Dissertations / Theses: 'Metabolomics and trascriptomics analysis'

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GUARNERIO, Chiara Francesca. "Effects of enod40 overexpression in non legume plants." Doctoral thesis, Università degli Studi di Verona, 2010. http://hdl.handle.net/11562/343215.

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Il gene ENOD40 è una nodulina precoce ed è indotto durante i primi stadi di formazione del nodulo radicale in risposta alle interazioni tra le leguminose ed i batteri simbionti del genere Rhizobia. Omologhi del gene ENOD40 sono stati identificati in diverse specie e la sua espressione, non unicamente correlata alla formazione del nodulo, è stata osservata in tessuti giovani e meristematici. Una caratteristica che accomuna i geni ENOD40 è l’assenza di un lunga open reading frame (ORF); al contrario, molte piccole ORF sono generalmente presenti nei trascritti. Il gene contiene due regioni altamente conservate chiamate box1 e box2. Tra le diverse specie è conservata l’ORF (ORF1) del box1, che sembra codificare per un putativo peptide di 10-13 amminoacidi. Inoltre, il gene contiene regioni corrispondenti a strutture conservate a livello del trascritto. Sei domini sono stati individuati nel mRNA del gene e due di questi domini sono fortemente conservati tra le leguminose e le non leguminose. Nonostante decenni di ricerche, il ruolo del gene ENOD40 non è stato finora completamente chiarito. La natura biologica del gene è tuttora in discussione, infatti se l’attività biologica del gene dipenda dall’ RNA o da entrambi è ancora da chiarire. I due principali obiettivi del mio progetto di ricerca sono: da una parte, indagare la possibile presenza del putativo peptide codificato dal box1 utilizzando cellule BY-2 che overesprimono il gene e dall’altra, studiare il ruolo del gene ENOD40 in piante non leguminose, utilizzando Arabidopsis thaliana. Nella prima parte del lavoro è stata messa appunto una procedura di purificazione per cercare il putativo peptide in cellule BY-2 che overesprimevano il gene ENOD40 di tabacco. Fin ad ora il putativo peptide non è mai stato trovato in vivo; è stato però suggerito da diverse osservazioni che il gene potrebbe, almeno in parte, agire attraverso il peptide codificato dall’ORF1. La procedura messa appunto consiste in un cut-off iniziale, seguita da cromatografia a scambio ionico, estrazione di cambio solido, HPLC-DAD e spettrometria di massa (LC-ESI-MS e MALDI-TOF). Purtroppo, nonostante i diversi tentativi per mettere appunto la procedura di purificazione e le diverse tecniche utilizzate per l'analisi delle frazioni putativamente peptide-arricchite, solo l’analisi MALDI-TOF PSD ha dato un primo indizio sulla possibile presenza del peptide in cellule BY-2 che overesprimevano il gene ENOD40. Nella seconda parte del lavoro, il possibile ruolo del gene è stato indagato mediante l’analisi metabolomica e trascrizionale in piante di Arabidopsis che overesprimevano il gene ENOD40 di soia. I profili metabolici e trascrizionali di tre linee di Arabidopsis trasformate con il gene ENOD40 sono stati acquisiti e confrontati con quelli ottenuti da piante wild type. In seguito, l'analisi dei biomarcatori dei dati ottenuti dalle analisi di metabolomica e trascrittomica è stata utilizzata per identificare i metaboliti e i trascritti che hanno mostrato un maggiore correlazione con l'overespressione del gene. Dai profili metabolici è emerso che le tre linee trasformate sono caratterizzate dalla presenza di glucosinolati, mentre i flavonoidi caratterizzano principalmente le piante wild type. Per quanto riguarda i profili trascrizionali, la maggior parte dei geni indotti nelle tre linee trasformate (12 su 23), sono correlati con processi che avvengono nella parete cellulare. Dato che, la parete cellulare determina la forma delle cellule, il gene ENOD40 potrebbe essere coinvolto in un processo che controlla la composizione e le dinamiche della parete. Precedenti studi morfologici condotti sulle stesse linee trasformate di Arabidopsis hanno dimostrato che queste piante presentano organi con dimensioni normali ma formati da celle più piccole; inoltre protoplasti di Arabidopsis trasfettati con il gene ENOD40 sono caratterizzati da una ridotta espansione. Questi dati hanno suggerito che il gene potrebbe avere un ruolo nel mantenere le cellule in uno stadio giovane e poco differenziato. L'osservazione che le linee trasformate di Arabidopsis accumulino glucosinolati, metabolici tipici di tessuti giovani, suggerisce che, anche dal punto di vista metabolico, le cellule trasformate hanno caratteristiche tipiche di cellule più giovani, mentre le cellule wild type accumulano maggiormente i flavonoidi, metaboliti secondari tipici dello stato differenziato. Per quanto riguarda l'analisi trascrizionale, dal momento che le piante trasformate sono morfologicamente caratterizzate da cellule con dimensioni ridotte, i geni indotti in queste linee, potrebbero essere coinvolti nella prevenzione dell’espansione cellulare. Questo ruolo del gene, atto a mantenere le cellule in uno stadio giovanile, è supportato anche dai profili di espressione del gene riportati in letteratura. ENOD40 is an Early Nodulin gene that it is know to play a key role in nodule formation in response to interaction of legume plants with symbiotic Rhizobium bacteria. Homologues of ENOD40 genes have been identified in several plant species and its expression is observed during the initiation and development of new organs, such as nodules, lateral roots, young leaves and stipule primordia. ENOD40 gene has an unusual structure: it lacks a long open reading frame, but several short ORFs are present. Moreover, at nucleotide level, two regions, named box1 and box2, are highly conserved among all ENOD40 genes. In box 1 region, a highly conserved ORF (ORF 1) is present and it seems to encode a putative peptide of 10-13 amino acids. Furthermore, the gene contains regions corresponding to conserved secondary structures of the transcript. Six domains were identified in ENOD40 mRNA and two of these domains are strongly conserved among legume and non legume species. Despite several researches, the roles of the ENOD40 gene has not been so far completely elucidated. Moreover, whether the biological activity should be ascribed to RNA or peptide, or both, is still unclear. For this reason, the two main goals of the research are: to investigate the possible presence of the putative peptide encoded by box1 of the ENOD40 gene in BY-2 cells and to investigate the role of ENOD40 gene in non legume plants, using Arabidopsis thaliana. That ENOD40 could act, at least in part, through the peptide encode by box1 is suggested by several observations, but no one have revealed biochemically the putative peptide. In the first part of the work a purification procedure consisting of membrane cut-off, ion exchange chromatography, solid exchange extraction, HPLC-DAD and mass spectrometry (LC-ESI-MS and MALDI-TOF) was set up to search for the putative peptide in BY-2 cells overexpressing NtENOD40 gene. Unfortunately, despite several attempts to set up the purification procedure and the different and sensitive techniques used for the analysis of the putatively peptide-enriched fractions, only MALDI-TOF PSD analysis gave an initial clue of the possible presence of the peptide in ENOD40 overexpressing BY2 cells. In the second part of the work, the possible role of the gene has been investigated through the metabolomics and transcriptomics characterization of ENOD40 overexpressing Arabidopsis plants. Metabolite and transcriptional profiles of the three Arabidopsis lines overexpressing soybean ENOD40 gene were acquired and compared to those obtained from wild type plants. Afterward, biomarker analysis of metabolomic and transcriptomic dataset was used in order to identify the metabolites and transcripts that showed the higher correlation with the overexpression of ENOD40 gene. In the metabolite profiles, glucosinolate metabolites characterized all the three transformed lines compared with the wild type, while flavonoids mainly characterized wild type plants. With regard to transcriptional profiling, most of the genes upregulated in the three transformed lines (twelve out of twenty-three), were correlated with processes occurring in the cell wall. Thus, the cell wall is the mechanical determinant of cell shape and size ENOD40 gene could be involved in a process that controls the composition and the dynamics of the cell wall. In conclusion, previous morphological studies on the same Arabidopsis thaliana ENOD40 transformed lines used in this work have been showed that these plants are characterised by normal organs containing smaller cells, and on ENOD40 transfected Arabidopsis protoplasts are characterized by reduced expansion, suggested that the gene could have some role in keeping the cells in a “young” state . The observation that ENOD40 transformed Arabidopsis lines accumulate high levels of glucosinolates, that are typical of the young tissues, suggests that, also from the metabolic point of view, the transformed cells have features typical of younger cells, whereas wild type cells use their metabolic resources to accumulate flavonoids, another class of secondary metabolites more typical of differentiated state. With regard to transcriptomic analysis, since transformed plants are morphologically characterized by small cell size, the genes upregulated in the transformed lines, involved in cell wall dynamics and composition, could be involved in the prevention of cell expansion. The role of ENOD40 in maintenance of cells in a “young state” is also supported by the expression patterns of ENOD40 genes reported in literature.

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Yet, Idil. "Integrated epigenomics and metabolomics analysis in twins." Thesis, King's College London (University of London), 2016. https://kclpure.kcl.ac.uk/portal/en/theses/integrated-epigenomics-and-metabolomics-analysis-in-twins(4d0fb76b-cc2b-4e31-8950-a7ffb5b91363).html.

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Epigenetics and metabolomics are rapidly growing areas of research, in part due to recent advances in technology that have allowed for a wide coverage of the human genome. Metabolites are small compounds present in cell and body fluids, and are involved in biochemical processes of the cell. Quantitative trait loci associated with levels of individual metabolites (mQTLs) have been identified from numerous metabolome GWAS. Here, I analysed metabolite levels in twins with the aim of identifying genetic variants that influence metabolomic traits (mQTLs) using two different metabolomics platforms, and consequently compared the results to report stable metabolites on both technologies to ultimately enable combining metabolite profiles across these two platforms. DNA methylation is a biochemical process that is vital for mammalian development. It is present throughout the genome and is the most extensively studied epigenetic mark. Previous studies have explored the heritability of DNA methylation and have identified methylation QTLs (meQTL). Here, I identified meQTLs with the goal of assesing the evidence of genetic effects influence not only DNA methylation levels, but also variability by using MZ-twin discordance as a measure of variance. Epigenetic mechanisms and metabolomic profiles have both been shown to play a role in gene expression and susceptibility for complex human disease. Here, I analysed the association between type 2 diabetes and metabolomic and epigenetic datasets and combined the data to identify connections between these levels of biological data at genetic variants linked to type 2 diabetes to gain more insight into the disease susceptibility and progression. Overall, the results confirmed previous findings of strong genetic influences on metabolites and extend current knowledge about genetic effects underlying several biochemical pathways. Additionally, the results also showed genetic influences on DNA methylation, and give insights into mechanisms by which genetic impacts epigenetic processes. Lastly, the findings show that specific genetic susceptibility variants for type 2 diabetes can also impact epigenetic and metabolomics profiles, and can help improve our understanding of the disease etiology.

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Muhamad, Ali Howbeer. "Metabolomics investigation of microbial cell factories." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/metabolomics-investigation-of-microbial-cell-factories(2e2f5f58-d38a-4c77-966b-56ce92aec619).html.

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The stream of new technological advancements and their integration into the field of microbiology have contributed significantly towards our understanding of life in the micro-scale world, making the fields of microbiology and biotechnology shine like never before. Since 1980, the recombinant protein-based therapeutics industry has become one of the fastest growing sectors in the biopharmaceutical market. Nearly 30% of commercially available recombinant proteins are produced in Escherichia coli, making this species one of the most commonly used bacterial expression systems for the production of recombinant biotherapeutics. However, when it comes to the production of enzymes and bioactive secondary metabolites (antibiotic, antifungal, antiviral and immunosuppressant), Streptomyces species remain the major producer within this sector. Meeting the high demand for such products requires a clear and in-depth understanding of the bioprocesses involved to achieve high yield and quality products, whilst keeping the process industrially attractive. It is generally accepted that the metabolome, as a down-stream process to the genome and proteome, may provide a clearer picture of a biological system. Thus, in this thesis a series of metabolomics approaches were adopted to obtain a deeper insight into the metabolic effects of recombinant protein production in E. coli and Streptomyces lividans. Furthermore, a Geobacter-based biomagnetite nanoparticle production system which displayed a prolonged lag phase upon scale-up was investigated by employing metabolic profiling and fingerprinting approaches combined with multivariate analysis strategies, to identify growth-limiting metabolites. The results of this analysis identified nicotinamide as the growth limiting metabolite. Nicotinamide-feeding experiments confirmed the above findings, leading to improved biomass yield whilst restoring the lag phase to bench-scale level. Raman and Fourier transform infrared spectroscopies combined with stable isotopic probing strategies were also employed to demonstrate the application of metabolic fingerprinting in providing detailed biochemical information for quantitative characterisation and differentiation of E. coli cells at community and single-cell levels. The single-cell approach proved promising, offering detailed biochemical information and perhaps accompanying other cultivation-free approaches such as metagenomics for further future investigations. It is hoped that the advances made in these studies have proved the potential applications of metabolomics strategies to aid the optimisation of microbially-driven bioprocesses.

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Klünder, Christina. "Metabolomics for toxicity analysis using the chlorophyte Scenedesmus vacuolatus /." Leipzig [u.a.], 2009. http://www.ufz.de/data/ufzdiss_2_2009_9947.pdf.

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Gloaguen, Yoann. "Supporting analysis, visualisation and biological interpretation of metabolomics datasets." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8433/.

