereby be injected into the spectrometer and then analyzed. The equipment used was a Q-TrapTM using a source of EASI built by researchers at the Thomson Laboratory of mass spectrometry (Institute of Chemistry / UNICAMP). The mass spectra obtained showed the presence of free fatty acids besides biphenols characteristic of each type of oil or fat analyzed. The presence in the same analysis (spectrum) showed that even for complex samples, the technique proves to be applicable to the identification, characterization and quality control unequivocally for oils and fats
Mestrado
Ciencias Biomedicas
Mestre em Ciências Médicas

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Rocha, Cláudia Manuela Mesquita da. "Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids." Doctoral thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/13957.

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Doutoramento em Química
This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.
A presente tese reporta a aplicação da metabolómica ao estudo de tecidos e biofluidos humanos (plasma sanguíneo e urina), com o intuito de caracterizar a assinatura metabólica do cancro pulmonar primário. No Capítulo 1, apresenta-se uma breve introdução sobre a epidemiologia e a patogénese deste tipo de cancro, bem como um sumário das principais alterações metabólicas tipicamente associadas ao cancro em geral. Descreve-se ainda a abordagem metabolómica, nomeadamente os métodos analíticos e estatísticos utilizados, assim como o estado da arte da sua aplicação em estudos clínicos do cancro do pulmão. No Capítulo 2, apresentam-se os detalhes experimentais deste trabalho, no que diz respeito ao grupo de indivíduos envolvidos, à colheita e análise das amostras e ao posterior tratamento dos dados. O Capítulo 3 descreve a caracterização metabólica de tecidos do pulmão (de 56 doentes) por espetroscopia de Ressonância Magnética Nuclear (RMN) de alta resolução com rotação no ângulo mágico. Após a otimização cuidada das condições de aquisição e a identificação detalhada dos sinais espetrais (mais de 50 metabolitos identificados), os perfis metabólicos dos tumores e dos tecidos adjacentes não envolvidos (controlos) foram comparados por análise multivariada, tendo sido discriminados com uma exatidão de 97%. Os metabolitos que mais significativamente contribuíram para esta diferenciação foram: glucose e acetato (diminuídos nos tumores), lactato, alanina, glutamato, GSH, taurina, creatina, fosfocolina, glicerofosfocolina, fosfoetanolamina, nucleótidos de uracilo e péptidos (aumentados nos tumores). Algumas destas variações corroboraram alterações típicas do metabolismo do cancro (e.g., glicólise e glutaminólise aumentadas), enquanto outras sugeriram novas pistas sobre a possível relevância de processos como a proteção antioxidante e a degradação proteica. Um outro resultado novo e importante descrito neste capítulo foi a dependência da assinatura metabólica em relação ao tipo histológico do tumor. Enquanto as principais alterações observadas nos adenocarcinomas (AdC) se relacionaram com o metabolismo fosfolipídico e proteico, os carcinomas de células escamosas (SqCC) apresentaram perfis glicolíticos e glutaminolíticos mais pronunciados, sendo possível construir um modelo válido para a discriminação destes subtipos. No Capítulo 4, apresenta-se o estudo metabolómico por RMN de plasma sanguíneo de mais de 100 doentes e quase 100 controlos saudáveis, do qual resultou um modelo multivariado com uma taxa de classificação de 87%. A distinção entre os grupos foi feita essencialmente com base nos níveis de lactato, piruvato, acetoacetato, lipoproteínas LDL+VLDL e glicoproteínas (aumentados nos doentes), juntamente com os níveis de glutamina, histidina, valina, metanol, lipoproteínas HDL e dois compostos não identificados (diminuídos nos doentes). Estas variações foram detetadas desde os estádios iniciais da doença e a magnitude de algumas delas dependeu do tipo histológico, embora não permitindo discriminar AdC de SqCC. Para além disso, mostra-se neste capítulo que o desequilíbrio dos grupos controlo e cancro em termos da idade dos indivíduos poderá ter alguma influência nos resultados, e apresenta-se uma tentativa exploratória de validação externa, que resultou numa taxa de classificação de 85%. O estudo por RMN do perfil metabólico da urina dos doentes com cancro do pulmão e dos controlos é apresentado no Capítulo 5. Comparativamente ao plasma, o modelo construído com os perfis urinários apresentou uma taxa de classificação superior (97%). Após uma avaliação cuidada da possível influência do género, idade e hábitos tabágicos, um conjunto de 19 metabolitos foi proposto como estando relacionado com a doença (incluindo 3 compostos desconhecidos e 6 parcialmente identificados como metabolitos N-acetilados). Tal como no caso do plasma, estas variações foram detetadas em doentes no estádio inicial e mostraram alguma dependência em relação ao tipo histológico, obtendo-se um modelo válido para a discriminação AdC vs. SqCC, ainda que com um poder preditivo modesto. Para além disso, o teste preliminar de validação externa revelou 100% de sensibilidade e 90% de especificidade, o que é um resultado bastante promissor em termos da potencial utilização dos perfis urinários em aplicações clínicas futuras. No Capitulo 6, descreve-se a caracterização dos perfis metabólicos da urina (de um subgrupo de indivíduos) por cromatografia líquida de ultra-eficiência acoplada a espetrometria de massa (UPLC-MS). Embora não avançando muito na identificação estrutural de possíveis marcadores, este estudo reforçou o valor diagnóstico da urina, já que os modelos multivariados resultantes apresentaram taxa de classificação e poder preditivo elevados. Finalmente, no Capítulo 7, apresentam-se as principais conclusões deste trabalho, realçando o contributo da metabolómica integrada de tecidos e biofluidos para a compreensão do metabolismo alterado do cancro do pulmão e para a deteção de novos perfis marcadores com valor diagnóstico.

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Pino, Rius Antoni del. "Development and application of analytical methods to characterise processed fruit and vegetable products." Doctoral thesis, Universitat de Lleida, 2017. http://hdl.handle.net/10803/459296.

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Aquesta tesis es centra en el desenvolupament i aplicació de metodologies analítiques per caracteritzar els metabòlits en productes de fruita i verdura processats provinents de l'agroindústria. En aquesta tesi s'han desenvolupat mètodes de UPLC-PDA-MS / MS per determinar els carotenoides i clorofil•les i els seus derivats en productes de fruites i verdures. El mètode d'anàlisi de carotenoides es va aplicar per estudiar els canvis en el perfil de carotenoides en sucs de fruita monovarietals i el desenvolupat per analitzar les clorofil•les i derivats es va aplicar per avaluar la transformació de les clorofil•les en productes vegetals processats. A més, es van aplicar diverses metodologies i tècniques per caracteritzar fibres processades obtingudes a partir de subproductes de la indústria del suc. Els compostos fenòlics de les fibres es van determinar i es va establir una comparació del seu contingut i perfil amb el determinat en fruita fresca liofilitzada. També es varen caracteritzar els metabòlits primaris i secundaris, i es van comparar amb les dades reportades en la literatura per a la fruita fresca corresponent. Finalment, amb l'objectiu d'obtenir una metodologia per autenticar els sucs de fruita monovarietals, es va utilitzar la resonancia magnètica nuclear (RMN) per analitzar metabòlits primaris i UPLC-PDA-MS / MS per determinar els compostos fenòlics els quals combinats amb eines quimiomètriques com ara l'anàlisi de components principals.
This thesis presents an approach to the development and application of analytical methodologies to characterise metabolite compounds in processed fruit and vegtable products from the agroindustry. In this thesis were developed UPLC-PDA-MS/MS methods to determine carotenoids and chlorophylls and their derivatives in processed fruit and vegetable products. The method developed to analyse carotenoids was applied to study the changes in the carotenoid profile of commercial monovarietal fruit juices and the developed to analyse chlorophyll and their derivatives was applied to assess the fate of chlorophylls in processed vegetable products. In addition, various methodologies and techniques were applied to characterise processed fibres obtained from juice industry by-products. The phenolic compounds of the fibres were determined and a comparison of its content and profile in lyophilised fresh fruit was established. Additionally metabolite profile and content of the fibres was characterised, and compared with data reported in previous literature for the corresponding fresh fruit. Finally, with the aim of obtain a methodology to authenticate monovarietal fruit juices, were used nuclear magnetic resonance spectroscopy (NMR) to analyse primary metabolites and UPLC-PDA-MS/MS to determine phenolic compounds combined chemometric tools such as principal component analysis
Esta tesis se centra en el desarrollo y aplicación de metodologías analíticas para caracterizar los metabolitos en productos de fruta y verdura procesados provenientes de la agroindustria. En esta tesis se han desarrollado métodos de UPLC-PDA-MS / MS para determinar los carotenoides y clorofila • las y sus derivados en productos de frutas y verduras. El método de análisis de carotenoides se aplicó para estudiar los cambios en el perfil de carotenoides en zumos de fruta monovarietales y el desarrollado para analizar las clorofila • las y derivados se aplicó para evaluar la transformación de las clorofila • en productos vegetales procesados. Además, se aplicaron diversas metodologías y técnicas para caracterizar fibras procesadas obtenidas a partir de subproductos de la industria del zumo. Los compuestos fenólicos de las fibras se determinaron y se estableció una comparación de su contenido y perfil con el determinado en fruta fresca liofilizada. También se caracterizaron los metabolitos primarios y secundarios, y se compararon con los datos reportados en la literatura para la fruta fresca correspondiente. Finalmente, con el objetivo de obtener una metodología para autenticar los zumos de fruta monovarietales, se utilizó la resonancia magnética nuclear (RMN) para analizar metabolitos primarios y UPLC-PDA-MS / MS para determinar los compuestos fenólicos los cuales combinados con herramientas quimiométricas como el análisis de componentes principales.

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Beisken, Stephan Andreas. "Informatics for tandem mass spectrometry-based metabolomics." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708325.

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Luan, Hemi. "Mass spectrometry based metabolomics for biomarkers of Parkinson's disease." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/396.

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Increasing evidence has shown that abnormal metabolic phenotypes in body fluids reflect the pathogenesis and pathophysiology of Parkinson's disease (PD). However, the relationship between metabolic phenotypes and PD is not fully understood. Mass spectrometry (MS) based metabolomics is a powerful technique, which was frequently used for the sensitive and reproducible detection of hundreds to thousands of metabolites in biofluid samples.. Here we developed and performed MS-based metabolomics studies involving hundreds of human urine samples with data acquired from multiple analytical batches for surveying potential biomarkers of PD. A new software statTarget was developed and introduced. Protocols for liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) were developed, including sample preparation, data acquisition, quality controls, quality assurance and data analysis. Urinary metabolites from a total of 401 clinical urine samples collected from 106 idiopathic PD patients and 104 normal control subjects were profiled by using LC-MS. Quality control (QC) strategy has been performed in MS-based metabolomics for high reproducibility and accuracy of MS data. GC-MS with methyl chloroformate (MCF) derivatization was used for profiling highly polar metabolites in patients with early-, middle- and advanced-stage PD. Our study revealed the significant correlation between clinical phenotypes and urinary metabolite profiles. Comprehensive metabolomics was successfully developed with the goal of identifying urinary metabolite markers that can be used for evaluating the development of PD. A group of 18 metabolites have shown not only a high discriminating ability for the early-stage PD patients but also accurately distinguished the middle- and advanced- stages patients from control subjects. For the evaluation of PD, 18 metabolites showed good potential as metabolite markers with related metabolic pathway variations observed in branched chain amino acid metabolism, glycine derivation, steroid hormone biosynthesis, tryptophan metabolism, and phenylalanine metabolism.. We have further performed targeted analysis of potential biomarkers by using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and GC-MS. The UPLC-MS/MS method was developed and optimized for detecting the concentration variation of metabolites in tryptophan metabolism for alpha-synuclein over-expressed flies (Parkinson's disease model). The altered tryptophan metabolism was proved as one of the common metabolite signatures between PD patients and alpha-synuclein over-expressed fly model of PD, and thus may be used for developing potential markers of the disease and evaluating the efficacy of novel therapeutic agents. An asymmetric labeling strategy and positive chemical ionization gas chromatography-tandem mass spectrometry (PCI-GC-MS-MS) approach was developed for the determination of non-amino organic acids and amino acids, as well as short chain fatty acids. Carboxylic and amino groups could be selectively labelled by propyl and ethyl groups, respectively. The specific neutral losses of C3H8O (60 Da), C3H5O2 (74 Da) and C4H8O2 (88 Da) were useful in the selective identification for qualitative analysis of organic acids and amino acid derivatives. The developed PCI-GC-MS/MS method showed good reproducibility and linear range.. In summary, metabolomics study has its inherent advantage in the characterization of biomarkers for the development of PD and may bring new scientific knowledge as well as impact on the progression of PD and other related neurodegenerative diseases.

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Jones, Christina Michele. "Applications and challenges in mass spectrometry-based untargeted metabolomics." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54830.

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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.

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Abdelrazig, Salah M. A. "Mass spectrometry for high-throughput metabolomics analysis of urine." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/30600/.

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Direct electrospray ionisation-mass spectrometry (direct ESI-MS), by omitting the chromatographic step, has great potential for application as a high-throughput approach for untargeted urine metabolomics analysis compared to liquid chromatography-mass spectrometry (LC-MS). The rapid development and technical innovations revealed in the field of ambient ionisation MS such as nanoelectrospray ionisation (nanoESI) chip-based infusion and liquid extraction surface analysis mass spectrometry (LESA-MS) suggest that they might be suitable for high-throughput metabolomics analysis. In this thesis, LC-MS and high-throughput direct ESI-MS methods using high resolution orbital trap mass spectrometer were developed and validated for untargeted metabolomics of human urine. Three different direct ESI-MS techniques were explored and compared with LC-MS: flow injection electrospray ionisation-MS (FIE-MS), chip-based infusion and LESA-MS of dried urine spots on a cell culture slide. A high-throughput sample preparation protocol was optimised using in-house artificial urine. Urine samples after consumption of green tea and healthy controls were used as a model to explore the performance and classification ability of the direct ESI-MS. High-throughput data pre-processing and multivariate analysis protocols were established for each method. The developed methods were finally applied for the analysis of clinical urine samples for biomarker discovery and to investigate the metabolic changes in osteoarthritis and malaria. Also, the methods were applied to study the effect of oligofructose diet on the gut microbial community of healthy subjects. The analytical performance of the methods for urine metabolomics was validated using quality control (QC) and principal component analysis (PCA) approaches. Rigorous validation including cross-validation, permutation test, prediction models and area under receiver operating characteristic (ROC) curve (AUC) was performed across the generated datasets using the developed methods. Analysis of green tea urine samples generated 4128, 748, 1064 and 1035 ions from LC-MS, FIE-MS, chip-based infusion and LESA-MS analysis, respectively. A selected set of known green tea metabolites in urine were used to evaluate each method for detection sensitivity. 15 metabolites were found with LC-MS compared to 8, 5 and 6 with FIE-MS, chip-based infusion and LESA, respectively. The developed methods successfully differentiated between the metabolic profiles of osteoarthritis active patients and healthy controls (Q2 0.465 (LC-MS), 0.562 (FIE-MS), 0.472 (chip-based infusion) and 0.493 (LESA-MS)). The altered level of metabolites detected in osteoarthritis patients showed a perturbed activity in TCA cycle, pyruvate metabolism, -oxidation pathway, amino acids and glycerophospholipids metabolism, which may provide evidence of mitochondrial dysfunction, inflammation, oxidative stress, collagen destruction and use of lipolysis as an alternative energy source in the cartilage cells of osteoarthritis patients. FIE-MS, chip-based infusion and LESA-MS increased the analysis throughput and yet they were able to provide 33%, 44% and 44%, respectively, of the LC-MS information, indicating their great potential for diagnostic application in osteoarthritis. Malaria samples datasets generated 9,744 and 576 ions from LC-MS and FIE-MS, respectively. Supervised multivariate analysis using OPLS-DA showed clear separation and clustering of malaria patients from controls in both LC-MS and FIE-MS methods. Cross-validation R2Y and Q2 values obtained by FIE-MS were 0.810 and 0.538, respectively, which are comparable to the values of 0.993 and 0.583 achieved by LC-MS. The sensitivity and specificity were 80% and 77% for LC-MS and FIE-MS, respectively, indicating valid, reliable and comparable results of both methods. With regards to biomarker discovery, altered level of 30 and 17 metabolites were found by LC-MS and FIE-MS, respectively, in the urine of malaria patients compared to healthy controls. Among these metabolites, pipecolic acid, taurine, 1,3-diacetylpropane, N-acetylspermidine and N-acetylputrescine may have the potential of being used as biomarkers of malaria. LC-MS and FIE-MS were able to separate urine samples of healthy subjects on oligofructose diet from controls (specificity/sensitivity 80%/88% (LC-MS) and 71%/64% (FIE-MS)). An altered level of short chain fatty acids (SCFAs), fatty acids and amino acids were observed in urine as a result of oligofructose intake, suggesting an increased population of the health-promoting Bifidobacterium and a decreased Lactobacillus and Enterococcus genera in the colon. In conclusion, the developed direct ESI-MS methods demonstrated the ability to differentiate between inherent types of urine samples in disease and health state. Therefore they are recommended to be used as fast diagnostic tools for clinical urine samples. The developed LC-MS method is necessary when comprehensive biomarker screening is required.

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Robinson, Sarah Jane. "Mass spectrometric approaches to carbohydrate metabolomics in monocotyledons." Thesis, University of York, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.423682.

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Kapoore, Rahul Vijay. "Mass spectrometry based hyphenated techniques for microalgal and mammalian metabolomics." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/8234/.