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Over the past decades, the emerging omics technologies have enabled scientists to take a step further in the investigation of biological systems. From food safety to stratified medicine, omics technologies are now an essential and powerful means to study biological processes. Omics technologies are however at different stages of maturity, and the most recent field of the omics family, metabolomics, is still in its infancy. Metabolomics attempts to catalogue, characterise and quantify all small molecules constitutive of a biological system. Liquid Chromatography - Mass Spectrometry (LCMS) is now the most commonly used technique to generate metabolomics data. The method allows the detection of hundreds of metabolites from a single sample and can provide a rapid assignment of formulae to detected masses using high accuracy mass spectrometers. While analytical methods are well developed, support for linking metabolites to detected features and interpreting the results of a data analysis in a biological context is still poorly developed. Significant challenges also arise from the additional steps required to export the data to third party environments to create a biological context. The study of integrated omics datasets as a single system has also shown to provide greater inferences than the study of each omics separately. Methods to integrate the different omics layers of biological systems are, however, at an early stage of development and no standard approach currently exists to provide a holistic view of organisms systems organisation. The objective of this thesis is to formalise, standardise and unify the data analysis of the metabolomics field, by providing to biologists the tools to support them from planning to analysis to biological impact reporting. The work presented here focuses particularly on untargeted LC-MS metabolomics approaches and attempts to assist non-expert users in performing their own analysis of metabolomics datasets. The project also aims to enable systematic biological interpretation of metabolomics datasets. The first part of the thesis focuses on creating the foundation of a unified environment for LC-MS metabolomics data analysis. Subsequently, the created environment will be expanded to integrate and support the latest technological advances in the field and provide better support for both designing studies and interpreting analysis results in a biological context. Finally, the last part of this thesis concentrates on integrating metabolomics data with other omics datasets in an attempt to provide a holistic view of a biological system.

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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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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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Duffy, Kate I. "Application of metabolomics to the analysis of ancient organic residues." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5670/.

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The grape is arguably one of the oldest cultivated products in human history and the analysis of its main product, wine, reveals clues to trade and associations of previous civilizations. In ancient times, wine was stored in clay amphorae, which, if not properly sealed with resin or pitch allowed the wine to wick into clay matrices, dry, and polymerize producing insoluble, intractable materials that may remain within the matrix for several thousand years. Presently, identification of wine residue is based upon the extraction of these polymeric materials from the ceramic matrix and analyzing/identifying the chemical fingerprints. Two main biomarkers have historically been employed for the identification of wine residue: tartaric and syringic acids. In some cases, the presence of one of these biomarkers has been designated as the confirmatory signature of wine often leading to false positives as amphorae were re-used in antiquity. Herein, a novel approach utilizing metabolomics has been applied to archaeological objects in order to further mine possible biomarkers for a more accurate assessment of the original foodstuff. An untargeted metabolic profiling method was combined with a targeted analytical method resulting in the successful validation of eight representative biomarkers in two separate archaeological sites.

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Daub, Carsten O. "Analysis of integrated transcriptomics and metabolomics data a systems biology approach /." [S.l. : s.n.], 2004. http://pub.ub.uni-potsdam.de/2004/0025/daub.pdf.

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Daub, Carsten Oliver. "Analysis of integrated transcriptomics and metabolomics data : a systems biology approach." Phd thesis, Universität Potsdam, 2004. http://opus.kobv.de/ubp/volltexte/2005/138/.

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Moderne Hochdurchsatzmethoden erlauben die Messung einer Vielzahl von komplementären Daten und implizieren die Existenz von regulativen Netzwerken auf einem systembiologischen Niveau. Ein üblicher Ansatz zur Rekonstruktion solcher Netzwerke stellt die Clusteranalyse dar, die auf einem Ähnlichkeitsmaß beruht. Wir verwenden das informationstheoretische Konzept der wechselseitigen Information, das ursprünglich für diskrete Daten definiert ist, als Ähnlichkeitsmaß und schlagen eine Erweiterung eines für gewöhnlich für die Anwendung auf kontinuierliche biologische Daten verwendeten Algorithmus vor. Wir vergleichen unseren Ansatz mit bereits existierenden Algorithmen. Wir entwickeln ein geschwindigkeitsoptimiertes Computerprogramm für die Anwendung der wechselseitigen Information auf große Datensätze. Weiterhin konstruieren und implementieren wir einen web-basierten Dienst fuer die Analyse von integrierten Daten, die durch unterschiedliche Messmethoden gemessen wurden. Die Anwendung auf biologische Daten zeigt biologisch relevante Gruppierungen, und rekonstruierte Signalnetzwerke zeigen Übereinstimmungen mit physiologischen Erkenntnissen. Recent high-throughput technologies enable the acquisition of a variety of complementary data and imply regulatory networks on the systems biology level. A common approach to the reconstruction of such networks is the cluster analysis which is based on a similarity measure. We use the information theoretic concept of the mutual information, that has been originally defined for discrete data, as a measure of similarity and propose an extension to a commonly applied algorithm for its calculation from continuous biological data. We compare our approach to previously existing algorithms. We develop a performance optimised software package for the application of the mutual information to large-scale datasets. Furthermore, we design and implement a web-based service for the analysis of integrated data measured with different technologies. Application to biological data reveals biologically relevant groupings and reconstructed signalling networks show agreements with physiological findings.

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Grinde, Maria Tunset. "Characterization of breast cancer using MR metabolomics and gene expression analysis." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for sirkulasjon og bildediagnostikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-19447.

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Brystkreft er den vanligste kreftformen blant kvinner i Norge. Prognose og overlevelse avhenger av type kreft, tumorstørrelse, lymfeknutemetastaser og reseptorstatus (østrogen, progesteron og HER2). Basert på genekspresjonanalyser av tumorvev kan brystkreft deles inn i fem grupper; luminal A, luminal B, basal-like, HER2 positive og normal-like. Prognosene til pasientene i de ulike gruppene varierer der pasienter med basal-like brystkreft har den dårligste prognosen, mens pasienter med luminal A brystkreft har de beste prognosene. Metabolismen i kreftceller og normale celler er svært forskjellig. Et kjennetegn ved kreftceller er endret glykolytisk aktivitet. Kreftcellene kan forbruke glukose og omdanne dette til laktat til tross for at det er rikelig med oksygen til stede. Denne effekten er ofte omtalt som Warburgeffekten. En annen karakteristisk egenskap ved kreftceller er endringer i kolinmetabolismen. High resolution magic angle spinning MR spektroskopi (HR-MAS MRS) er en metode som er egnet for å studere biokjemiske forbindelser, kalt metabolitter, i vev. Glykolyseog kolinmetabolitter i kreftvev kan derfor studeres med denne teknikken. Proton (1H) MRS gir et spekter med informasjon om hvilke metabolitter som finnes i vev. 13C MRS er velegnet til å studere metabolsk omsetning i celler, dyr eller mennesker. Ved administrering av 13C-merkede metabolitter, kan man derfor kartlegge metabolske reaksjonsveier. Siden MRS er en kvantitativ metode kan den brukes til å beregne metabolittkonsentrasjoner i vev. Ved bruk av multivariate dataanalyser kan flere metabolitter i HR-MAS MR-spektrene analyseres samtidig. Denne metoden er derfor egnet til å studere metabolske forskjeller mellom ulike brystkreftgrupper. Siden vevet er intakt etter HR-MAS MRS kan det brukes til andre analyser etterpå, som for eksempel histopatologi eller genekspresjonsanalyser. Genekspresjonsanalyse er en egnet metode for å kartlegge hele eller deler av genomet. Med denne metoden kan man undersøke de genetiske forandringene som oppstår i kreftceller. Denne doktorgraden består av tre studier. Målet med det første studiet var å kartlegge prognostiske faktorer i brystkreftvev ved bruk HR-MAS MRS og multivariate dataanalyser. Tre ulike typer multivariate metoder ble benyttet for å undersøke om HRMAS MR spektrene inneholder informasjon som kan brukes til å prediktere østrogenreseptorstatus, progesteronreseptorstatus og lymfeknutestatus. Resultatene viste at det finnes metabolske forskjeller mellom tumorer som har positiv og negativ hormonreseptorstatus. I det andre studiet ble 13C HR-MAS MRS og genekspresjonsanalyser brukt til å kartlegge den glykolytiske aktiviteten i to ulike brystkreft musemodeller som representerer luminal-like og basal-like brystkreft. 13C-merket glukose ble injisert i de to modellene og tumorvev samlet 10 eller 15 minutter etter injeksjon. HR-MAS MRSanalysene av tumorvevet viste at glukose/laktat (Glc/Lac) og glukose/alanin (Glc/Ala)- ratioene var større i de raskt voksende basal-like svulstene sammenlignet med den luminal-like modellen. De fleste glykolytiske genene var dessuten oppregulert i den luminal-like modellen. Disse resultatene indikerer at den luminal-like modellen har større glykolytisk aktivitet enn den basal-like modellen, og at tumorvekst ikke nødvendigvis er en avgjørende faktor for glykolytisk aktivitet. Hensikten med det tredje studiet var å beskrive den metabolske profilen til et større utvalg av brystkreft musemodeller som representerer både luminal A, luminal B, basallike og HER2 positiv brystkreft. Resultatene viste at luminal B-svulstene hadde en større fosfokolin/glyserofosfokoline (PCho/GPC)-ratio enn de fleste basal-like svulstene. I tillegg var kolin, PCho og GPC korrelert til andre gener i kolinmetabolismen i luminal B- svulstene enn i de basal-like svulstene. Dette kan bety at reguleringen av kolinmetabolismen er ulik i de to undergruppene av brystkreft. Det var i tillegg god overensstemmelse mellom både metabolitt- og genekspresjonsprofiler mellom xenograftprøvene og brystkreftprøver fra pasienter i de to undergruppene. Resultatene fra studiet viser at dette panelet av xenograftmodeller er representativt for brystkreft hos mennesker, og betyr at modellene kan brukes til å identifisere nye behandlingsregimer ved bruk av HR-MAS MRS og genekspresjonsanalyser. Studiene beskrevet i denne avhandlingen har vist at HR-MAS MRS og genekspresjonsanalyser reflekterer ulike karakteristikker i brystkreft og at disse metodene derfor kan brukes til å utvikle prognostiske verktøy for brystkreftpasienter.

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Ahmed, Mohamed Fathi Youssef Mohamed. "Development of computational analysis tools for natural products research and metabolomics." 京都大学 (Kyoto University), 2016. http://hdl.handle.net/2433/215499.

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BACCOLO, GIACOMO. "Chemometrics approaches for the automatic analysis of metabolomics GC-MS data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/374731.

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La metabolomica, che consiste nella identificazione di tutti i metaboliti presenti all’interno dei campioni biologici analizzati, è un approccio ampiamente applicato in diversi campi di ricerca quali: identificazione di biomarcatori, sviluppo di nuovi farmaci, scienze alimentari e ambientali. La metabolomica è strettamente legata alla capacità di tecniche analitiche fra queste una delle più applicate è la gas cromatografia accoppiata alla spettrometria di massa. Moderne piattaforme analitiche possono generare centinaia di migliaia di spettri, rilevando una quantità impressionante di molecole distinte. Nonostante i progressi tecnici raggiunti sul lato sperimentale, la conversione dei segnali misurati dagli strumenti in informazioni utili non è un passaggio scontato in studi metabolomici. Per ogni composto identificato, l’obbiettivo è ottenere la concentrazione relativa tra tutti i campioni analizzati e lo spettro di massa associato al composto, necessario per l’identificazione della molecola stessa. I software disponibili per l’analisi dei dati sperimentali sono stati ripetutamente indicati come una fonte importante di incertezza, limitando fortemente sia la quantità che la qualità delle informazioni estratte. Gli strumenti più applicati richiedono l’impostazione di diversi parametri da parte dell’operatore, influenzando il risultato dell’analisi. In questa tesi è descritto un nuovo approccio, chiamato AutoDise, per l’analisi dei dati GC-MS. L’elaborazione dei segnali sperimentali si basa su PARAFAC2. PARAFAC2 è un modello che scompone dati multidimensionali, discriminando tra i diversi segnali nei campioni. Grazie alle sue proprietà, PARAFAC2 non ha bisogno che i dati siano pretrattati e non richiede di impostare parametri, mentre software utilizzati in questo ambito richiedono di definire diversi parametri e un laborioso pretrattamento dei dati, richiedendo l’intervento di un utente esperto, inoltre la riproducibilità dei risultati è limitata, dipendendo i parametri scelti dall’utente. Tuttavia, il fitting di modelli PARAFAC2 coinvolge diverse fasi ed è necessario un esperto analista per l’analisi e l’interpretazione dei modelli. AutoDise è un sistema esperto in grado di gestire tutti i passaggi riguardanti la modellazione e di generare una tabella dei picchi in cui ogni composto è identificato in modo univoco, con risultati completamente riproducibili. Questo è possibile grazie alla combinazione di diversi strumenti diagnostici e grazie all’ applicazione di modelli d’intelligenza artificiale. Le prestazioni dell’approccio sono state testate su un complesso dataset di oli d’oliva ottenuto tramite analisi GC-MS. I dati sono stati analizzati sia manualmente, da utenti esperti, sia automaticamente con il metodo AutoDise proposto e le tabelle dei picchi risultanti sono state confrontate. I risultati mostrano che AutoDise supera l’analisi manuale sia in termini di numero di composti identificati che di qualità dell’identificazione e della quantificazione. Inoltre, è stata sviluppata una GUI per rendere l’algoritmo più accessibile a persone non esperte nel linguaggio di programmazione. La tesi include un tutorial che mostra le caratteristiche principali e come utilizzare l’interfaccia grafica. Un’altra parte importante della tesi è stata dedicata al test e allo sviluppo di nuove reti neurali artificiali da implementare nel software AutoDise per rilevare quali componenti PARAFAC2 stanno fornendo informazioni chimicamente utili. A tal fine, più di 170.000 profili sono stati etichettati manualmente, al fine di addestrare, validare e testare una rete neurale convoluzionale e una rete bilineare con memoria a breve termine e un modello k-nearest neighbour. I risultati suggeriscono che le reti di deep learning possono essere efficacemente applicate per la classificazione automatica dei profili cromatografici. Metabolomics, which consists of identifying all the metabolites present in the biological samples analysed, is an approach widely applied in various research fields such as biomarker identification, new drug development, food and environmental sciences. Metabolomics is closely linked to the ability of analytical techniques, one of the most widely applied being gas chromatography coupled to mass spectrometry. Modern analytical platforms can generate hundreds of thousands of spectra, detecting an impressive number of distinct molecules. Despite the technical progress achieved on the experimental side, the conversion of signals measured by instruments into useful information is not an obvious step in metabolomic studies. For each identified compound, the goal is to obtain the relative concentration among all analysed samples and the mass spectrum associated with the compound needed to identify the molecule itself. The software available for analysing experimental data has repeatedly been cited as a major source of uncertainty, severely limiting both the quantity and quality of the information extracted. The most applied tools are based on univariate data analysis, considering each sample separately from the others and requiring the operator to set several parameters, affecting the result of the analysis. In this thesis, a new approach, called AutoDise, for the analysis of GC-MS data is described. The processing of the experimental signals is based on PARAFAC2. PARAFAC2 is a model that decomposes multidimensional data, discriminating between different signals in the samples. Due to its properties, PARAFAC2 does not need the data to be pre-processed and does not require parameters to be set, whereas software used in this field requires several parameters to be defined and laborious pre-processing of the data, requiring the intervention of an expert user, and the reproducibility of the results is limited, depending on the parameters chosen by the user. However, fitting PARAFAC2 models involves several steps and an experienced analyst is needed to analyse and interpret the models. AutoDise is an expert system capable of handling all modelling steps and generating a peak table in which each compound is uniquely identified, with fully reproducible results. This is possible thanks to the combination of different diagnostic tools and the application of artificial intelligence models. The performance of the approach was tested on a complex dataset of olive oils obtained by GC-MS analysis. The data were analysed both manually, by experienced users, and automatically with the proposed AutoDise method and the resulting peak tables were compared. The results show that AutoDise outperforms manual analysis both in terms of the number of compounds identified and the quality of identification and quantification. In addition, a GUI was developed to make the algorithm more accessible to people not skilled in the programming language. The thesis includes a tutorial showing the main features and how to use the GUI. Another important part of the thesis was devoted to testing and developing new artificial neural networks to be implemented in the AutoDise software to detect which PARAFAC2 components are providing chemically useful information. To this end, more than 170,000 profiles were manually labelled in order to train, validate and test a convolutional neural network and a bilinear network with short-term memory and a k-nearest neighbour model. The results suggest that deep learning networks can be effectively applied for the automatic classification of chromatographic profiles.

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Johnsson, Anna. "Mining for Lung Cancer Biomarkers in Plasma Metabolomics Data." Thesis, Linköping University, Biotechnology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57670.

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Lung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.NyckelordLung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.Nyckelord

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Öman, Tommy. "Multivariate Analysis of 2D-NMR Spectroscopy : Applications in wood science and metabolomics." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-80201.

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Wood is our most important renewable resource. We need better quality and quantity both according to the wood itself and the processes that are using wood as a raw material. Hence, the understanding of the chemical composition of the wood is of high importance. Improved and new methods for analyzing wood are important to achieve better knowledge about both refining processes and raw material. The combination of NMR and multivariate analyses (MVA) is a powerful method for these analyses but so far it has been limited mainly to 1D NMR. In this project, we have developed methods for combining 2D NMR and MVA in both wood analysis and metabolomics. This combination was used to compare samples from normal wood and tension wood, and also trees with a down regulation of a pectin responsible gene. Dissolving pulp was also examined using the same combination of 2D-NMR and MVA, together with FT-IR and solid state 13C CP-MAS NMR. Here we focused on the difference between wood type (softwood and hardwood), process type (sulfite and sulfate) and viscosity. These methods confirmed and added knowledge about the dissolving pulp. Also reactivity was compared in relation to morphology of the cellulose and pulp composition. Based on the method and software used in the wood analysis projects, a new method called HSQC-STOCSY was developed. This method is especially suited for assignment of substances in complex mixtures. Peaks in 2D NMR spectra that correlate between different samples are plotted in correlation plots resembling regular NMR spectra. These correlation plots have great potential in identifying individual components in complex mixtures as shown here in a metabolic data set. This method could potentially also be used in other areas such as drug/target analyses, protein dynamics and assignment of wood spectra.

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Spicer, Rachel. "Fit for purpose? : a metascientific analysis of metabolomics data in public repositories." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/287634.

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Metabolomics is the study of metabolites and metabolic processes. Due to the diversity of structures and polarities of metabolites, no single analytical technique is able to measure the entire metabolome - instead a varied set of experimental designs and instrumental technologies are used to measure specific portions. This has led to the development of many distinct data analysis and processing methods and software. There is hope that metabolomics can be utilized for clinical applications, in toxicology and to measure the exposome. However, for these applications to be realised data must be high quality, sufficiently standardised and annotated, and FAIR (Findable, Accessible, Interoperable and Reproducible). For this purpose, it is also important that standardised, FAIR software workflows are available. There has also recently been much concern over the reproducibility of scientific research, which FAIR and open data, and workflows can help to address. To this end, this thesis aims to assess current practices and standards of sharing data within the field of metabolomics, using metascientific approaches. The types of functions of software for processing and analysing metabolomics data is also assessed. Reporting standards are designed to ensure that the minimum information required to un- derstand and interpret the results of analysis are reported. However, poor reporting standards are ignored and not complied with. Compliance to the biological context Metabolomics Standards Initiative (MSI) guidelines was examined, in order to investigate their timeliness. The state of open data within the metabolomics community was examined by investigating how much publicly available metabolomics data there is and where has it been deposited. To explore whether journal data sharing policies are driving open metabolomics data, which journals publish articles that have their underlying data made open was also examined. However, open data alone is not inherently useful: if data is incomplete, lacking in quality or missing crucial metadata, it is not valuable. Conversely, if data are reused, this can demonstrate the worth of public data archiving. Levels of reuse of public metabolomics data were therefore examined. With greater than 250 software tools specific for metabolomics, practitioners are faced with a daunting task to select the best tools for data collection and analysis. To help educate researchers about what software is available, a taxonomy of metabolomics software tools and a GitHub pages wiki, which provides extensive details about all included software, have been developed.

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Alonso, Arnald. "Bioinformatics methods for the genomics and metabolomics analysis of immune-mediated inflammatory diseases." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/320191.

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During the last decade, genomics have been widely used to the characterization of the molecular basis of common diseases. Genome-wide association studies (GWAS) have been highly successful in characterizing the genetic variation that influences human traits including the susceptibility to common diseases. In metabolomics, recent improvements of analytical technologies have enabled the analysis of complete metabolomic profiles. Using this approach, high-throughput metabolomics studies have already demonstrated a high potential for the discovery of disease biomarkers. The use of powerful high-throughput measurement technologies has resulted in the generation of large datasets of biological variation. In order to extract relevant biological information from this data, highly specialized bioinformatics methods are required. This thesis is focused on the development of new methodological tools to improve the processing of genomics and metabolomics high-throughput data. These new tools have been used in the analysis framework of the Immune-Mediated Inflammatory Diseases (IMIDs) Consortium. The IMID Consortium is a large Spanish network of biomedical researchers on autoimmune diseases, which holds one of the largest collections of biological samples from this group of diseases, as well as healthy controls. The first analysis tool that has been developed is a computationally efficient algorithm for simultaneous genotyping of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) using microarray data. This bioinformatics tool, called GStream, integrates the genotyping of both types of genomic variants into a single processing pipeline. We demonstrate that the developed algorithms provide a significant increase in genotyping accuracy and call rate when compared to previous algorithms. Using GStream, the researchers performing large-scale GWASs will not only benefit from the combined and fast genotyping of SNPs and CNVs but, more importantly, they will also improve the accuracy and therefore the statistical power of their studies. The second tool that was developed during this thesis was FOCUS, a bioinformatics framework that provides a complete data analysis workflow for high-throughput metabolomics studies based on one-dimensional nuclear magnetic resonance (NMR). FOCUS workflow includes quality control, peak alignment, peak picking and metabolite identification. The algorithms included in FOCUS were designed to overcome several technical challenges that can dramatically affect the quality of the results. FOCUS allows users to easily obtain high-quality NMR feature matrices, which are ready for chemometric analysis, as well as metabolite identification scores for each peak that greatly simplify the biological interpretation of the results. When tested against previous NMR data processing methodologies, FOCUS clearly showed a superior performance, even in datasets with high levels of spectral unalignment. he final research work included in this thesis is a GWAS in Crohn's disease (CD) clinical phenotypes. CD is the most prevalent chronic inflammatory disease of the bowel, and is characterized by segmental and transmural inflammation of the gastrointestinaltract. CD is a highly heterogeneous disease, with patients showing different degrees of severity. The identification of the genetic basis associated with disease severity is therefore a major objective in CD translational research. The present PhD thesis includes the first GWAS of clinically relevant phenotypes in CD. A total of 17 phenotypes associated with different clinical complications were analyzed. In this study, we identified new genetic regions significantly associated to complicated disease course, disease location, mild disease course, and erythema nodosum. These findings are of high relevance since they show the existence of a genetic component for disease heterogeneity that is independent of the genetic variation associated with susceptibility to CD. Durant la darrera dècada, la genòmica ha jugat un paper clau en la caracterització de la base molecular de les malalties complexes. Els estudis d'associació de genoma complet (GWAS) han permès caracteritzar les regions genètiques que influencien fenotips humans tals com la susceptibilitat a desenvolupar malalties complexes. En metabolòmica, millores en les tecnologies analítiques han impulsat l'obtenció de perfils metabolòmics en grans cohorts de mostres. Els estudis resultants han demostrat també un gran potencial per a identificar biomarcadors d'utilitat en malalties humanes. L'aplicació de les tecnologies high-throughput permet generar grans conjunts de dades de variació biològica i l'extracció de la informació rellevant requereix l'aplicació de potents eines bioinformàtiques. Aquesta tesi es centra en el desenvolupament de nous mètodes per a millorar i agilitzar el processat de dades genòmiques i metabolòmiques high-throughput, així com la seva posterior implementació en forma d'aplicacions bioinformàtiques. Aquestes aplicacions s'han incorporat al flux d'anàlisi del consorci IMID (malalties inflamatòries mediades per immunitat). Aquest consorci és una xarxa espanyola d'investigadors biomèdics amb l'interès comú de l'estudi de malalties autoimmunes i disposa d'una de les col·leccions de mostres més extenses de pacients d'aquestes malalties. La primera eina bioinformàtica implementada consisteix en un conjunt d'algoritmes que integren el genotipat de polimorfismes de nucleòtid simple i variacions de nombre de còpies sobre dades de microarrays de genotipat. Aquesta eina, anomenada GStream, incorpora de forma eficient tot el flux d'anàlisi necessari per al genotipat en GWAS. S'ha demostrat que els algoritmes desenvolupats milloren significativament la precisió del genotipat i augmenten el nombre de variants genètiques identificades respecte a les metodologies anteriors. La utilització d'aquesta eina permet doncs ampliar el nombre de variants genètiques analitzades, incrementant de forma significativa el poder estadístic dels estudis genètics GWAS. La segona eina desenvolupada ha estat FOCUS. Es tracta d'una eina bioinformàtica integrada que inclou totes les etapes de processat d'espectres de ressonància magnètica nuclear per a estudis de metabolòmica. El flux d'anàlisi inclou el control de qualitat, l'alineament/quantificació de pics espectrals i la identificació dels metabolits associats als pics quantificats. Tots els algoritmes han estat dissenyats per a corregir els biaixos que limiten considerablement la qualitat dels resultats i que són un dels reptes tècnics de la metabolòmica actual. FOCUS obté una matriu numèrica d'alta qualitat llesta per a l'anàlisi quimiomètric, i genera uns scores d'identificació que simplifiquen la interpretació biològica dels resultats. FOCUS ha assolit un rendiment significativament superior al de metodologies prèvies. Aquesta tesi conclou amb el primer GWAS de fenotips clínics de malaltia de Crohn. Aquesta malaltia IMID és la malaltia inflamatòria intestinal de major prevalença i és molt heterogènia, amb pacients que presenten graus molt diferents de gravetat. La identificació de variants genètiques associades als fenotips d'aquesta malaltia és, per tant, un dels objectius més rellevants per a la investigació translacional. Un total de 17 fenotips han estat analitzats utilitzant cohorts de descobriment i validació per tal d'identificar i replicar loci de risc associats a cadascun d'ells. Els resultats de l'estudi han permès identificar, per primer cop, regions genètiques associades a l'evolució de la malaltia i a la seva localització. Aquests resultats són de gran rellevància ja que no tan sols han permès identificar noves vies biològiques associades a fenotips clínics, sinó que també demostren, per primer cop, la existència d'un component genètic de la heterogeneïtat a la malaltia de Crohn i que és independent de la variació genètica associada al risc de patir la malaltia.