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In metabolomics, the analytical challenge is to capture the chemical diversity of the metabolome. With the current technologies only a portion of the metabolome can be analysed. As a result there is a drive to direct significant analytical efforts towards capturing the metabolome or changes in the metabolome reliably and reproducibly in biological systems. Apart from analytical challenges, the challenges also include development of appropriate methodologies to quench and extract metabolites which is a crucial parameter in sample preparation and is required to achieve an accurate representation of phenotype. This thesis focuses on addressing both the challenges in mammalian and microalgal metabolomics. Metabolomics in cancer research is gaining momentum as a tool to understand the molecular mechanism of disease progression and for the identification of specific biomarkers which may assist distinguishing between normal, benign and metastatic cancer states. In our first investigation we developed GC-MS based modified direct cell scraping, bead harvesting and LN2 methods for harvesting three adherently grown mammalian cell lines (two breast cancer cell lines MDA-MB 436, MCF7 and an endothelial cell line HMEC1) which provided rapid and reliable route with three fold improved metabolome coverage and reduced the artifacts due to metabolome leakage compared to conventional methods. Later optimized treatments were employed and the influence of various washing and quenching solvents (buffered/unbuffered) on metabolite leakage was investigated for metastatic cancer cell line MDA-MB-231. This identified one washing step with PBS followed by quenching with 60% methanol (buffered with HEPES) as the best washing and quenching solvents. Further validation and comparison of proposed workflows for metabolomic study of two metastatic TNBC cell lines (MDA-MB-231 and MDA-MB-436) resulted in recovery of 154 unique metabolites and demonstrated the robustness and reliability of these methods in pathway based analysis in cancer. In case of GC-MS based microalgal metabolomics, with comprehensive evaluation of selected quenching and extraction methods in model microalga C. reinhardtii, we have successfully demonstrated that the choice of quenching and extraction solvents have significant impact on recovery of different classes of metabolites. Our results clearly indicate that 60% methanol (buffered with HEPES) and 25 % aqueous methanol are the best suited quenching and extraction solvent respectively for untargeted metabolomic analysis of C. reinhardtii, as the highest number of metabolites belonging to various chemical classes were recovered with good intensities and reproducibilities with this miniaturized proposed method compared to other evaluated methods. Later impact of various stages involved in biodiesel production workflow from microalga on recovery of biodiesel was assessed in three microalgal species namely C. reinhardtii, D. salina and N. salina. Within which we have developed an optimized GC-FID method and miniaturized direct TE method for quantification of fatty acids, which can be applied to a small amount of biomass and saves tremendous amounts of time, solvents and reagents required, is less expensive and uses environment friendly solvents making it more suitable for sustainable large scale production. In our final investigation, we directed our efforts towards preliminary optimization and comparative analysis of HILIC and IP-RP-HPLC based separation for the retention and separation of specific metabolites classes. This identified HILIC as the best available column till date for untargeted metabolomic studies. The descriptive understanding gained from each of these investigations provides greater insight into biology of mammalian and algal systems by improving the metabolome coverage for various metabolite classes. These insights illustrating the underlying molecular pathways involved in respective biology's, will help scientific communities in identifying as-of-yet-missing reactions in the metabolic network. In addition these insights will surely help in generating many hypothesis based investigations in microalgal and cancer community.

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Tengstrand, Erik. "Data analysis of non-targeted mass spectrometry experiments." Doctoral thesis, Stockholms universitet, Institutionen för miljövetenskap och analytisk kemi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-116820.

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Data processing tools are valuable to the analytical chemist as they can speed up the analysis, and sometimes solve problems that are not feasible to solve in a traditional manner. However, the complexity of many data processing tools can make their use daunting for the inexperienced user. This thesis includes two applications and two tools for data processing. The first application focuses on minimizing the manual input, reducing the time required for a simple task. The second application required more manual input, in the form of parameter selection, but process far more data. The data processing tools both include features that simplify the manual work required. The first by including visual diagnostics tools that helps in setting the parameters. The second via internal validation that makes the tool’s process more robust and reliable, and thereby less sensitive to small changes in the parameters. No matter how good or precise a data processing tool is, if it is so cumbersome that it is not used by the analytical chemists that need it, it is useless. Therefore, the main focus of this thesis is to make data processing easier.

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Submitted.

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Malkar, Aditya. "Analytical methods based on ion mobility and mass spectrometry for metabolomics." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/14524.

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Travelling wave ion mobility spectrometry (TWIMS) in combination with ultra-high performance liquid chromatography (UHPLC) and mass spectrometry (MS) has been applied successfully for the untargeted, global metabolic profiling of biofluids such as mouse plasma and saliva. Methods based on UHPLC-MS alone and in combination with ion mobility spectrometry (UHPLC-IM-MS) have been developed and validated for the untargeted metabolite profiling of saliva, obtained non-invasively by passive drool. Three separate metabolic profiling studies have been carried out in conjunction with bioinformatics strategies to identify potential metabolomic biomarker ions that are associated with efficacy of rice bran in colorectal cancer, physiological stress and that have the potential for the diagnosis of asthma. The advantages offered by the utility of ion mobility in UHPLC-MS based metabolic profiling studies, including the increased analytical space, mass spectral clean-up of contaminants such as PEG post-UHPLC-IM-MS analysis, enhancement of the selectivity of targeted metabolites as well as the potential for the identification of metabolites by comparison of ion mobility drift times have been highlighted. Ten potential metabolic biomarker ions of asthma have been identified from the moderate asthmatics from untargeted metabolite profiling of saliva by UHPLC-MS. A predictive model based on partial least squares discriminant analysis (PLS-DA) has been constructed using these ten discriminant ions, which demonstrates good predictive capability for moderate asthmatics and controls. Potential metabolic biomarker ions of physiological stress have been identified through untargeted metabolite profiling analysis of saliva samples collected before and after exercise by UHPLC-IM-MS. Valerolactam has been identified as a potential biomarker of physiological stress from saliva by comparison of retention time, ion mobility drift time and MS/MS spectra with a standard of δ-valerolactam.

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Huang, He. "Mass Spectrometry-Based Metabolomics: Platform Development and Application to Neurodegenerative Disease." University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1496678565947565.

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Gipson, Geoffrey T. Sokhansanj Bahrad. "Discovery Of discriminative LC-MS and 1H NMR metabolomics markers /." Philadelphia, Pa. : Drexel University, 2008. http://hdl.handle.net/1860/2766.

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Domingo, Almenara Xavier. "Automated mass spectrometry-based metabolomics data processing by blind source separation methods." Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/397799.

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Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i adquirir nous coneixements biològics.Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i adquirir nous coneixements biològics.
Una de las principales limitaciones de la metabolómica es la transformación de datos crudos en información biológica. Además, la metabolómica basada en espectrometría de masas genera grandes cantidades de datos complejos caracterizados por la co-elución de compuestos y artefactos experimentales. El objetivo de esta tesis es desarrollar estrategias automatizadas basadas en deconvolución ciega de la señal para mejorar las capacidades de los métodos existentes que tratan las limitaciones de los diferentes pasos del procesamiento de datos en metabolómica. El objetivo de esta tesis es también desarrollar herramientas capaces de ejecutar el flujo de trabajo del procesamiento de datos en metabolómica, que incluye el preprocessamiento de datos, deconvolución espectral, alineamiento e identificación. Como resultado, tres nuevos métodos automáticos para deconvolución espectral basados en deconvolución ciega de la señal fueron desarrollados. Estos métodos fueron incluidos en dos herramientas computacionales que permiten convertir automáticamente datos crudos en información biológica interpretable y por lo tanto, permiten resolver hipótesis biológicas y adquirir nuevos conocimientos biológicos.
One of the major bottlenecks in metabolomics is to convert raw data samples into biological interpretable information. Moreover, mass spectrometry-based metabolomics generates large and complex datasets characterized by co-eluting compounds and with experimental artifacts. This thesis main objective is to develop automated strategies based on blind source separation to improve the capabilities of the current methods that tackle the different metabolomics data processing workflow steps limitations. Also, the objective of this thesis is to develop tools capable of performing the entire metabolomics workflow for GC--MS, including pre-processing, spectral deconvolution, alignment and identification. As a result, three new automated methods for spectral deconvolution based on blind source separation were developed. These methods were embedded into two computation tools able to automatedly convert raw data into biological interpretable information and thus, allow resolving biological answers and discovering new biological insights.

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Hodson, M. P. "The application of liquid chromatography-mass spectrometry to metabolomics, toxicology and systems biology." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604133.

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In this thesis LC-MS has initially been applied to investigate metabolic changes associated with gender, diurnal variation and time to define how key physiological differences affect the metabolome in rats. In the case of gender, a clear metabolic separation of females from males was observed based upon the urinary excretion of a sulphated endogenous steroid metabolite and this illustrates the benefits attained by improving metabolome coverage, as this metabolite is not detected by ¹H NMR spectroscopy. After development of acquisition and data processing methods, ultra performance LC-MS (and ¹H NMR spectroscopy) was applied to a 3-dose level toxicological evaluation of bromobenzene. As well as increasing metabolome coverage, the sensitivity of LC-MS resulted in improved accuracy of measurement of a candidate biomarker, 5-oxoproline. This additional information lent weight to the possibility that 5-oxoproline could be used as a pre-emptive indicator of glutathione depletion (and oxidative stress) based upon smaller but convincing changes elicited in doses not found to result in the stark pathological damage of the high dose administration. LC-MS was then applied to investigations of dietary restriction, not only to characterise the metabolic changes observed due to reduce energy intake but also as a direct comparator to the bromobenzene study in an attempt to delineate the effects of reduced feeding from the toxicological impact on the metabolic profile. The metabolomic information was then combined with transcriptomic analyses to place the metabolomic output in a systems biology context. Alterations in energy metabolism were detected using both metabolomics and transcriptomics and many metabolic changes due to dietary restriction were also observed after administration of bromobenzene, particularly relating to glycolysis/gluconeogenesis, tricarboxyic acid cycle, amino acid metabolism and fatty acid metabolism.