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18

Porter, Sarah Elizabeth Graham. "Chemometric analysis of multivariate liquid chromatography data : applications in pharmacokinetcs, metabolomics and toxicology /." Available to VCU users online at:, 2006. http://hdl.handle.net/10156/1816.

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Porter, Sarah Elizabeth Graham. "Chemometric Analysis of Multivariate Liquid Chromatography Data: Applications in Pharmacokinetics, Metabolomics, and Toxicology." VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd/1156.

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In the first part of this work, LC-MS data were used to calculate the in-vitro intrinsic clearances (CLint) for the metabolism of p-methoxyrnethamphetamine (PMMA) and fluoxetine by the CYP2D6 enzyme using a steady-state (SS) approach and a new general enzyme (GE) screening method. For PMMA, the SS experiment resulted in a CLint of 2.7 ± 0.2 µL pmol 2D6-1min-1 and the GE experiment resulted in a CLint of 3.0 ± 0.6 µL pmol 2D6-1min-1. For fluoxetine, the SS experiment resulted in a CLint of 0.33 ± 0.17 µL pmol 2D6-1min-1 and the GE experiment resulted in a CLint of 0.188 ± 0.013 µL pmol 2D6-1min-1. The inhibition of PMMA metabolism by fluoxetine was also demonstrated.In the second part of the work, target factor analysis was used as part of a library search algorithm for the identification of drugs in LC-DAD chromatograms. The ability to resolve highly overlapped peaks using the spectral data afforded by the DAD is what distinguished this method from conventional library searching methods. A validation data set of 70 chromatograms was used to calculate the sensitivity (correct identification of positives) and specificity (correct identification of negatives) of the method, which were 92% and 94% respectively.Finally, the last part of the work shows the development of data analysis methods for four-way data generated by two-dimensional liquid chromatography separations with DAD. Maize seedlings were analyzed, specifically focusing on indole-3-acetic acid (IAA) and related compounds. Window target testing factor analysis was used to identify the spectral groups represented by the standards in the mutant and wild-type chromatograms. Two curve resolution algorithms were applied to resolve overlapped components in the data and to demonstrate the quantitative potential of these methods. A total of 95 peaks were resolved. Of those peaks, 45 were found in both the mutant and wild-type maize, 16 peaks were unique to the mutants, 13 peaks were unique to the wild-types, and the remaining peaks were standards. Several IAA conjugates were quantified in the maize samples at levels of 0.3 - 2 µg/g plant material.

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Davenport, Peter William. "A metabolomics-based analysis of acyl-homoserine lactone quorum sensing in Pseudomonas aeruginosa." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274674.

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Pseudomonas aeruginosa is a metabolically versatile environmental bacterium that grows in extremely diverse habitats—from sea water to jet fuel—and is able to infect a large variety of organisms. It is a significant cause of human disease and is one of the most frequent healthcare-associated infections. P. aeruginosa uses a sophisticated gene regulatory network to adapt its growth strategy to these diverse environmental niches and the fluctuating conditions it encounters therein. The las and rhl “quorum sensing” (QS) intercellular communication systems play integral roles in this regulatory network and control the expression of factors important to the bacterium’s ecological fitness, including many secreted factors involved in nutrient acquisition, microbial competition, and virulence. These QS systems use diffusible acyl-homoserine lactone (AHL) signalling molecules to infer environmental parameters, including bacterial population density, and to coordinate behaviour across bacterial communities. This dissertation describes an investigation into the relationship between QS and small molecule primary metabolism, using metabolomic methods based on nuclear magnetic resonance spectroscopy and mass spectrometry. Analysis of extracellular metabolic profiles (the bacteria’s “metabolic footprint”) established that QS can modulate the uptake and excretion of primary metabolites and that this effect was strongest during the transition from exponential to stationary phase cell growth. Analysis of the cellular metabolome and proteome demonstrated that QS affected most major branches of primary metabolism, notably central carbon metabolism, amino acid metabolism and fatty acid metabolism. These data indicate that QS repressed acetogenesis and the oxidative C02-evolving portion of the TCA cycle, while inducing the glyoxylate bypass and arginine fermentation. QS also induced changes to fatty acid pools associated with lower membrane fluidity and higher chemical stability. Elevated levels of stress-associated polyamines were detected in QS mutants, which may be a consequence of a lack of QS-dependent adaptations. These findings suggest that wild-type QS directs metabolic adaptations to stationary phase stressors, including oxidative stress. Previous work, including several transcriptomic studies, has suggested that QS can play a role in primary metabolism. However, there has been no previous study of the global impact of AHL QS on the metabolome of P. aeruginosa. Research presented here demonstrates that QS induces a global readjustment in the primary metabolism and provides insight into QS- dependent metabolic changes during stationary-phase adaptation.

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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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Shiryaeva, Liudmila. "Proteomics and metabolomics in biological and medical applications." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-43520.

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Biological processes in living organisms consist of a vast number of different molecular networks and interactions, which are complex and often hidden from our understanding. This work is focused on recovery of such details for two quite distant examples: acclimation to extreme freezing tolerance in Siberian spruce (Picea obovata) and detection of proteins associated with prostate cancer. The first biological system in the study, upon P. obovata, is interesting by this species ability to adapt and sustain extremely low temperatures, such as -60⁰C or below. Despite decades of investigations, the essential features and mechanisms of the amazing ability of this species still remains unclear. To enhance knowledge about extreme freezing tolerance, the metabolome and proteome of P. obovata’s needles were collected during the tree’s acclimation period, ranging from mid August to January, and have been analyzed. The second system within this study is the plasma proteome analysis of high risk prostate cancer (PCa) patients, with and without bone metastases. PCa is one of the most common cancers among Swedish men, which can abruptly develop into an aggressive, lethal disease. The diagnostic tools, including PSA-tests, are insufficient in predicting the disease’s aggressiveness and novel prognostic markers are urgently required. Both biological systems have been analyzed following similar steps: by two-dimensional difference gel electrophoresis (2D-DIGE) techniques, followed by protein identification using mass spectrometry (MS) analysis and multivariate methods. Data processing has been utilized for searching for proteins that serve as unique indicators for characterizing the status of the systems. In addition, the gas chromatography-mass spectrometry (GC-MS) study of the metabolic content of P.obovata’s needles, from the extended observation period, has been performed. The studies of both systems, combined with thorough statistical analysis of experimental outcomes, have resulted in novel insights and features for both P. obovata and prostate cancer. In particular, it has been shown that dehydrins, Hsp70s, AAA+ ATPases, lipocalin and several proteins involved in cellular metabolism etc., can be uniquely associated with acclimation to extreme freezing in conifers. Metabolomic analysis of P. obovata needles has revealed systematic metabolic changes in carbohydrate and lipid metabolism. Substantial increase of raffinose, accumulation of desaturated fatty acids, sugar acids, sugar alcohols, amino acids and polyamines that may act as compatible solutes or cryoprotectants have all been observed during the acclimation process. Relevant proteins for prostate cancer progression and aggressiveness have been identified in the plasma proteome study, for patients with and without bone metastasis. Proteins associated with lipid transport, coagulation, inflammation and immune response have been found among them.

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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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Näsström, Elin. "Diagnosis of acute and chronic enteric fever using metabolomics." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-140188.

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Enteric (or typhoid) fever is a systemic infection mainly caused by Salmonella Typhi and Salmonella Paratyphi A. The disease is common in areas with poor water quality and insufficient sanitation. Humans are the only reservoir for transmission of the disease. The presence of asymptomatic chronic carriers is a complicating factor for the transmission. There are major limitations regarding the current diagnostic methods both for acute infection and chronic carriage. Metabolomics is a methodology studying metabolites in biological systems under influence of environmental or physiological perturbations. It has been applied to study several infectious diseases, with the goal of detecting diagnostic biomarkers. In this thesis, a mass spectrometry-based metabolomics approach, including chemometric bioinformatics techniques for data analysis, has been used to evaluate the potential of metabolite biomarker patterns for diagnosis of enteric fever at different stages of the disease. In Paper I, metabolite patterns related to acute enteric fever were investigated. Human plasma samples from patients in Nepal with culture-confirmed S. Typhi or S. Paratyphi A infection were compared to afebrile controls. A metabolite pattern discriminating between acute enteric fever and afebrile controls, as well as between the two causative agents of enteric fever was detected. The strength of using a panel of metabolites instead of single metabolites as biomarkers was also highlighted. In Paper II, metabolite patterns for acute enteric fever, this time focusing only on S. Typhi infections, were investigated. Human plasma from patients in Bangladesh with culture-positive or -negative but clinically suspected S. Typhi infection were compared to febrile controls. Differences were found in metabolite patterns between the culture-positive S. Typhi group and the febrile controls with a heterogeneity among the suspected S. Typhi samples. Consistencies in metabolite patterns were found to the results from Paper I. In addition, a validation cohort with culture-positive S. Typhi samples and a control group including patients with malaria and infections caused by other pathogens was analysed. Differences in metabolite patterns were detected between S. Typhi samples and all controls as well as between S. Typhi and malaria. Consistencies in metabolite patterns were found to the primary Bangladeshi cohort and the Nepali cohort from Paper I. Paper III focused on chronic Salmonella carriers. Human plasma samples from patients in Nepal undergoing cholecystectomy with confirmed S. Typhi or S. Paratyphi A gallbladder carriage were compared to non-carriage controls. The Salmonella carriage samples were distinguished from the non-carriage controls and differential signatures were also found between the S. Typhi and S. Paratyphi A carriage samples. Comparing metabolites found during chronic carriage and acute enteric fever (in Paper I) resulted in a panel of metabolites significant only during chronic carriage. This work has contributed to highlight the potential of using metabolomics as a tool to find diagnostic biomarker patterns associated with different stages of enteric fever.

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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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Uawisetwathana, Umaporn. "Metabolomics analysis of brown planthopper (BPH)-resistant traits in Thai Jasmine rice (Oryza sativa)." Thesis, Queen's University Belfast, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.696327.

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A metabolomics platform technology was used to study known traits of Thai rice, brown planthopper (BPH) resistance trait. Three rice varieties with different BPH resistance capacity, namely BPH-susceptible KD cultivar, BPH-resistant IL7 containing Bph 3 and BPH-resistant+ IL308 containing Bph 3 and terpene synthase (TPS) genes, were analyzed to identify differential metabolomics profiles between them, with and without BPH infestation at different time points (Days 1, 2, 3, 4 and 8). Metabolic profiles were obtained using the analytical methods including Proton Nuclear Magnetic Resonance (1 H NMR) Spectroscopy, Gas Chromatography-Mass Spectrometry (GC-MS) and Ultra Performance Liquid Chromatography Quadrupole Time of Flight -Mass Spectrometry (UPLC-QToF-MS). The metabolomics data were analysed using multivariate statistical analysis to reveal metabolite markers underlying those traits. Different physiological responses from the three varieties were observed in leaves which were dependent on the level of BPH resistance. Untargeted metabolome profiling of rice leaves obtained by 1 H NMR provided thirty primary metabolites profiles revealing the separation between early and late responses. UPLC-QToF-MS method provided more sensitivity and coverage of compounds, hence, it revealed the effective secondary metabolites differences in the early response between the resistant+ IL308 and the other examined (KD and IL7) varieties. Besides, target fatty acid analysis identified fourteen potential fatty acid associated with the BPH resistance. The overall metabolic pathways obtained by the three different methods suggested that BPH infestation causes the metabolic perturbations in transamination, amino acid metabolism, shikimate, purine/pyrimidine, gluconeogenesis, phenylpropanoid and fatty acid pathways. The susceptible and the resistant rice varieties were likely to employ different pathways to fight against BPH infestation. This study identified several potential metabolic pathways of Thai Jasmine rice and its BPH-isogenic lines that can play a role in acclimatization and defense mechanisms against BPH infestation.

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Palisi, Angelica. "NMR-based metabolomic analysis of biological fluids to monitor relevant unsolved diseases." Doctoral thesis, Universita degli studi di Salerno, 2017. http://hdl.handle.net/10556/2565.