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Tong, Lily Victoria. "Development and application of mass spectrometry-based metabolomics methods for disease biomarker identification." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/46014.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2008.
MIT Science Library copy printed in leaves.
Includes bibliographical references (p. 281-299).
Human societies face diverse health challenges including a rapidly aging population, rising incidence of metabolic disease, and increasing antibiotic resistance. These problems involve complex interactions between genes and environment and are often not well understood. To address these challenges, high-throughput and reproducible advances in genome sequencing, transcript measurement, and protein measurement have been developed; the information resulting from these techniques has led to an increased understanding of cellular function and the identification of number of novel biomarkers for a variety of diseases.In recent years, the monitoring of such systems-level cellular behavior has naturally extended to the metabolite level, leading to the study of metabolomics. The rise of metabolomics corresponds hand in hand with the desire to address some of the phenotypic informational gaps left behind from genomics, transcriptomics, and proteomics. The study of metabolites carries several advantages. First, the number of metabolites in the human "metabolome," estimated at 2500 metabolites, remains a tractable number for analysis as compared to the 35,000 genes and 100,000-1,000,000 proteins. Metabolites also reliably provide an instantaneous "downstream" biochemical snapshot of a cell, and the typical metabolomics analysis is carried out on relatively noninvasive patient fluids such as urine or plasma.The goal of this thesis is to design, develop, and apply methods for the metabolomic analysis of blood via gas chromatography-mass spectrometry (GC-MS) instrumentation. Despite initial successes, methods in metabolomics vary widely and have not been standardized. This was first addressed via the optimization of the instrumentation itself, a topic rarely addressed in the literature but crucial toward the reliable identification of biomarkers.
(cont) We investigated the different GC-MS parameters found to have the largest impact on data quality and employed D-optimal design to pare down the search space to a feasible number of experiments. These parameters were then optimized via response surface estimation to ensure maximum reproducibility and sensitivity of the entire metabolite mixture. The results from this optimization constitute a significant improvement upon existing methods in the literature.Next, methods were developed for the bioinformatics analysis of raw GC-MS data. Current techniques for metabolite tracking are non-systematic and typically require the laborious use of reference libraries. We developed a method to track conserved metabolites across GC-MS replicates and conditions with the optional use of reference libraries and validated it an E. coli dataset and the differential detection of metabolites in a spiked mixture. In addition, we investigated the best methods for the imputation of missing data as applied to three different metabolomics datasets; to this date, missing data imputation has not been comprehensively addressed in the metabolomics literature, and many methods currently used are needlessly inaccurate. After investigating eight different imputation methods via three deletion methods, it was concluded that k-nearest neighbor algorithms were the best and most accurate method for data imputation.Finally, the instrumental parameter optimization and metabolite tracking methods were applied to the problem of predicting patient mortality in end-stage renal disease (ESRD). Although ESRD is a complex and well-studied disease, known risk factors only account for 50% of patient deaths, and prediction accuracies for the disease remain relatively low; in addition, mortality rates in the first 90 days of dialysis treatment are double that after 90 days.
(cont.) We sought to investigate whether the addition of metabolomic information would result in increased accuracy of mortality prediction. One hundred twenty patient samples were obtained from a national dialysis study (equally representing death and survival within 90 days of starting dialysis) and analyzed according to our protocol. Two feature selection algorithms were applied to identify significant metabolites distinguishing death and survival, and the corresponding models resulted in improved receiver-operating characteristic (ROC) curve areas of 0.85 and 0.93. This result constitutes a significant improvement from existing clinical models, which at best result in ROC curve areas of 0.80. Based on this work, we hypothesize that our observed differential fatty acid concentrations are indicative of impaired fatty acid oxidation, leading to insulin resistance in ESRD patients (regardless of Type II diabetes status) and eventually, patient mortality.
by Lily Victoria Tong.
Ph.D.

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Alamri, Hassan. "DISCOVERY OF PATHWAYS LINKED TO CARDIOVASCULAR DISEASE RISK BY MASS SPECTROMETRY-BASED METABOLOMICS." Cleveland State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu1513168141266115.

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Li, Xiaona. "Mass spectrometry-based metabolomics study on KRAS-mutant colorectal cancer and rheumatoid arthritis." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/540.

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Ample studies have shown that perturbation of metabolic phenotype is correlated with gene mutation and pathogenesis of colorectal cancer (CRC) and rheumatoid arthritis (RA). Mass spectrometry (MS)-based metabolomics as a powerful and stable approach is widely applied to bridge the gap from genotype/metabolites to phenotype. In CRC suffers, KRAS mutation accounts for 35%-45%. In previous study, SLC25A22 that encodes the mitochondrial glutamate transporter was found to be overexpressed in CRC tumor and thus to be essential for the proliferation of CRC cells harboring KRAS mutations. However, the role of SLC25A22 on metabolic regulation in KRAS-mutant CRC cells has not been comprehensively characterized. We performed non-targeted metabolomics, targeted metabolomics and isotope kinetic analysis of KRAS-mutant DLD1 cells with or without SLC25A22 knockdown using ultra-high performance liquid chromatography (UHPLC) coupled to Orbitrap MS and tandem MS (MS/MS). In global metabolomics analysis, 35 differentially regulated metabolites were identified, which were primarily involved in alanine, aspartate and glutamate metabolism, urea cycle and polyamine metabolism. Then targeted metabolomics analysis on intracellular metabolites, including tricarboxylic acid (TCA) cycle intermediates, amino acids and polyamines, was established by using LC-MS/MS coupled with an Amide BEH column. Targeted metabolomics analysis revealed that most TCA cycle intermediates, aspartate (Asp)-derived asparagine, alanine and ornithine (Orn)-derived polyamines were strongly down-regulated in SLC25A22 knockdown cells. Moreover, the targeted kinetic isotope analysis using [U-13C5]-glutamine as isotope tracer showed that most of the 13C-labeled TCA cycle intermediates were down-regulated in SLC25A22-silencing cells. Orn-derived polyamines were significantly decreased in SLC25A22 knockdown cells and culture medium. Meanwhile, accumulation of Asp in knockdown of GOT1 cells indicated that oxaloacetate (OAA) was majorly converted from Asp through GOT1. Exogenous addition of polyamines could significantly promote cell proliferation in DLD1 cells, highlighting their potential role as oncogenic metabolites that function downstream of SLC25A22-mediated glutamine metabolism. SLC25A22 acts as an essential metabolic regulator during CRC progression as promotes the synthesis of TCA cycle intermediates, Asp-derived amino acids and polyamines in KRAS-mutant CRC cells. Moreover, OAA and polyamine could promote KRAS-mutant CRC cell growth and survival. Rheumatoid arthritis (RA) is a chronic, inflammatory and symmetric autoimmune disease and a major cause of disability. However, there is insufficient pathological evidence in term of metabolic signatures of rheumatoid arthritis, especially the metabolic perturbation associated with gut microbiota (GM). Based on consistent criteria without special diet and therapeutic intervention to GM, we enrolled 50 RA patients and 50 healthy controls. On basis of the platform of UHPLC-MS and GC-MS, were performed for the non-targeted metabolomics to investigate alterations of endogenous metabolites in response to RA inflammation and interaction with GM. 32 and 34 significantly changed metabolites were identified in urine and serum of patients with RA, respectively. The altered metabolites were identified by HMDB, METLIN database or authentic standards, and mostly metabolites were attributed into tryptophan and phenylalanine metabolism, valine, leucine and isoleucine biosynthesis, aminoacyl-tRNA biosynthesis and citrate cycle. To obtain alterations of more components in tryptophan and phenylalanine metabolism, we developed and validated a targeted metabolomics method of 19 metabolites by using LC-QqQ MS. Combining the results of targeted metabolomics with global metabolomics, significantly up-regulated kynurenine (KYN), anthranilic acid (AA) and 5-hydroxylindoleacetic acid (HIAA) simultaneously in urine and serum was found to implicate the activation of tryptophan metabolism under the condition of RA, which acted pro-inflammatory roles in inflammation and was closely correlated with GM. IDO/TDO functioned as a pro-inflammation mediator was overexpressed in RA patients. Urinary kynurenic acid and serum serotonin that have impacts on anti-inflammation in immune system were down-regulated in RA patients. The levels of phenylacetic acid and phenyllactic acid serving as a pro-inflammatory and an anti-inflammatory agent, respectively, increased in serum of patients with RA. Moreover, certain essential amino acids (EAAs), and mostly conditional EAAs were decreased in RA patients, which have been reported to inhibit cell proliferation of immune cells. In particular, deficiency of branched chain amino acids (BCAAs, valine and isoleucine) was observed in serum of patients with RA, which may lead to muscle loss and cartilage damage. The specificity of all altered metabolites resulted from RA was considerably contributed through the GM-derived metabolites. The findings revealed that GM-modulated RA inflammation was mainly resulted from tryptophan and phenylalanine metabolism, and amino acid biosynthesis, which may provide more information for better understanding the RA mechanism.