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2015 - 2016 Metabolomics and metabonomics encompass the comprehensive profiling of multiple metabolite concentrations and their cellular and systemic fluctuations in response to drugs, diet, lifestyle, environment, stimuli and genetic modulations, in order to characterize the beneficial and adverse effects of such interactions. In the context of biomedical applications, metabolomics will have a preferential role with respect to the other "Omics" sciences for its ability to detect in real time the response of the organisms to pathological stressors. The application of the NMR technique for the metabolomics analysis was applied to bio-fluids deriving from populations of patients respectively affected by salivary gland tumor, antiphospholipid autoimmune syndrome and altered lipid profile. This NMR metabolomic screening was aimed i) at the definition of a metabolomic profile that may be patognomonic of the disease under scrutiny and ii) at the identification of biomarkers to be used with diagnostic and prognostic scope. In the present work, we present a NMR-based metabolomic study of saliva of patients suffering of salivary gland tumors. Our data show that individuals suffering parotid tumor have a characteristic metabolomic profile with abnormalities associated to the metabolism of acetate, alanine, lactate, methanol, phenylalanine, propionate, succinate. We have identified for the first time the metabolomic fingerprint characterizing parotid tumor patients disease having potential application to improve timely diagnosis and appropriate therapeutic approaches. Salivary gland tumor, as many other cancers, is a complex disease, resulting from an interdependent series of biochemical alterations, rather than a single disruptive event. In this case our approach aimed at the identification of a panel of metabolite markers rather than a single biomarker, will improve the sensitivity and specificity for detection. Integrating the protocols of tumor grading and histological classification. Our NMR-based metabolomic study revealed different metabolomic profiles in saliva of male patients affected by salivary gland tumors compared with the profiles of age, gender, and sampling-date matched control individuals. Our approach provide preliminary data for the identification of metabolites that can be used as metabolomics fingerprint of salivary gland tumor. Determination of metabolomics fingerprint, rather than single metabolic biomarker, may fully reflect the multifactorial nature of oncogenesis and the heterogeneity of oncogenic pathways, providing precious elements to integrate diagnostic laboratory and clinical tests. Antiphospholipid syndrome (APS) is a rheumatic inflammatory chronic autoimmune disease inducing hypercoagulable state associated with vascular thrombosis and pregnancy loss in women. Cardiac, cerebral and vascular strokes in these patients are responsible for reduction in life expectancy. Timely diagnosis and accurate monitoring of disease is decisive to improve the accuracy of therapy. In the present work, we present a NMR-based metabolomic study of blood sera of APS patients. Our data show that individuals suffering APS have a characteristic metabolomic profile with abnormalities associated to the metabolism of methyl group donors, ketone bodies and amino acids. We have identified for the first time the metabolomic fingerprint characterizing APS disease having potential application to improve APS timely diagnosis and appropriate therapeutic approaches. The first stratification of APS patients according to the gender offers preliminary indications for the management of the disease according to the gender oriented medicinal approach. Human serum includes a large number of components which derive from endogenous metabolism and nutritional intake. Serum components vary in response to diet. Serum lipid composition is probably the most important benchmark in assessing cardiovascular risk and disease progression. Serum components, also derived from nutritional intake, can affect general metabolism and, more specifically, affect molecular mechanisms and pathways linking nutritional intake and chronic disease risk. To identify the effect exerted by altered lipid composition on the genome expression pattern, response of gene expression to serum samples from hypercholesterolemic and normocholesterolemic male subjects was previously studied. In the present part of my PhD thesis, using a NMR metabolomics approach I studied the metabolomics profile of the aforementioned hypercholesterolemic and normocholesterolemic sera to correlate the previously identified trascriptomic signature of human hepatoma cells to the relative metabolomics profile. Hypercholesterolemic sera previously proved to increase in human hepatoma cells, the mRNA expression of HMGCS2, an enzyme involved in the pathway of keton bodies. Our NMR based metabolomics analysis evidences abnormal concentrations of metabolites involved in the keton bodies pathway. This indicates a correlation between the trascriptomic profile of hepatoma cells treated with hypercholesterolemic sera, and the metabolomics profile of the same sera. [edited by author] La metabolomica e la metabonomica comprendono il profilo completo di numerosi metaboliti con riferimento alle varie concentrazioni e fluttuazioni sia cellulari che sistemiche in risposta a farmaci, dieta, stile di vita, influenza dell'ambiente, stimoli e modulazioni genetiche, al fine di caratterizzare gli effetti benefici e negativi di tali interazioni. Nel contesto delle applicazioni biomediche, la metabolomica avrà in futuro un ruolo preferenziale rispetto alle altre scienze 'omiche' per la possibilità di rilevare in tempo reale la risposta degli organismi agli stress patologici. L' applicazione della tecnica NMR è stata utilizzata per l' analisi metabolomica di bio-fluidi derivanti da popolazioni di pazienti affetti rispettivamente da tumore delle ghiandole salivari; da sindrome da antifosfolipidi; pazineti con profilo lipidico alterato. Questo screening metabolomico NMR è mirato i) alla definizione di un profilo metabolomico che potrebbe essere patognomonico delle malatte monitorate e ii) l'identificazione di biomarcatori da utilizzare in ambito diagnostico e prognostico. In questo studio metabolomico basato su analisi NMR della saliva di pazienti affetti dai tumori delle ghiandole salivari i nostri dati mostrano caratteristiche anomalie nel profilo metabolomico connesse con il metabolismo di acetato , alanina, lattato, metanolo, fenilalanina, propionato, succinato. Abbiamo identificato per la prima volta l'impronta digitale metabolomica che caratterizza pazienti con tumori della parotide con una potenziale applicazione per migliorare la diagnosi tempestiva ed un approccio terapeutico adeguato. I tumori alle ghiandole salivari, come molti altri tipi di cancro, sono patologie complesse, risultanti da una serie interdipendente di alterazioni biochimiche, piuttosto che un singolo evento dirompente. In questo caso, con un approccio rivolto all'identificazione di un panel di metaboliti marcatori, piuttosto che ad un singolo biomarcatore, miglioreranno ed aumenteranno la sensibilità e la specificità per il rilevament, integrando i protocolli diagnostici classici e la classificazione istologica. Il nostro studio metabolomico NMR-based ha rivelato diversi profili nella saliva di pazienti affetti da tumori delle ghiandole salivari, confrontati in base all' età e al sesso, abbinati con i controlli. Il “finger print”, piuttosto che i singoli biomarkers, può riflettere in pieno la natura multifattoriale ed etrogenea della oncogenesi , fornendo preziosi elementi per integrare i test diagnostici clinici e di laboratorio. La sindrome antifosfolipidi (APS) è una malattia autoimmune, reumatica, infiammatoria cronica associata ad uno stato di ipercoagulabilità: inducendo trombosi vascolari ed aborti spontaeni nelle donne. Ictus cerebrali e vascolari in questi pazienti sono responsabili della riduzione della aspettativa di vita: una diagnosi tempestiva ed un accurato monitoraggio della malattia è determinante per migliorare la precisione della terapia. Nel presente lavoro, vi presentiamo uno studio di metabolomica NMR su siero di pazienti affetti da APS. I nostri dati mostrano che gli individui che soffrono di APS hanno un profilo metabolomico caratteristico con anomalie del metabolismo associate ai donatori di gruppi metilici, di aminoacidi e corpi chetonici. Abbiamo identificato per la prima volta il “finger print” della sindrome da APS con la potenziale applicazione di migliorare la diagnosi tempestiva e favorire un approccio terapeutico adeguato. La prima stratificazione di pazienti APS pazienti in base al sesso offre indicazioni per la gestione della malattia secondo un approccio medico gender oriented. Il siero umano comprende un gran numero di componenti derivanti sia dal metabolismo endogeno sia dall' apporto nutrizionale i quali variano in risposta alla dieta. La composizione lipidica del siero è probabilmente il punto di riferimento più importante nella valutazione del rischio cardiovascolare e della progressione della malattia. Inoltre la composizione lipidica può influenzare il metabolismo e più in particolare, i percorsi molecolari che collegano l' apporto nutrizionale ed rischio di malattia cronica. L'effetto esercitato dalla composizione lipidica modificata sul pattern genomico in risposta all' espressione su campioni di siero da sogetti maschi ipercolesterolemici, confrontati con normocholesterolemici è stato oggetto di un precedente studio. Nell' ultimaparte di questa tesi di dottorato, utilizzando l'approccio metabolomico NMR ho studiato il profilo dei supramenzionati ipercolesterolemici e normocholesterolemici per correlare il profilo trascrittomico ottenuto dalle cellule epatiche umane con il profilo metabolomico del siero umano utilizzato per la cultura, mostrando una aumentata espressione di mRNA di HMGCS2, un enzima coinvolto nel percorso di corpi chetonici. Dall' analisi NMR sono emerse concentrazioni alterate di metaboliti coinvolti della via biosintetica dei corpi chetonici. Questo indica una correlazione tra il profilo trascriptomico di cellule epatiche trattate con sieri ipercolesterolemici, e il profilo metabolomica dei sieri stessi. [a cura dell'autore] XV n.s. (XXIX )

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Diaz, Sílvia de Oliveira. "Pregnancy and newborns disorders followed by urine metabolomics." Doctoral thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13110.

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Doutoramento em Química Chapter 1 introduces the scope of the work by identifying the clinically relevant prenatal disorders and presently available diagnostic methods. The methodology followed in this work is presented, along with a brief account of the principles of the analytical and statistical tools employed. A thorough description of the state of the art of metabolomics in prenatal research concludes the chapter, highlighting the merit of this novel strategy to identify robust disease biomarkers. The scarce use of maternal and newborn urine in previous reports enlightens the relevance of this work. Chapter 2 presents a description of all the experimental details involved in the work performed, comprising sampling, sample collection and preparation issues, data acquisition protocols and data analysis procedures. The proton Nuclear Magnetic Resonance (NMR) characterization of maternal urine composition in healthy pregnancies is presented in Chapter 3. The urinary metabolic profile characteristic of each pregnancy trimester was defined and a 21-metabolite signature found descriptive of the metabolic adaptations occurring throughout pregnancy. 8 metabolites were found, for the first time to our knowledge, to vary in connection to pregnancy, while known metabolic effects were confirmed. This chapter includes a study of the effects of non-fasting (used in this work) as a possible confounder. Chapter 4 describes the metabolomic study of 2nd trimester maternal urine for the diagnosis of fetal disorders and prediction of later-developing complications. This was achieved by applying a novel variable selection method developed in the context of this work. It was found that fetal malformations (FM) (and, specifically those of the central nervous system, CNS) and chromosomal disorders (CD) (and, specifically, trisomy 21, T21) are accompanied by changes in energy, amino acids, lipids and nucleotides metabolic pathways, with CD causing a further deregulation in sugars metabolism, urea cycle and/or creatinine biosynthesis. Multivariate analysis models´ validation revealed classification rates (CR) of 84% for FM (87%, CNS) and 85% for CD (94%, T21). For later-diagnosed preterm delivery (PTD), preeclampsia (PE) and intrauterine growth restriction (IUGR), it is found that urinary NMR profiles have early predictive value, with CRs ranging from 84% for PTD (11-20 gestational weeks, g.w., prior to diagnosis), 94% for PE (18-24 g.w. pre-diagnosis) and 94% for IUGR (2-22 g.w. pre-diagnosis). This chapter includes results obtained for an ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) study of pre-PTD samples and correlation with NMR data. One possible marker was detected, although its identification was not possible. Chapter 5 relates to the NMR metabolomic study of gestational diabetes mellitus (GDM), establishing a potentially predictive urinary metabolic profile for GDM, 2-21 g.w. prior to diagnosis (CR 83%). Furthermore, the NMR spectrum was shown to carry information on individual phenotypes, able to predict future insulin treatment requirement (CR 94%). Chapter 6 describes results that demonstrate the impact of delivery mode (CR 88%) and gender (CR 76%) on newborn urinary profile. It was also found that newborn prematurity, respiratory depression, large for gestational age growth and malformations induce relevant metabolic perturbations (CR 82-92%), as well as maternal conditions, namely GDM (CR 82%) and maternal psychiatric disorders (CR 91%). Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the value of maternal or newborn urine metabolomics for pregnancy monitoring and disease prediction, towards the development of new early and non-invasive diagnostic methods. O Capítulo 1 descreve o enquadramento deste trabalho identificando as doenças pré-natais relevantes e os métodos de diagnóstico actualmente disponíveis. É depois apresentada a metodologia seguida, assim como uma breve introdução dos princípios dos métodos analíticos e estatísticos aplicados. O capítulo é concluído com uma descrição do estado da arte na área de metabolómica em investigação pré-natal, identificando o mérito desta inovadora estratégia para a identificação de marcadores robustos de doenças pré-natais. A relevância deste trabalho torna-se clara através do escasso uso de urina materna e do recém-nascido em trabalhos anteriores. O Capítulo 2 descreve os procedimentos experimentais utilizados neste trabalho, incluindo condições de amostragem, recolha e preparação das amostras, protocolos de aquisição e de tratamento dos dados. A caracterização da composição da urina materna, através de espectroscopia de Ressonância Magnética Nuclear (RMN) de protão é apresentada no Capítulo 3. Define-se o perfil metabólico urinário característico para cada trimestre de gravidez, tendo sido encontrado um conjunto de 21 metabolitos descritivo das alterações metabólicas ocorridas ao longo da gravidez. 8 metabolitos foram encontrados a variar com a gravidez, pela primeira vez, tendo sido confirmadas variações metabólicas conhecidas. É ainda estudado o efeito do não-jejum (usado neste trabalho) como possível factor de confusão. O Capítulo 4 apresenta o estudo metabolómico de urina materna do 2º trimestre para o diagnóstico de doenças fetais e previsão de complicações mais tarde desenvolvidas. Este estudo compreende a aplicação de um método de selecção de variáveis desenvolvido no âmbito desta tese. Observou-se que as malformações fetais (e, especificamente, do sistema nervoso central, SNC) e as cromossomopatias (e, especificamente, a trissomia 21, T21) são acompanhadas por alterações nos metabolismos energético, dos aminoácidos, lípidos e nucleótidos, enquanto que as cromossomopatias mostraram ser acompanhadas por uma desregulação adicional dos metabolismos dos açúcares, ciclo da ureia e/ou biossíntese da creatinina. A validação dos modelos multivariados revelou taxas de classificação (CR) de 84% para malformações (87%, SNC) e 85% para CD (94%, T21). Para o parto pré-termo, pré-eclampsia (PE) e restrição de crescimento intrauterino (RCIU) observaram-se perfis que podem ajudar à previsão precoce, com CR 84% para pretermo (11-20 semanas de gestação, g.w. pré-diagnóstico), 94% para PE (18-24 g.w. pré-diagnóstico) e 94% para RCIU (2-22 g.w. pré-diagnóstico). Este capítulo inclui resultados obtidos por cromatografia líquida de ultra eficiência acoplada a espectrometria de massa (UPLC-MS) para pré-pretermo e correlação com os dados de RMN. Um possível composto marcador foi detectado mas a sua identificação não foi possível. O Capítulo 5 descreve o estudo metabolómico por RMN da diabetes mellitus gestacional (DMG), estabelecendo-se um perfil metabólico potencialmente preditivo da doença (CR 83%, 2-21 g.w. pré-diagnóstico). Verificou-se ainda que o espectro de RMN contém informação sobre o fenótipo individual, capaz de prever a necessidade futura de tratamento com insulina (CR 94%). No Capítulo 6 demonstra-se o impacto do tipo de parto (CR 88%) e género do bebé (CR 76%) no perfil da urina do recém-nascido. Verificou-se ainda que a prematuridade, depressão respiratória, crescimento grande para a idade gestacional e malformações induzem perturbações metabólicas relevantes (CR 82-92%), assim como algumas doenças maternas como a DMG (CR 82%) e doenças psiquiátricas (91% CR). Finalmente, no Capítulo 7 apresentam-se as principais conclusões deste trabalho, enfatizando o potencial da metabolómica de urina materna e do bebé para o acompanhamento da gravidez e previsão de doenças, visando o desenvolvimento de novos métodos de diagnóstico precoce e não-invasivo.