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Gullberg, Jonas. "Metabolomics : a tool for studying plant biology /." Umeå : Dept. of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, 2005. http://epsilon.slu.se/200588.pdf.

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Mörén, Lina. "Metabolomics and proteomics studies of brain tumors : a chemometric bioinformatics approach." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-111309.

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The WHO classification of brain tumors is based on histological features and the aggressiveness of the tumor is classified from grade I to IV, where grade IV is the most aggressive. Today, the correlation between prognosis and tumor grade is the most important component in tumor classification. High grade gliomas, glioblastomas, are associated with poor prognosis and a median survival of 14 months including all available treatments. Low grade meningiomas, usually benign grade I tumors, are in most cases cured by surgical resection. However despite their benign appearance grade I meningiomas can, without any histopathological signs, in some cases develop bone invasive growth and become lethal. Thus, it is necessary to improve conventional treatment modalities, develop new treatment strategies and improve the knowledge regarding the basic pathophysiology in the classification and treatment of brain tumors. In this thesis, both proteomics and metabolomics have been applied in the search for biomarkers or biomarker patterns in two different types of brain tumors, gliomas and meningiomas. Proteomic studies were carried out mainly by surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS). In one of the studies, isobaric tags for relative and absolute quantitation (iTRAQ) labeling in combination with high-performance liquid chromatography (HPLC) was used for protein detection and identification. For metabolomics, gas-chromatography time-of-flight mass spectrometry (GC-TOF-MS) has been the main platform used throughout this work for generation of robust global metabolite profiles in tissue, blood and cell cultures. To deal with the complexity of the generated data, and to be able to extract relevant biomarker patters or latent biomarkers, for interpretation, prediction and prognosis, bioinformatic strategies based on chemometrics were applied throughout the studies of the thesis. In summary, we detected differentiating protein profiles between invasive and non-invasive meningiomas, in both fibrous and meningothelial tumors. Furthermore, in a different study we discovered treatment induce protein pattern changes in a rat glioma model treated with an angiogenesis inhibitor. We identified a cluster of proteins linked to angiogenesis. One of those proteins, HSP90, was found elevated in relation to treatment in tumors, following ELISA validation. An interesting observation in a separate study was that it was possible to detect metabolite pattern changes in the serum metabolome, as an effect of treatment with radiotherapy, and that these pattern changes differed between different patients, highlighting a possibility for monitoring individual treatment response. In the fourth study of this work, we investigated tissue and serum from glioma patients that revealed differences in the metabolome between glioblastoma and oligodendroglioma, as well as between oligodendroglioma grade II and grade III. In addition, we discovered metabolite patterns associated to survival in both glioblastoma and oligodendroglioma. In our final work, we identified metabolite pattern differences between cell lines from a subgroup of glioblastomas lacking argininosuccinate synthetase (ASS1) expression, (ASS1 negative glioblastomas), making them auxotrophic for arginine, a metabolite required for tumor growth and proliferation, as compared to glioblastomas with normal ASS1 expression (ASS1 positive). From the identified metabolite pattern differences we could verify the hypothesized alterations in the arginine biosynthetic pathway. We also identified additional interesting metabolites that may provide clues for future diagnostics and treatments. Finally, we were able to verify the specific treatment effect of ASS1 negative cells by means of arginine deprivation on a metabolic level.

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Fernández, Albert Francesc. "Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomics." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/283980.

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Liquid Chromatography-Mass Spectrometry (LC/MS) instruments are widely used in Metabolomics. To analyse their output, it is necessary to use computational tools and algorithms to extract meaningful biological information. The main goal of this thesis is to provide with new computational methods and tools to process and analyse LC/MS datasets in a metabolomic context. A total of 4 tools and methods were developed in the context of this thesis. First, it was developed a new method to correct possible non-linear drift effects in the retention time of the LC/MS data in Metabolomics, and it was coded as an R package called HCor. This method takes advantage of the retention time drift correlation found in typical LC/MS data, in which there are chromatographic regions in which their retention time drift is consistently different than other regions. Our method makes the hypothesis that this correlation structure is monotonous in the retention time and fits a non-linear model to remove the unwanted drift from the dataset. This method was found to perform especially well on datasets suffering from large drift effects when compared to other state-of-the art algorithms. Second, it was implemented and developed a new method to solve known issues of peak intensity drifts in metabolomics datasets. This method is based on a two-step approach in which are corrected possible intensity drift effects by modelling the drift and then the data is normalised using the median of the resulting dataset. The drift was modelled using a Common Principal Components Analysis decomposition on the Quality Control classes and taking one, two or three Common Principal Components to model the drift space. This method was compared to four other drift correction and normalisation methods. The two-step method was shown to perform a better intensity drift removal than all the other methods. All the tested methods including the two-step method were coded as an R package called intCor and it is publicly available. Third, a new processing step in the LC/MS data analysis workflow was proposed. In general, when LC/MS instruments are used in a metabolomic context, a metabolite may give a set of peaks as an output. However, the general approach is to consider each peak as a variable in the machine learning algorithms and statistical tests despite the important correlation structure found between those peaks coming from the same source metabolite. It was developed an strategy called peak aggregation techniques, that allow to extract a measure for each metabolite considering the intensity values of the peaks coming from this metabolite across the samples in study. If the peak aggregation techniques are applied on each metabolite, the result is a transformed dataset in which the variables are no longer the peaks but the metabolites. 4 different peak aggregation techniques were defined and, running a repeated random sub-sampling cross-validation stage, it was shown that the predictive power of the data was improved when the peak aggregation techniques were used regardless of the technique used. Fourth, a computational tool to perform end-to-end analysis called MAIT was developed and coded under the R environment. The MAIT package is highly modular and programmable which ease replacing existing modules for user-created modules and allow the users to perform their personalised LC/MS data analysis workflows. By default, MAIT takes the raw output files from an LC/MS instrument as an input and, by applying a set of functions, gives a metabolite identification table as a result. It also gives a set of figures and tables to allow for a detailed analysis of the metabolomic data. MAIT even accepts external peak data as an input. Therefore, the user can insert peak table obtained by any other available tool and MAIT can still perform all its other capabilities on this dataset like a classification or mining the Human Metabolome Dataset which is included in the package.

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Chetwynd, Andrew John. "Development of nanoflow liquid chromatography-nanoelectrospray ionization mass spectrometry methodology for improved urine metabolomics." Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/56253/.

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Global metabolomic analysis of urine offers great potential for detection of early warning markers of disease. Current methods focus on rapid sample preparation and high throughput analyses at the expense of the detection of low abundance metabolites. The aim of this study was to develop sensitive analytical methods for metabolomic profiling. Methods were developed to use nanoflow ultra high performance liquid chromatography-nanospray ionization-mass spectrometry (nUHPLC-nESI-TOFMS), normally used for proteomics, for metabolomic analyses of urine samples. Compared with a conventional UHPLC-ESI-TOFMS, the use of a nanoflow-nanospray platform increased the sensitivity to a standard mixture of metabolites by 2-2000 fold. Highly repeatable results for retention time and metabolome peak area were achieved, where the coefficients of variation were <0.2% and <30% respectively for the majority of peaks present in the urine metabolome. To further increase sensitivity and enable small injection volumes, a sample preparation method was developed using polymeric anion and cation exchange mixed mode solid phase extraction with pre-concentration. Combined with the nano platform, this enabled the detection of low abundance signalling molecules (estrogens, eicosanoids and unconjugated androgens) not usually detected with conventional methods. A pre-analysis normalisation technique based on osmolality concentrations was used to reduce sample variability due to differing urine concentrations. These methods were used to investigate the metabolomic consequences of HIV infection and patient response to combined antiretroviral therapy (cART). No significant differences in metabolomic profiles between HIV positive and negative patients were observed. However, disruption of bile acid profiles and decreased concentrations of selected carnitines, steroid conjugates, polypeptides and nucleosides were detected in patients on cART therapy indicating disrupted lipid and protein metabolism but improved immunological function associated with antiretroviral medication. These finding highlight the importance of these newly developed SPE sample preparation and nUHPLC-nESI-TOFMS analysis methods for global profiling of the urinary metabolome.

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Yang, Kundi. "Assessing and Evaluating Biomarkers and Chemical Markers by Targeted and Untargeted Mass Spectrometry-based Metabolomics." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1605044640528563.

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Timischl, Birgit. "Hyphenated mass spectrometric methods for quantitative metabolomics in E. coli and human cells." kostenfrei, 2008. http://www.opus-bayern.de/uni-regensburg/volltexte/2008/1028/.

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Pervukhin, Anton. "Molecular formula identification using high resolution mass spectrometry algorithms and applications in metabolomics and proteomics /." kostenfrei, 2009. http://d-nb.info/1001408578/34.