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Pietzke, Matthias [Verfasser]. "Analysis of the metabolic control of cell growth using stable isotope resolved metabolomics / Matthias Pietzke." Berlin : Freie Universität Berlin, 2015. http://d-nb.info/1067442219/34.

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Parsons, Helen Michelle. "Optimised spectral processing and lineshape analysis in 2-dimensional J-resolved NMR spectroscopy based metabolomics." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/816/.

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NMR spectroscopy is a primary analytical approach of metabolomics. Although 1D 1H NMR spectroscopy is versatile, highly reproducible and widely used, analysis of complex biological samples yields congested spectra with many overlapping signals. This makes metabolite identification and quantification challenging. 1H J-resolved (JRES) experiments spreads this high signal density into a second dimension, simplifying the spectral analysis. This thesis analyses the approaches and suitability of JRES spectroscopy to analyse metabolomics data. Firstly, the robustness of the JRES experiment is investigated. Using spectral relative standard deviation, benchmarks of spectral robustness can be compared between disparate processing techniques, sample types and analytical platforms. JRES spectra were found to be suitable for metabolomics experiments. Secondly, the application of standard metabolomic analysis methods to JRES spectra was examined. Using principal component analysis, the classification accuracy of 1D 1H and JRES spectra were investigated using several data sets. Alongside, three scaling methods were also evaluated. It was found that 2D JRES spectra and the glog transformation could produce 100% classification accuracy. Finally, spectral deconvolution of 2D JRES spectra from line-shape fitting was investigated Here, the mathematical functions describing the JRES line-shape, under several different processing conditions, are derived and used to create a semi-automated metabolite identification and quantification algorithm. Furthermore, possible quantitation errors arising from using JRES spectra are investigated, evaluating effects such as the overlapping of dispersive tails of nearby signals. In conclusion, the JRES experiment is a suitable for use in the field of metabolomics.

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Gutiérrez, Eva Caamaño. "The effect of diet on Plasmodium falciparum development revealed by NMR metabolomics and image analysis." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/87354/.

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The development of axenic in vitro growth models of the human malaria parasite Plasmodium falciparum, has been pivotal in accelerating knowledge of this very important human pathogen. Despite the importance of this pathogen, there have been very few studies relating to the metabolism of the parasite. Furthermore, much of the preceding studies have been undertaken using culture conditions that do not accurately represent the physiological environment of the human host. There is a need to address whether different nutrient environments would trigger a parasite response at the systems level promoting a metabolic rewiring that would have an effect in progeny generation or life cycle progression. Because of its robustness, reproducibility and suitability for footprinting studies, NMR spectroscopy was chosen as the analytic technique for the study. One of the disadvantages of NMR is limited availability of software for identification and quantification of metabolites. This was taken as an opportunity to develop a pipeline using free, open-source programming framework. This tool was used to find unique and discriminatory metabolic profiles for both uninfected and P. falciparum infected red blood cells at various life-cycle stages using cell extracts and extracellular material. With the aim of studying parasite development in physiological conditions a culture medium mimicking human blood conditions was developed and tested on P. falciparum infected RBCs finding both phenotypic and metabolic differences. Further studies consisted of the development of tightly synchronised parasite cultures that were followed during 54 h using NMR-based metabolomics to assess consumption and excretion of metabolites in media, and high content imaging and bright field microscopy to assess parasite size and progeny. The measurements were taken under three different nutritional conditions: usual in vitro, physiological-like and hypoglycaemic. In usual culturing conditions P. falciparum 3D7 life cycle lasted around 45 h. During the early stages there was moderate consumption of glucose and glutamine and excretion of lactate, alanine and glycerol. During the mature trophozoite stages and schizonts, glucose uptake dramatically increased with a consequent augmentation of the lactate, alanine and glycerol production. These were excreted but their function was not clear. It was observed that these “wasteful" products were proportionally lower in the early developmental stages than in the later ones, suggesting a higher demand of raw materials (glucose) for biomass production during the early stages. During the late trophozoite stage the most abundant amino acids in the haemoglobin chain (leucine, valine and glycine) were excreted, a likely consequence of the need for space to nish maturation. Myoinositol, which is essential for creation of membranes was also avidly consumed. When comparing these findings with parasites growing in more physiological conditions there was a noticeable delay in the life cycle of at least 9 h. Consequently haemoglobin digestive products were excreted later in the time course. A decrease in the progeny resulting from schizonts containing significantly fewer merozoites was also observed. Parasites growing in physiological conditions but challenged with lower glucose availability also presented a further delay of the life-cycle and a decreased number of merozoites with respect to usual laboratory conditions. Haemoglobin degradation products were also excreted later in the life cycle and at lower rates compared to the parasites grown in complete media. These results suggest that there are significant differences between in vivo and in vitro life-cycles of P. falciparum. Such effects as the reduction in growth rates and elongation of the life cycle, if not accounted for, could severely compromise the in vivo results of in vitro drug killing rates assays.

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Davoren, Elmarie. "A metabolomics study of selected perturbations of normal human metabolism / Elmarie Davoren." Thesis, North-West University, 2010. http://hdl.handle.net/10394/4219.

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Metabolism is an integrated network of biochemical pathways involving a series of enzymecatalysed anabolic or catabolic reactions in cells. Metabolites are chemical compounds that are involved in or are products of metabolic pathways, and the metabolome is defined as the total complement of all the low molecular weight metabolites present in a cell at any given time. Metabolomics is a relatively new research technology utilised for the global investigation, identification and quantification of the metabolome. Three aims were defined for the metabolomics study presented here: • The use of metabolomics technology to generate new biological information; • Application of the metabolomics technology to gain information on the three natural perturbations, namely the menstrual cycle, pregnancy and aging; and • Reflection on metabolomic studies as a hypothesis-generating approach. I obtained three sets of urine samples from women during their menstrual cycle, samples from sixteen pregnant and eleven non-pregnant women for the second natural perturbation, and data sets from previous investigations on infant and child groups, as well as thirty-two urine samples from adults for the study of the metabolomic profiles due to age. These urine samples were analysed to determine the organic acid metabolite profiles. The metabolites were identified by means of AMDIS and were manually quantified. Data matrixes were compiled, which underwent certain data reduction steps, prior to statistical analysis. Different statistical approaches were used to generate information on these three natural perturbations due to the clear differences between the three experimental groups used. The investigation of the menstrual cycle did not show a distinct difference between the three phases involved in the cycle, whereas the pregnancy perturbation showed a difference between pregnant groups and non-pregnant groups. The most pronounced difference in metabolite profiles were found when the different age groups were compared to one another. Finally a hypothesis on the effect of age on metabolism was defined and an experimental approach was proposed to evaluate this hypothesis. In conclusion three proposals were formulated from this investigation: 1. If it appears that an insufficient number of participants can be generated for a metabolomics study, such a study should be discarded in the interest of a more feasible investigation. 2. It is advisable that a number of appropriate analytical validation parameters should be incorporated in the early stages of a metabolomics study, specifically linked to the context of the perturbation chosen for the investigation. 3. The control and experimental groups should be homogenous that is to say as comparable as possible with regard to age, ethnicity, diet, and gender, lifestyle habits and other possible confounding influences, except for the specific perturbation being studied. In a perfect world this would be possible, specifically when hypothesis formulation, testing and finally the expansion of scientific knowledge is a desired outcome of the investigation. Thesis (M.Sc. (Biochemistry))--North-West University, Potchefstroom Campus, 2010.

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Karimpour, Masoumeh. "Multi-platform metabolomics assays to study the responsiveness of the human plasma and lung lavage metabolome." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-120591.

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Metabolomics as a field has been used to track changes and perturbations in the human body by investigating metabolite profiles indicating the change of metabolite levels over time and in response to different challenges. In this thesis work, the main focus was on applying multiplatform-metabolomics to study the human metabolome following exposure to perturbations, such as diet (in the form of a challenge meal) and exhaust emissions (air pollution exposure in a controlled setting). The cutting-edge analytical platforms used for this purpose were nuclear magnetic resonance (NMR), as well as gas chromatography (GC) and liquid chromatography (LC) coupled to mass spectrometry (MS). Each platform offered unique characterization features, allowing detection and identification of a specific range of metabolites. The use of multiplatform-metabolomics was found to enhance the metabolome coverage and to provide complementary findings that enabled a better understanding of the biochemical processes reflected by the metabolite profiles. Using non-targeted analysis, a wide range of unknown metabolites in plasma were identified during the postprandial stage after a well-defined challenge meal (in Paper I). In addition, a considerable number of metabolites were detected and identified in lung lavage fluid after biodiesel exhaust exposure compared to filtered air exposure (in Paper II). In parallel, using targeted analysis, both lung lavage and plasma fatty acid metabolites were detected and quantified in response to filtered air and biodiesel exhaust exposure (in Paper III and IV). Data processing of raw data followed by data analysis, using both univariate and multivariate methods, enabled changes occurring in metabolites levels to be screened and investigated. For the initial pilot postprandial study, the aim was to investigate the plasma metabolome response after a well-defined meal during the postprandial stage for two types of diet. It was found that independent of the background diet type, levels of metabolites returned to their baseline levels after three hours. This finding was taken into consideration for the biodiesel exhaust exposures studies, designed to limit the impact of dietary effects. Both targeted and non-targeted approaches resulted in important findings. For instance, different metabolite profiles were detected in bronchial wash (BW) compared to bronchoalveolar lavage (BAL) fluid with mainly NMR and LC-MS. Furthermore, biodiesel exhaust exposure resulted in different metabolite profiles as observed by GC-MS, especially in BAL. In addition, fatty acid metabolites in BW, BAL, and plasma were shown to be responsive to biodiesel exhaust exposure, as measured by a targeted LC-MS/MS protocol. In summary, the new analytical methods developed to investigate the responsiveness of the human plasma and lung lavage metabolome proved to be useful in an analytical perspective, and provided important biological findings. However, further studies are needed to validate these results. Metabolomik har använts för att spåra förändringar och störningar i kroppens funktioner genom undersökning av metabolit-profiler. I detta avhandlingasarbete har huvudfokus varit på tillämpning av flera olika analytiska plattformar för metabolomikstudier av det mänskliga metabolomet efter exponering för olika kost och avgasutsläpp från biodieselbränsle. De sofistikerade analytiska plattformarna som användes för detta ändamål var kärnmagnetisk resonans (NMR), samt gaskromatografi (GC) och vätskekromatografi (LC) kopplat till masspektrometri (MS). Varje plattform erbjöd unika karakteriseringsmöjligheter med detektion och identifiering av specifika grupper av metaboliter. Användningen av multipattformmetabolomik förbättrade täckningen av metabolomet och genererade kompletterande resultat som möjliggjorde en bättre förståelse av de biokemiska processer som reflekteras av metabolitprofilerna. Med hjälp av breda analyser har ett stort antal okända metaboliter i plasma identifierats under den postprandial fasen efter en väldefinerad måltid (i Paper I). Dessutom har ett stort antal metaboliter påvisats och identifierats i lungsköljvätska efter exponering av biodieselavgaser jämfört med kontollexponering med filtrerad luft (i Paper II). Parallellt med dessa breda analyser har också riktade analyser genomförts av både lungsköljvätska och plasma. Därigenom har bioaktiva lipider detekterats och kvantifieras efter avgasexponering och resultaten har jämförts med filtrerad luft som kontrollexponering (Paper III och IV). Processning av rådata följt av dataanalys, med både univariata och multivariata metoder möjliggjorde screening och fördjupad undersökning av förändringen i metabolitnivåer. I den första pilotstudien av postprandiala nivåer var syftet att undersöka responsen i plasmametabolomet efter en väldefinierad måltid under den postprandiala fasen vid två olika typer av kost. Resultaten visade att oberoende av kosten, så återvände metabolitnivåerna till sina baslinjenivåer tre timmar efter måltiden. Detta togs i beaktande vid exponeringsstudierna för biodieselavgaser, som designades så att dietens inverkan minimerades. Både breda och riktade analyser resulterade i viktiga resultat. Exempelvis så detekterades olika metabolitprofiler i bronkiell sköljvätska (BW) jämfört med bronkoalveolär sköljvätska (BAL), speciellt med NMR och LC-MS. Dessutom resulterade avgasexponering i förändrade metabolitprofiler, observerade med GC-MS, särskilt i BAL. Dessutom uppvisade fettsyrametaboliter i BW, BAL och plasma förändrade halter efter avgasexponering, uppmätt genom en riktad LC-MS/MS-analys. Sammanfattningsvis så visade sig de nya metoderna som utvecklats för att undersöka förändringar i metabolithalterna i plasma och lungsköljvätska fungera väl ur ett analytiskt perspektiv och resulterade i viktiga biologiska fynd. Fördjupade studier behövs dock för att validera resultaten.

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Cortes, Bermudez Diego Fernando. "Functional genomics through metabolite profiling and gene expression analysis in Arabidopsis thaliana." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28457.

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In the post-genomic era, one of the most important goals for the community of plant biologists is to take full advantage of the knowledge generated by the Arabidopsis thaliana genome project, and to employ state-of-the-art functional genomics techniques to assign function to each gene. This will be achieved through a complete understanding of what all cellular components do, and how they interact with one another to produce a phenotype. Among the proteins encoded by the Arabidopsis genome are 24 related carboxyl methyltransferases that belong to the SABATH family. Several of the SABATH methyltransferases convert plant hormones, like jasmonic acid, indole-3-acetic acid, salicylic acid, gibberellins, and other plant constituents into methyl esters, thereby regulating the biological activity of these molecules and, consequently, myriad important physiological processes. Our research aims to decipher the function of proteins belonging to the SABATH family by applying a combination of genomics tools, including genome-wide expression analysis and gas-chromatography coupled with mass spectrometry-based metabolite profiling. Our results, combined with available biochemical information, provide a better understanding of the physiological role of SABATH methyltransferases, further insights into secondary plant metabolism and deeper knowledge of the consequences of modulating the expression of SABATH methyltransferases, both at the genome-wide expression and metabolite levels. Ph. D.

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Duong, Viêt Dung. "Development of numerical approaches for nuclear magnetic resonance data analysis." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEN010/document.

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La résonance magnétique nucléaire (RMN) est devenue une des techniques spectroscopiques les plus puissantes et polyvalentes de la chimie analytique avec des applications multiples dans des différents domaines de la recherche. Cependant, un des inconvénients majeurs de la RMN est le processus fastidieux d'analyse de donnée qui nécessite fréquemment des interventions humaines. Ces dernières font diminuer non seulement l'efficacité et l'objectivité des études mais également renferment les champs d'applications potentielles de la RMN pour les non-initiés. Par conséquent, le développement des méthodes computationnelles non supervisées se trouve nécessaire. Les travaux réalisés ici représentent des nouvelles approches dans le domaine de la métabolomique et de la biologie structurelle. Le défi ultime de la RMN métabolomique est l'identification complète de l'ensemble des molécules constituant les échantillons biologiques complexes. Cette étape est vitale pour toute interprétation biologique. Dans la première partie de cette thèse, une nouvelle méthode numérique a été développée pour analyser des spectres à deux dimensions HSQC et TOCSY afin d'identifier les métabolites. La performance de cette nouvelle méthode a été démontrée avec succès sur les données synthétiques et expérimentales. La RMN est une des principales techniques analytiques de la biologie structurale. Le processus conventionnel de détermination structurelle est bien établie avec souvent une attribution explicite des signaux. Dans la seconde partie de cette thèse, une nouvelle approche computationnelle est présentée. Cette nouvelle méthode permet de déterminer les structures RMN sans attributions explicites des signaux. Ces derniers proviennent de données spectrales tridimensionnelles TOCSY et NOESY. Les structures ont été résolues en appliquant cette nouvelle méthode aux données spectrales d'une protéine de 12kDa Nuclear Magnetic Resonance (NMR) has become one of the most powerful and versatile spectroscopic techniques in analytical chemistry with applications in many disciplines of scientific research. A downside of NMR is however the laborious data analysis workflow that involves many manual interventions. Interactive data analysis impedes not only on efficiency and objectivity, but also keeps many NMR application fields closed for non-experts. Thus, there is a high demand for the development of unsupervised computational methods. This thesis introduces such unattended approaches in the fields of metabonomics and structural biology. A foremost challenge to NMR metabolomics is the identification of all molecules present in complex metabolite mixtures that is vital for the subsequent biological interpretation. In this first part of the thesis, a novel numerical method is proposed for the analysis of two-dimensional HSQC and TOCSY spectra that yields automated metabolite identification. Proof-of principle was successfully obtained by evaluating performance characteristics on synthetic data, and on real-world applications of human urine samples, exhibiting high data complexity. NMR is one of the leading experimental techniques in structural biology. However the conventional process of structure elucidation is quite elaborated. In this second part of the thesis, a novel computational approach is presented to solve the problem of NMR structure determination without explicit resonance assignment based on three-dimensional TOCSY and NOESY spectra. Proof-of principle was successfully obtained by applying the method to an experimental data set of a 12-kilodalton medium- sized protein

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Power, Kristin Marie. "Raman and near infrared spectroscopic analysis of amniotic fluid : metabolomics of maternal and fetal health indicators." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=112345.

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This thesis presents quantitative tools for the metabolomic analysis of amniotic fluid (AF) using vibrational spectroscopy. A total of 300 AF samples were collected for this retrospective cohort study and both Raman and near infrared (NIR) spectra were measured. Spectral data was compressed using a Haar wavelet transform and stage-wise multilinear regression (MLR). Calibration models were calculated for glucose, lactate and uric acid concentrations in AF. Birth weight, gestational diabetes mellitus (GDM) and gestational age were classified with the resulting compressed Raman and NIR spectra, using a genetic algorithm (GA) and a cross-validation approach. Results show that both Raman and NIR spectra of AF were not able to estimate the concentrations of glucose, lactate or uric acid with high precision. However, metabolomic analysis of AF Raman and NIR spectra was capable of estimating the development of GDM, abnormal birth weights as well as gestational ages with sensitivities >75% and specificities >77%. In addition, Raman and NIR metabolomic profiles showed a statistical difference in patients delivering preterm. Of the two spectroscopic analyses studied, NIR spectroscopy of AF has the potential to become a robust and non-invasive diagnostic tool for maternal and fetal health.

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Bamba, Mamadou. "Development and application of novel computational intelligence techniques to the multivariate analysis of metabolomics biofluids datasets." Thesis, De Montfort University, 2017. http://hdl.handle.net/2086/16407.

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The present decades have witnessed major advances in the development and applications of Computational Intelligence Techniques (CITs), which are commonly associated with metabolomics and omics analyses related to diseases diagnosis. This includes, amongst others, research work performed on Niemann-Pick class 1 and 2 diseases (NPC1 and NPC2 respectively), the severest form of which may involve liver dysfunction. Some of the main reasons for the high frequency of CITs use in metabolomics studies are also related to the development of techniques to detect major discriminatory metabolite variables for the purpose of disease diagnosis and progression. Alongside this, is the major demanding requirement to further understand potential metabolic pathways involved in order to improve our understanding of the molecular mechanisms underlying NPC1 disease. NPC1 is a rare neurodegenerative disorder attributable to NPC1 gene function loss, which causes adverse fat storage at the lysosomal levels (Mathieson, 2013; Xu et al., 2010). However, plasma metabolite profiling can provide insights into disease diagnosis and prognosis, while providing a clear ‘picture’ of the underlying metabolites altered during disease processes, including their early stages. Currently, biomarker discovery appears as the most effective solution to employ regarding the monitoring of disease progression (Mathieson, 2013; Ruiz-Rodado et al., 2014). In the present thesis, the intelligent tri-modelling techniques (ITMTs) which are combination of CITs applied to the multivariate (MV) analysis of biofluid datasets is proposed. The ITMTs serves as a combination of the scalar visualisation algorithm (SVA) for data visualisation and high-dimensional data representation into bi or tri-dimensional spaces. The optimum super support vector machine (OSVM) is also employed for the MV classification of metabolic datasets. Moreover, principal component regression (PCR) was also employed for data iv probabilistic classification and regression purposes. This was followed by investigations of correlations between these biomolecular diseases features. Furthermore, the tri-ranking techniques (TRTs) was developed in order to establish a ranking between the NPC1 disease features, in addition to those available for the NPC1 liver dysfunction disease features in a mouse model system to determine their importance in the further development of these diseases. High-resolution proton nuclear magnetic resonance (1H NMR) is used as high-throughput multicomponent analytical technique to generate very large quantities of metabolic data, which hold essential and useful information regarding the metabolites analysed. Prior to the performance of MV metabolomics analysis, a robust data handling technique based on balancing the dataset, feature selection, and stratified cross-validation of datasets is involved. Furthermore, the intelligent task technology fit theory has been proposed here, enabling a swift, consistent and rational model development through threshold settings for model validations. Application of the intelligent tri-modelling techniques (ITMTs) using SVA, OSVM and PCR, combined with the tri-ranking techniques (TRTs) have allowed the discovery of major discriminatory variables for NPC1 disease. Hence, using the blood plasma dataset the scalar visualisation algorithm could diagnose NPC1 disease through the following potential biomarkers: hexacosanoate acid, (R)-3-hydroxybutyrate, L-fucose, lactate, 3-hydroxyisovalerate, Citrate, N-acetyl-4-O-acetylneuraminate, methionine, and glutamine. Additionally, the SVA strategy highlighted the following major biomarkers in the 1H NMR NPC liver dysfunction dataset, including glycogen, glutamate, glutamine, taurine, glycerophosphocholine, acetoacetate, taurine, myo-inositol, lactate, leucine, isoleucine, and alanine. However, the PCR approach established a significant correlation between biomarker features for NPC1 disease, in addition to the mouse model of NPC liver dysfunction progression. Moreover, the OSVM technique could clearly segregate between the two classes of patients/animals in both disease pathogenesis studies. This thesis presents the Intelligent Tri-Model Techniques (ITMTs), using 1H NMR-linked metabolite profiling, new biomarkers for NPC1 disease diagnosis, and the NPC1-based liver dysfunction were discovered; these biomarkers displayed very high-performance accuracies. v This may represent a major advance regarding the diagnosis of NPC1 disease and its pathological sequelae. Such biomarkers may serve as valuable assets for monitoring the effectiveness of appropriate treatments for this debilitating condition, for example miglustat.

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Bian, Fang. "Novel Aspects of Fatty Acid Oxidation Uncovered by the Combination of Mass Isotopomer Analysis and Metabolomics." Case Western Reserve University School of Graduate Studies / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=case1144955161.

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Banday, Viqar. "Metab-Immune analysis of the non-obese diabetic mouse." Doctoral thesis, Umeå universitet, Immunologi/immunkemi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-119444.

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Type 1A diabetes mellitus or T1D is a chronic disease characterized by T cell mediated destruction of the insulin producing β cells in the islets of Langerhans. The classical symptoms include high glucose levels in urine and blood, polyuria, and polydipsia. Complications associated with T1D include blindness, amputations, and end-stage renal disease, and premature death. The non-obese diabetic (NOD) mouse, first described in 1980, is widely used as a model organism for T1D. T1D disease in the NOD mouse shares a number of similarities to human T1D including dependence on genetic and environmental factors. More than 30 disease associated gene regions or loci (termed insulin dependent diabetes, or Idd, loci) have been associated with T1D development in NOD. For some of these Idds, the corresponding region in human has been linked to the development of T1D in human. T1D, both in humans and mice, is recognized as a T cell mediated disease. However, many studies have shown the importance of both the metabolome and the immune system in the pathogenesis of the disease. Appearance of autoantibodies in the serum of patients is the first sign of pathogenesis. However, molecular and cellular events precede the immune attack on the β-cell immunity. It has been shown that patients who developed T1D have an altered metabolome prior to the appearance of autoantibodies. Although much is known about the pathogenesis of T1D, the contribution of the environment/immune factors triggering the disease is still to be revealed. In the present study both metabolic and immune deviations observed in the NOD mouse was analyzed. Serum metabolome analysis of the NOD mouse revealed striking resemblance to the human metabolic profile, with many metabolites in the TCA cycle significantly different from the non-diabetic control B6 mice. In addition, an increased level of glutamic acid was of the most distinguishing metabolite. A detailed bioinformatics analysis revealed various genes/enzymes to be present in the Idd regions. Compared to B6 mice, many of the genes correlated to the metabolic pathways, showed single nucleotide polymorphism (SNP), which can eventually affect the functionality of the protein. A genetic analysis of the increased glutamic acid revealed several Idd regions to be involved in this phenotype. The regions mapped in the genetic analysis harbor important enzymes and transporters related to glutamic acid. In-vitro islet culture with glutamic acid led to increased beta cell death indicating a toxic role of glutamic acid specifically towards insulin producing beta cells. In the analysis of the immune system, B cells from NOD mice, which are known to express high levels of TACI, were stimulated with APRIL, a TACI ligand. This resulted in enhanced plasma cell differentiation accompanied with increased class switching and IgG production. NOD mice have previously been shown to react vigorously to T-dependent antigens upon immunization. In this study we confirmed this as NOD mice showed an enhanced and prolonged immune response to hen egg lysozyme. Thus, serum IgG levels were significantly increased in the NOD mice and were predominantly of the IgG1 subtype. Immunofluorescence analysis revealed increased number of germinal centers in the NOD mice. Transfer of purified B and T cells from NOD to an immune deficient mouse could reproduce the original phenotype as seen in the NOD mice. Collectively, this thesis has analyzed the metabolomics and immune deviations observed in the NOD mice.