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Kopka, Joachim. "Applied metabolome analysis : exploration, development and application of gas chromatography-mass spectrometry based metabolite profiling technologies." Thesis, Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2010/4059/.

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The uptake of nutrients and their subsequent chemical conversion by reactions which provide energy and building blocks for growth and propagation is a fundamental property of life. This property is termed metabolism. In the course of evolution life has been dependent on chemical reactions which generate molecules that are common and indispensable to all life forms. These molecules are the so-called primary metabolites. In addition, life has evolved highly diverse biochemical reactions. These reactions allow organisms to produce unique molecules, the so-called secondary metabolites, which provide a competitive advantage for survival. The sum of all metabolites produced by the complex network of reactions within an organism has since 1998 been called the metabolome. The size of the metabolome can only be estimated and may range from less than 1,000 metabolites in unicellular organisms to approximately 200,000 in the whole plant kingdom. In current biology, three additional types of molecules are thought to be important to the understanding of the phenomena of life: (1) the proteins, in other words the proteome, including enzymes which perform the metabolic reactions, (2) the ribonucleic acids (RNAs) which constitute the so-called transcriptome, and (3) all genes of the genome which are encoded within the double strands of desoxyribonucleic acid (DNA). Investigations of each of these molecular levels of life require analytical technologies which should best enable the comprehensive analysis of all proteins, RNAs, et cetera. At the beginning of this thesis such analytical technologies were available for DNA, RNA and proteins, but not for metabolites. Therefore, this thesis was dedicated to the implementation of the gas chromatography – mass spectrometry technology, in short GC-MS, for the in-parallel analysis of as many metabolites as possible. Today GC-MS is one of the most widely applied technologies and indispensable for the efficient profiling of primary metabolites. The main achievements and research topics of this work can be divided into technological advances and novel insights into the metabolic mechanisms which allow plants to cope with environmental stresses. Firstly, the GC-MS profiling technology has been highly automated and standardized. The major technological achievements were (1) substantial contributions to the development of automated and, within the limits of GC-MS, comprehensive chemical analysis, (2) contributions to the implementation of time of flight mass spectrometry for GC-MS based metabolite profiling, (3) the creation of a software platform for reproducible GC-MS data processing, named TagFinder, and (4) the establishment of an internationally coordinated library of mass spectra which allows the identification of metabolites in diverse and complex biological samples. In addition, the Golm Metabolome Database (GMD) has been initiated to harbor this library and to cope with the increasing amount of generated profiling data. This database makes publicly available all chemical information essential for GC-MS profiling and has been extended to a global resource of GC-MS based metabolite profiles. Querying the concentration changes of hundreds of known and yet non-identified metabolites has recently been enabled by uploading standardized, TagFinder-processed data. Long-term technological aims have been pursued with the central aims (1) to enhance the precision of absolute and relative quantification and (2) to enable the combined analysis of metabolite concentrations and metabolic flux. In contrast to concentrations which provide information on metabolite amounts, flux analysis provides information on the speed of biochemical reactions or reaction sequences, for example on the rate of CO2 conversion into metabolites. This conversion is an essential function of plants which is the basis of life on earth. Secondly, GC-MS based metabolite profiling technology has been continuously applied to advance plant stress physiology. These efforts have yielded a detailed description of and new functional insights into metabolic changes in response to high and low temperatures as well as common and divergent responses to salt stress among higher plants, such as Arabidopsis thaliana, Lotus japonicus and rice (Oryza sativa). Time course analysis after temperature stress and investigations into salt dosage responses indicated that metabolism changed in a gradual manner rather than by stepwise transitions between fixed states. In agreement with these observations, metabolite profiles of the model plant Lotus japonicus, when exposed to increased soil salinity, were demonstrated to have a highly predictive power for both NaCl accumulation and plant biomass. Thus, it may be possible to use GC-MS based metabolite profiling as a breeding tool to support the selection of individual plants that cope best with salt stress or other environmental challenges.
Die Aufnahme von Nährstoffen und ihre chemische Umwandlung mittels Reaktionen, die Energie und Baustoffe für Wachstum und Vermehrung bereitstellen, ist eine grundlegende Eigenschaft des Lebens. Diese Eigenschaft wird Stoffwechsel oder, wie im Folgenden, Metabolismus genannt. Im Verlauf der Evolution war alles Leben abhängig von solchen Reaktionen, die essentielle und allen Lebensformen gemeinsame Moleküle erzeugen. Über diese sogenannten Primärmetabolite hinaus sind hochdiverse Reaktionen entstanden. Diese erlauben Organismen, einzigartige sogenannte Sekundärmetabolite zu produzieren, die in der Regel einen zusätzlichen Überlebensvorteil vermitteln. Die Gesamtheit aller Metabolite, die von dem komplexen Reaktionsnetzwerk in Organismen erzeugt werden, nennt man seit 1998 das Metabolom. Die Größe des Metaboloms kann nur geschätzt werden. Neben der Gesamtheit aller Metabolite werden heute drei weitere Arten an Molekülen als wesentlich betrachtet, um die Phänomene des Lebens zu verstehen: erstens die Proteine, deren Summe, das Proteom, auch die Enzyme einschließt, die die obigen metabolischen Reaktionen durchführen, zweitens die Ribonukleinsäuren (RNS), deren Gesamtheit als Transkriptom bezeichnet wird, und drittens die doppelsträngige Desoxyribonukleinsäure (DNS), die das Genom, die Summe aller Gene eines Organismus, ausmacht. Die Untersuchung aller dieser vier molekularen Ebenen des Lebens erfordert Technologien, die idealerweise die vollständige Analyse der Gesamtheit aller DNS-, RNS-, Protein-Moleküle, bzw. Metabolite erlauben. Zu Beginn meiner Arbeiten waren solche Technologien für DNS, RNS, und Proteine verfügbar, aber nicht für Metabolite. Aus diesem Grund habe ich meine Forschungstätigkeit auf das Ziel ausgerichtet, so viele Metabolite wie irgend möglich in einer gemeinsamen Analyse zu erfassen. Zu diesem Zweck habe ich mich auf eine einzelne Technik, nämlich die gekoppelte Gaschromatographie und Massenspektrometrie, kurz GC-MS, konzentriert. Nicht zuletzt durch meine Arbeiten ist GC-MS heute eine der am häufigsten angewandten Technologien und unverzichtbar für das breite Durchmustern der Metabolite. Neben der Etablierung der grundlegenden GC-MS-Profilanalyse-Technologie liegen die Haupterrungenschaften meiner Arbeiten sowohl in den technischen Neuerungen als auch in den Einsichten in metabolische Mechanismen, die es Pflanzen erlauben, erfolgreich auf Umwelteinflüsse zu reagieren. Die technologischen Errungenschaften waren erstens wesentliche Beiträge zur Labor-Automatisierung und zur Auswertung von modernen, auf Flugzeitmassenspektrometrie beruhenden, GC-MS-Profilanalysen, zweitens die Entwicklung einer entsprechenden Prozessierungs-Software, genannt TagFinder, und drittens die Etablierung einer internationalen Datensammlung zur Metabolitidentifizierung aus komplexen Mischungen. Diese massenspektralen und gaschromatographischen Daten haben seit 2005 Eingang in die von mir initiierte Entwicklung der Golm Metabolom Datenbank (GMD) gefunden, die die zunehmend wachsenden GC-MS-Referenzdaten wie auch die Metabolitprofildaten verwaltet und öffentlich zugänglich macht. Darüber hinaus wurden die langfristigen Ziele einer verbesserten Präzision für relative und absolute Quantifizierung wie auch einer Kopplung von Konzentrationsbestimmung und metabolischen Flussanalysen mittels GC-MS verfolgt. Sowohl die Stoffmengen als auch die Geschwindigkeit der Stoffaufnahme und der chemischen Umsetzung, d.h. der metabolische Fluss, sind wesentlich für neue biologische Einsichten. In diesem Zusammenhang wurde von mir die Aufnahme von CO2 durch Pflanzen, der Basis allen Lebens auf der Erde, untersucht. Angewandt auf das Temperaturstress- und Salzstressverhalten von Modell- und Kulturpflanzen, nämlich des Ackerschmalwands (Arabidopsis thaliana), des Hornklees (Lotus japonicus) und der global bedeutendsten Nutzpflanze Reis (Oryza sativa), wurden detaillierte und vergleichende neue metabolische Einsichten in den Zeitverlauf der Temperaturanpassung und die Anpassung an zunehmend salzhaltige Böden erzielt. Metabolismus verändert sich unter diesen Bedingungen allmählich fortschreitend und nicht in plötzlichen Übergängen. Am Beispiel des Hornklees konnte gezeigt werden, dass Metabolitprofilanalysen eine hohe Vorhersagekraft für die Biomasseerzeugung unter Salzeinfluss wie auch für die Aufnahme von Salz durch die Pflanze haben. So mag es in Zukunft möglich werden, GC-MS-Profilanaysen anzuwenden, um den Züchtungsprozess von Kulturpflanzen zu beschleunigen.

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Vinayavekhin, Nawaporn. "Metabolomics Strategies for Discovery of Biologically Active or Novel Metabolites." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10150.

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Along with genes and proteins, metabolites play important roles in sustaining life. There remains much to be learned about the in vivo roles of metabolites. Metabolomics is a comparative tool to study global metabolite levels in samples under various conditions. This dissertation describes the development and application of metabolomics strategies for discovery of biologically active or novel metabolites with priori knowledge about genes, proteins, or phenotypes. The power of metabolomics for discovery of novel metabolites from genes is demonstrated through the work with the pyochelin (pch) gene cluster. Comparison of the extracellular metabolomes of pch gene cluster mutants to the wild-type Pseudomonas aeruginosa (strain PA14) identified 198 ions regulated by the pch genes. In addition to known metabolites, a pair of novel metabolites were characterized as 2-alkyl-4,5-dihydrothiazole-4-carboxylates (ATCs). Subsequent assays revealed that ATCs bind iron and that their production is regulated by iron levels and dependent on pchE gene in the pch gene cluster. Metabolomics can also facilitate discovery of active metabolites from proteins, as shown in the work with orphan nuclear receptor Nur77. We applied a metabolomics platform for detected protein-metabolite interactions to identify lipids that bind to Nur77. Using this approach, we discovered that the Nur77 ligand-binding domain (Nur77LBD) enriched unsaturated fatty acids (UFAs) in tissue lipid mixtures. Subsequent biophysical and biochemical assays indicate that UFAs bind to Nur77LBD to cause changes in the conformation and oligomerization of the receptor. Last, analogous to classic fractionation experiments, metabolomics can also be applied to discover active metabolites from phenotypes. Using combination of genetics, biochemistry, and metabolomics, we identified three phenazine compounds produced by Pseudomonas aeruginosa that are toxic to the nematode Caenorhabditis elegans. 1-hydroxyphenazine, phenazine-1-carboxylic acid (PCA), and pyocyanin are capable of killing nematodes in a matter of hours. 1-hydroxyphenazine is toxic over a wide pH range, whereas the toxicities of PCA and pyocyanin are strictly pH-dependent at non-overlapping pH ranges. The diversity within a class of metabolites can be used to modulate bacterial toxicity in different environmental niches.
Chemistry and Chemical Biology

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Puschmann, Robert. "Analysis and Quantification of Inositol Poly- and Pyrophosphates by NMR Spectroscopy and Mass Spectrometry." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21044.

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Inositolpyrophosphate (PP-InsP) sind eine Gruppe sekundärer Signalmoleküle, die in einer Vielzahl zellulärer Prozesse, von Phosphathomeostase über Insulinsignalisierung bis Apoptose eine Rolle spielen. Die Art und Weise, wie PP-InsPs ihre Funktion ausführen, noch weitgehend unbekannt. Deshalb wurden zwei neue analytische Methoden basierend auf Kernspinresonanzspektroskopie und Flüssigchromatographie mit Massenspektrometrie-Kopplung (LCMS) entwickelt. Um die limitierende Sensitivität der Kernresonanzspektroskopie zu umgehen, wurde die Synthese von kernspinresonanzaktivem, 13C-markiertem Inositol optimiert. Des Weiteren wurde eine chemoenzymatische Synthese für alle Säugetier-PP-InsP-Isomere entwickelt, die auf der skalierbaren Ausfällung mittels Mg2+ Ionen basiert. Menschliche Zellen wurden mit 13C-Inositol isotopenmarkiert und in den Spektren der Zellextrakte wurde, basierend auf den PP-InsP-Standards, Fingerabdrucksignale identifiziert mit denen die Konzentrationen der dazugehörigen Moleküle bestimmt werden konnte. Die LCMS basierte Methode wurde auf dem Prinzip der Umsetzung von hochgeladenen Inositolpyrophosphaten zu ihren korrespondieren Methylestern mittels Trimethylsilyldiazomethan geplant. Die ungeladenen, permethylierten PP-InsPs wären geeignet für LC-Auftrennungen und MS-Messungen und sollten eine von Kernspinresonanzspektroskopie nicht erreichbare Sensitivität ermöglichen. Die Methode wurde mittels Inositolhexakisphosphat (InsP6), einem einfacheren PP-InsP-Analog, etabliert und methyliertes InsP6 konnte in Mengen von 10 femtomol detektiert werden. Die Adaption der Methode für die PP-InsPs gestaltete sich jedoch herausfordernd, da der Analyt während der Reaktion zersetzt wurde. Ein Wechsel zu Diazomethan als Methylierungsagens zeigte vielversprechende Resultate.
Inositol pyrophosphates (PP-InsPs) are a well conserved group of second messengers that are involved in a plethora of cellular processes including phosphate homeostasis, insulin signaling, and apoptosis. Despite much effort, it is still mostly unknown how PP-InsPs exert their diverse functions. In order to decipher the mechanisms, researchers have relied either on metabolic labeling with radioactive inositol or on electrophoretic separation on polyacrylamide gels but these methods either lack ease of use or sensitivity. Therefore, two new analytical tools, based on nuclear magnetic resonance (NMR) spectroscopy, and liquid chromatography coupled mass spectrometry (LCMS), were developed. To overcome the limited sensitivity provided by NMR spectroscopy, a high yielding synthesis of NMR-active 13C-labeled inositol was designed and optimized. Furthermore, a chemoenzymatic synthesis of all mammalian PP-InsPs isomers was developed that relied on a scalable purification strategy utilizing precipitation with Mg2+ ions. Human cells were metabolically labeled with 13C-inositol and the prepared PP-InsPs were used as standards to identify peaks in the NMRspectra. These fingerprint signals enabled the quantification of the corresponding molecules. The LCMS-based method was based on the derivatization of the highly charged inositol pyrophosphates to their corresponding methyl esters by trimethylsilyldiazomethane. The permethylated InsPs and PP-InsPs were suitable for LC separation and MS measurement, and provide a sensitivity unmatched by NMR spectroscopy. The method was established using inositol hexakisphosphate, a simpler analog of PP-InsPs, and methylated InsP6 could be detected at quantities as low as 10 femtomole. However, the adaptation of the derivatization for PP-InsPs proved challenging as the reaction caused degradation of the analyte but strategies to circumvent the decay by changing the derivatization agent to diazomethane were promising.

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44

Samino, Gené Sara. "Mass spectrometry and nuclear magnetic resonance based metabolomics applied to the study of polycystic ovary syndrome." Doctoral thesis, Universitat Rovira i Virgili, 2013. http://hdl.handle.net/10803/128209.

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Objectives: Three objectives of this thesis have been: (i) Mastering of the main analytical platforms used in metabolomics, (ii) Developing an untargeted metabolomic workflow, involving novel aspects of sample preparation, and data processing for metabolite identification, (iii) Implementing our untargeted metabolomic workflow to the study of human patients with Polycystic Ovary Syndrome (PCOS) and their response to drug treatment Results: In Work 1: Optimization metabolite extraction conditions for NMR analysis, followed by LC/ESI-MS by using the same sample extract with no need for solvent exchange or further pretreatment. In Work 2: Investigate the impact of different aspects of univariate statistical analysis on untargeted LC-MS based metabolomic experiments. In Work 3: Implementation of GC-MS untargeted metabolomic approach to provide new insights on the impact that obesity exerts on the metabolic derangements associated with PCOS. In Work 4: Implementation of multiplatform metabolomics approach based on NMR and LC-MS to provide new insights in PCOS disease in a cohort of young lean PCOS patients. In Work 5: Implementation of multiplatform metabolomics approach based on NMR, GC-MS and LC-MS to provide new insights on the action of drug polytherapy to PCOS disorder. Conclusion: Metabolomics can be consider as a powerful tool for the study of metabolic disorders. Furthermore, metabolite profiling has demonstrated feasibility and flexibility for revealing new mechanistic insights in metabolic disorders that are not been consider when classical analysis is used. Therefore, our metabolomic analysis have demonstrated a great potential as a useful diagnostic technique and can facilitate monitoring of both disease progression and effects of therapeutic treatment.
Objetivos: El presente trabajo tiene dos objetivos generalizables que han sido estudiados con más detalle en la presente tesis doctoral. El primero de ellos es mejorar aspectos metodológicos en el ámbito de la metabolómica y el segundo ha sido la aplicación de la metabolómica en el estudio del síndrome del ovario poliquístico (PCOS). Resultados: Del primer objetivo se han realizado dos trabajos: en el primero, la optimización de un método de extracción común para analizar muestras biológicas en dos plataformas analíticas complementarias utilizadas en metabolómica como son la resonancia magnética nuclear y la espectrometría de masas. Del segundo trabajo realizado se han obtenido unas pautas para abordar los retos que surgen del análisis de datos de metabolómica en espectrometría de masas. Del segundo objetivo también han sido realizados dos trabajos: en ambos se ha utilizado la metabolómica no dirigida para abordar el estudio del PCOS. En el primer trabajo, se ha utilizado la metabolómica para conocer el impacto que ejerce la obesidad en los trastornos metabólicos asociados al PCOS. En el segundo trabajo, se ha utilizado la metabolómica no dirigida para evaluar como afecta la aplicación de una politerapia con medicamentos al metabolismo de pacientes con PCOS. Conclusión: La metabolómica puede ser utilizada como una nueva herramienta para estudiar los trastornos metabólicos.

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Wei, Juntong. "Mass spectrometry-based metabolomics to unveil the polybrominated diphenyl ether-47 induced alteration in breast carcinoma." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/661.

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Based on the findings from breast cancer cells and nude mouse assays, we noticed that fatty acid metabolism was influenced by BDE-47 exposure. To have a comprehensive understanding of the impact, we performed targeted metabolomics analysis of fatty acids. Short-chain fatty acids (SCFAs) and hydroxylated short-chain fatty acids (OH-SCFAs) are crucial intermediates related to a variety of diseases, such as bowel disease, cardiovascular disease, renal disease and cancer. We developed a global profiling method to screen SCFAs and OH-SCFAs by tagging these analytes with d0/d6-N, N-dimethyl-6,7-dihydro-5H-pyrrolo[3,4-d] pyrimidine-2-amine (d0/d6-DHPP) and UHPLC-MS/MS in parallel reaction monitoring (PRM) mode. The derivatization procedure was simple and rapid. The targeted compounds could be derivatized within three minutes under mild condition and analyzed without the need of further purification. The derivatization significantly improved the chromatographic performance and mass spectrometry response. The d6-DHPP tagged standards were used as internal standards, which remarkably reduced the matrix effects. The use of high resolution PRM mode made it possible to identify unknown SCFA and OH-SCFA species. The developed method was successfully applied to the analysis of mouse feces, serum, and liver tissue samples harvested from the breast cancer nude mice that had been exposed to BDE-47. By using the developed method, 40 analytes (10 SCFAs and 30 OH-SCFAs) were characterized. Semi-quantitative analysis indicated that the exposure of BDE-47 to the mice altered the SCFA and OH-SCFA metabolism, especially in the high dose group. In addition, medium- and long-chain fatty acids (MLFAs) are essential energy sources in cells and possess vital biological functions. Characteristics of MLFAs in biosamples can contribute to the understanding of biological process and the discovery of potential biomarkers for relevant diseases. However, there are obstacles of the MLFAs determination because of the poor ionization efficiency in mass spectrometry and structural similarity. Herein, a derivatization strategy was developed by labeling with d0-DHPP and detecting with UHPLC-MS/MS in multiple reaction monitoring (MRM) mode. The parallel isotope labeled internal standards were generated by tagging d6-DHPP to MLFAs. The simple and rapid derivatization procedure and mild reaction conditions greatly reduced the potential of MLFA degradation. With the methodology, the chromatography performance was greatly improved, and the mass spectrum response was enhanced up to 1, 600 folds. Finally, the developed derivatization method was applied to serum samples to analyze the alteration of MLFAs induced by BDE-47 exposure in breast cancer nude mice. The semi-quantitative results demonstrated that the BDE-47 exposure significantly influenced the MLFA metabolism. Together, mass spectrometry-based targeted and nontargeted metabolomics of in vitro and in vivo studies suggested that BDE-47 impacted multiple metabolic pathways and was positively associated with breast tumor growth in mice. This study might further our understanding of the health risks of BDE-47 to breast cancer.;Polybrominated diphenyl ethers (PBDEs) are commonly used to prevent the development of fire in various factory products. Due to the adverse effects on human health and bio-accumulation capacity, PBDEs are considered as one kind of persistent organic pollutants. 2,2',4,4'-Tetrabromodiphenyl ether (BDE-47) is one of the most frequently detected PBDE congeners in humans. Although numerous studies have shown the close connection between BDE-47 and human health, few reports were related to breast carcinoma. In vivo study of the association between BDE-47 and breast cancer was also scarce. In this study, both in vitro and in vivo experiments were conducted to explore the influence of BDE-47 to breast cancer. Firstly, we performed the in vitro study by exposing different concentrations of BDE-47 (5, 10 µM) to MCF-7 breast cancer cells. Nontargeted metabolomics analysis was conducted by using ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS). Results showed that the toxicity to MCF-7 cells gradually increased when the concentration of BDE-47 exceeded 1 µM in the medium. Pyrimidine metabolism, purine metabolism and pentose phosphate pathway (PPP) were the most influenced metabolic pathways, and the metabolites in the three metabolic pathways were significantly downregulated. Moreover, the increase of reactive oxygen species was detected by using the 2',7'-dichlorodihydrofluorescein diacetate staining assay. Results suggested that the BDE-47 induced oxidative stress by downregulating the NADPH generation in PPP. The pyrimidine metabolism and purine metabolism might be downregulated by the downregulation of mRNA transcripts. Therefore, BDE-47 could induce oxidative stress in breast cancer cells by inhibiting PPP and disordering the metabolism of the entire cell subsequently. Secondly, we constructed a breast cancer nude mouse model, performed in vivo exposure of BDE-47 to the mice, and conducted mass spectrometry-based metabolomics and lipidomics analysis to investigate the metabolic changes in mice. Results showed that the tumor sizes were positively associated with the dosage of BDE-47. Metabolomics and lipidomics profiling analysis indicated that BDE-47 induced significant alterations of metabolic pathways in livers, including glutathione metabolism, ascorbate and aldarate metabolism, and lipids metabolism, etc. The upregulations of phosphatidylcholines and phosphatidylethanolamines suggested the membrane remodeling, and the downregulations of Lyso-phosphatidylcholines and Lyso-phosphatidylethanolamines might be associated with the tumor growth. Targeted metabolomics analysis revealed that BDE-47 inhibited fatty acid β-oxidation (FAO) and induced incomplete FAO. The inhibition of FAO and downregulation of PPARγ would contribute to inflammation, which could promote tumor growth. In addition, BDE-47 elevated the expression of the cytokines TNFRSF12A, TNF-α, IL-1β and IL-6, and lowered the cytokines SOCS3 and the nuclear receptor PPARα. The changes of cytokines and receptor may contribute to the tumor growth of mice.

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Agana, Bernice A. "Mass Spectrometry-Based Proteomics and Metabolomics: Understanding Protein Interactions, Proteome Complexity and Perturbations in Cellular Metabolism." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574636665012436.

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Vogl, Franziska C. [Verfasser], and Peter [Akademischer Betreuer] Oefner. "Methodical aspects of urinary metabolomics by liquid chromatography-mass spectrometry / Franziska C. Vogl ; Betreuer: Peter Oefner." Regensburg : Universitätsbibliothek Regensburg, 2020. http://d-nb.info/1212240227/34.

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Crighton, Elly Gwyn. "Are supplements supplemented? Evaluating the composition of complementary and alternative medicines using mass spectrometry and metabolomics." Thesis, Crighton, Elly Gwyn (2020) Are supplements supplemented? Evaluating the composition of complementary and alternative medicines using mass spectrometry and metabolomics. PhD thesis, Murdoch University, 2020. https://researchrepository.murdoch.edu.au/id/eprint/57740/.

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The complementary and alternative medicines (CAM) industry is worth over US$110 billion globally. Products are available to consumers with little medical advice; with many assuming that such products are ‘natural’ and therefore safe. However, with adulterated, contaminated and fraudulent products reported on overseas markets, consumers may be placing their health at risk. Previous studies into product content have reported undeclared plant materials, ingredient substitution, adulteration and contamination. However, no large-scale, independent audit of CAM has been undertaken to demonstrate these problems in Australia. This study aimed to investigate the content and quality of CAM products on the Australian market. 135 products were analysed using a combination of next-generation DNA sequencing and liquid chromatography-mass spectrometry. Nearly 50% of products tested had contamination issues, in terms of DNA, chemical composition or both. 5% of the samples contained undeclared pharmaceuticals. Increasing reports of adulteration with novel drug analogues led to the development of a high-throughput untargeted method for pharmacovigilance. Rapid direct sample analysis coupled to mass spectrometry was used to screen products, this time for hundreds of compounds in minutes with minimal sample preparation. The data correlated well with previous analyses, with the added benefit of detected additional compounds including phytochemicals and vitamins. Finally, metabolomics was used to assess the compositional diversity of finished herbal products on the market and how they compare to standard reference materials. The analysis iii showed that, despite all products stating the same ingredients, there was a clear difference in biochemical profile between products and also the reference materials. The combined techniques and analyses used in this project provide an audit and quality control toolkit which will allow for stronger regulation of CAM products. The data collected has shown that such regulation is needed to improve product quality and to protect consumer safety.

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Wang, Yu. "The Application of Metabolomics to the Evaluation of the Celllular Toxicity." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1397057769.

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Showiheen, Salah Ali A. "Metabolomics profiling of amino acids metabolism in osteoarthritis." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/123249/1/Salah%20Ali%20A_Showiheen_Thesis.pdf.

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The researcher studied the role of amino acid metabolism in osteoarthritis progression. The study suggests that this abnormal amino acid metabolism aids in the development of the disease. This data further suggests that amino acids could be potential circulatory markers for diagnosing OA and therapeutic strategies of amino acids supplementation could be considered as a potential treatment.

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