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Pinto, Joana Isabel Monteiro. "Healthy pregnancy and prenatal disorders followed by blood plasma metabolomics." Doctoral thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/14784.

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Doutoramento em Bioquímica The work presented in this thesis aimed to investigate the impact of healthy pregnancy and selected prenatal disorders on the metabolome and lipidome of maternal blood plasma, in order to define new potential biomarkers for non-invasive prediction and diagnosis. Chapter 1 describes the present status and challenges of the clinically relevant prenatal disorders, along with a presentation of the metabolomics strategy applied and the state of the art of metabolomics in prenatal research. All experimental details are described in Chapter 2, comprising sample metadata, sample collection and preparation, data acquisition protocols and data analysis procedures. The plasma metabolome and lipidome viewed by 1D and 2D NMR experiments are presented in Chapter 3. In this chapter, the use of Multiple Quantum NMR spectroscopy was explored, for the first time, for assignment of complex lipid mixtures. Chapter 4 contributes to filling in some existing gaps regarding human plasma degradability during handling and storage, as well as the importance of fasting conditions at collection. The use of heparin collection tubes resulted in no interference of the polysaccharide and full conservation of spectral information, while EDTA tubes produced a number of interfering signals from free and Ca2+/Mg2+ complexed EDTA, the impact of which on metabolomic analysis is discussed. Regarding temperature stability, large changes in lipoproteins and choline compounds were observed in plasma kept at room temperature for  2.5 hours, whereas short-term storage at -20ºC was found suitable up to 7 days, with storage at -80ºC being recommended, particularly for long-term periods (at least up to 2.5 years). Regarding freeze-thaw cycles, no more than 3 consecutive cycles were found advisable, while the use of non-fasting conditions (instead of fasting) was found acceptable. Chapter 5 presents the first NMR metabolomics study of maternal plasma throughout pregnancy, including correlation between plasma and urine metabolites. Some of the metabolic alterations observed confirmed known metabolic effects, while novel changes were observed, suggesting adjustments in energy and gut microflora metabolisms (citrate, lactate and dimethyl sulfone) and alterations in glomerular filtration rate (creatine and creatinine). Correlations studies unveiled specific lipoprotein/protein metabolic aspects of healthy pregnancy with impact on the excreted metabolome, providing further understanding of pregnancy metabolism. In Chapter 6, the impact of prenatal disorders on maternal plasma metabolome and lipidome is described for fetal chromosomal disorders (CD), including Trisomy 21 (T21). High classification rates were obtained for CD (88-89%) and T21 (85-92%) in 1st and 2nd trimesters, based on variable selection of NMR data. In addition, novel metabolic deviations were found through plasma/urine correlations, namely in low density and very low density lipoproteins (LDL+VLDL), sugar and gut microflora metabolisms. Changes in plasma phospholipid profile, namely in phosphatidylcholines, were further confirmed and characterised by hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-LC/MS). In Chapter 7, metabolic biomarkers of pre- and post-diagnosis GDM were sought by NMR metabolomics of whole maternal plasma and plasma lipid profile in the 2nd trimester. Metabolic alterations found to be predictive of GDM comprised increases in cholesterol, fatty acids, triglycerides and small metabolites changes in glucose, amino acids, betaine, urea, creatine and metabolites related with gut microflora. Post-diagnosis GDM was successfully classified using a 26-resonance plasma biomarker corresponding to 10 metabolites and lipids, advancing the possibility of using a multi-metabolite biomarker as a complementary tool in the clinical management of GDM. Chapter 8 describes the results obtained for prenatal disorders shown to have lower impact on maternal plasma metabolome, namely diagnosed fetal malformations and pre-diagnosis premature rupture of membranes, preterm delivery and preeclampsia. Finally, Chapter 9 describes the general conclusions and future perspectives in the context of this thesis, highlighting how this work contributes with new knowledge on prenatal disease mechanisms and possible biomarkers for prenatal diagnosis and prediction methods. O trabalho apresentado nesta tese teve como principal objetivo investigar o impacto da gravidez saudável e algumas doenças pré-natais no metaboloma e lipidoma de plasma sanguíneo materno, com vista à definição de novos biomarcadores para a previsão e diagnóstico não invasivos daquelas doenças. O Capítulo 1 descreve a perspectiva atual e os desafios das doenças pré-natais mais relevantes, assim como a estratégia metabolómica e estado da arte na investigação pré-natal. Todos os detalhes experimentais do trabalho realizado estão descritos no Capítulo 2, incluindo as condições de amostragem, recolha e preparação das amostras, bem como os protocolos de aquisição e análise dos dados. No Capítulo 3 descreve-se o metaboloma e lipidoma de plasma detectados por RMN 1D e 2D. Neste capítulo, a utilização de espectroscopia de RMN de quantum-múltiplo foi explorada, pela primeira vez, para caracterização de misturas lipídicas complexas. O Capítulo 4 contribui para colmatar algumas falhas no conhecimento sobre a degradibilidade do plasma humano durante o manuseamento da amostra e armazenamento, e a importância de condições de colheita como o jejum. A utilização de tubos de colheita com heparina não mostrou interferência do polissacarídeo nos espectros conservando-se toda a informação espectral, enquanto que os tubos com EDTA deram origem a sinais interferentes provenientes do EDTA livre e complexado com Ca2+/Mg2+, cujo impacto na análise metabolómica é discutido. Relativamente à estabilidade do plasma à temperatura ambiente, foram observadas alterações nas lipoproteínas e compostos de colina a partir de 2.5 horas, enquanto que o armazenamento a -20ºC mostrou ser adequado até 7 dias, sendo o armazenamento a -80ºC aconselhado, particularmente para períodos de tempo longos (pelo menos até 2.5 anos). Relativamente aos ciclos de congelação-descongelação, não se aconselham mais de 3 ciclos consecutivos, enquanto que o efeito da colheita das amostras em não-jejum (em vez de jejum) foi considerado aceitável. O Capítulo 5 apresenta o primeiro estudo de metabolómica por RMN do plasma materno ao longo da gravidez, incluindo correlação entre plasma e urina. Algumas das alterações metabólicas observadas confirmaram efeitos metabólicos conhecidos, tendo outras sido observadas pela primeira vez sugerindo alterações no metabolismo energético, na microflora bacteriana (citrato, lactato e dimetil sulfona) e na taxa de filtração glomerular (creatina e creatinina). Os estudos de correlação revelaram aspetos metabólicos específicos das lipoproteínas/proteínas com impacto no metaboloma excretado. No Capítulo 6 descreve-se o impacto das doenças cromossómicas (CD), incluindo Trissomia 21 (T21) no metaboloma e lipidoma de plasma materno. Obtiveram-se elevadas taxas de classificação para CD (88-89%) e T21 (85-92%) no 1º e 2º trimestres baseadas na seleção de variáveis dos dados de RMN. A correlação de plasma e urina revelou novos desvios metabólicos, nomeadamente no metabolismo das lipoproteínas de baixa densidade e de muito baixa densidade (LDL+VLDL), dos açúcares e da microflora bacteriana. As alterações observadas no perfil de fosfolípidos do plasma, nomeadamente das fosfatidilcolinas, foram confirmadas e caracterizadas por cromatografia liquida hidrofílica acoplada a espetrometria de massa (HILIC-LC/MS). No Capítulo 7 apresentam-se os resultados obtidos na prospecção de biomarcadores metabólicos de diabetes mellitus gestacional (GDM) pré- e pós-diagnóstico por metabolómica de RMN de plasma materno do 2º trimestre. Observaram-se alterações metabólicas com poder de previsão de GDM, nomeadamente um aumento no colesterol, ácidos gordos, triglicerídeos e pequenas variações metabólicas na glucose, aminoácidos, betaína, ureia, creatina e metabolitos relacionados com a microflora bacteriana. O grupo de GDM pós-diagnóstico foi bem classificado utilizando como biomarcador um conjunto de 26 ressonâncias do espectro de plasma correspondendo a lípidos e 10 metabolitos de baixo peso molecular, sugerindo-se a possibilidade de usar este marcador conjunto na gestão clínica da GDM. O Capítulo 8 descreve os resultados obtidos para as doenças pré-natais que mostraram ter um menor impacto no metaboloma de plasma materno, nomeadamente as malformações fetais (FM), e os estados de pré-diagnóstico da rutura prematura das membranas (PROM), parto pré-termo (PTD) e pré-eclampsia. Finalmente, no Capítulo 9 são descritas as conclusões gerais e perspetivas futuras no contexto desta tese, realçando-se como este trabalho contribui para o novo conhecimento dos mecanismos das doenças pré-natais e possíveis biomarcadores para a sua previsão e diagnóstico.

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Bell, Madison. "Developing Statistical and Analytical Methods for Untargeted Analysis of Complex Environmental Matrices." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41626.

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The main objective of this thesis was to develop statistical and analytical methods for untargeted analyses of complex environmental matrices like soil and sediment. Untargeted analyses are notoriously difficult to perform in matrices like soil and sediment because of the complexity of organic matter composition within these matrices. This thesis aimed to (1) Develop and compare extraction methods for untargeted analyses of soil and sediment while also developing data handling and quality control protocols; (2) Investigate novel applications of untargeted analyses for environmental classification and monitoring; and (3) Investigate the experimental factors that can influence the organic matter composition of untargeted extractions. CHAPTER TWO is a literature review of metabolomics protocols, and these protocols were incorporated into a proposed workflow for performing untargeted analysis in oil, soil, and sediment. This thesis contains the first application of untargeted analysis to freshwater lake sediment organic matter (i.e. sedimentomics) in CHAPTER THREE, and this has implications for discovering new biomarkers for paleolimnology (APPENDIX ONE). I demonstrated successful extraction methods for both sedimentomics and soil metabolomics studies in CHAPTER THREE and CHAPTER FIVE, respectively, using the proposed workflow from CHAPTER TWO. I also applied sedimentomics to the classification of lake sediments using machine learning and geostatistics based on sediment organic matter compositions in CHAPTER FOUR; this was a novel application of sedimentomics that could have implications for ecosystem classifications and advance our knowledge of organic matter cycling in lake sediments. Lastly, in CHAPTER FIVE I determined microbial activity, extraction method, and soil type can all influence the composition of soil organic matter extracts in soil metabolomics experiments. I also developed novel quality controls and quantitative methods that can help control these influences in CHAPTER FIVE and APPENDIX THREE. APPENDIX TWO was written in collaboration with multiple researchers and is a review of all “omics” types of analyses that can be performed on soil or sediment, and how methods like the untargeted analysis of soil and sediment organic matter can be linked with metagenomics, metatranscriptomics, and metaproteomics for a comprehensive metaphenomics analysis of soil and sediment ecosystems. In CHAPTER SIX the conclusions and implications for each chapter and overall for this thesis are detailed and I describe future directions for the field. In the end the overall conclusions of this thesis were: 1) Quality controls are necessary for sedimentomics and soil metabolomics studies, 2) Sedimentomics is a valid technique to highlight changes in sediment organic matter, 3) Soil metabolomics and sedimentomics yield more information about carbon cycling than traditional measurements, and 4) Soil metabolomics organic matter extractions are more variable and require more quality controls.

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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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D'Souza, Arun. "PathCase^MAW: A Workbench for Metabolomic Analysis." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1222895452.

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Strigun, Alexander [Verfasser], and Elmar [Akademischer Betreuer] Heinzle. "Assessment of in vitro cardiotoxicity using metabolomics and 13C metabolic flux analysis / Alexander Strigun. Betreuer: Elmar Heinzle." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2012. http://d-nb.info/1052551424/34.

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Al-Zubaidi, Mohammed Abdulridha. "Human stem cell metabolomics : headspace volatile gas analysis as an indicator of self-renewal and differentiation status." Thesis, Keele University, 2018. http://eprints.keele.ac.uk/4371/.

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Self-renewal and an ability to differentiate are key hallmarks of stem cells. Pluripotent stem cells (PSC) have the capacity to differentiate into all cell types found within the three germ layers; ectoderm, endoderm, mesoderm. Multipotent stem cells including mesenchymal stem cells (MSC) customarily differentiate into cell types representative of one germ layer only. Metabolomics focusses on characterising the low molecular weight organic compounds which are by-products of protein-protein interactions and metabolic enzymatic processes; volatile organic compounds (VOCs) emitted from or consumed by cells can be correlated with metabolic status. Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) has been used to identify and discriminate between metabolites (VOCs) from both undifferentiated and differentiated stem cells in specific cell culture oxygen conditions (air (21% O2) and physiological oxygen (2% O2)). The suitability of SIFT-MS for detecting and identifying VOCs from hPSCs was evaluated with SIFT-MS spectral data analysed via OPLS-DA. hPSCs cultured in 2% O2 displayed a distinct metabolic profile to those cultured in 21% O2. Metabolite markers differing between culture conditions for undifferentiated hPSCs included ethanol and acetaldehyde. Inhibition of ethanol/acetaldehyde conversion enzymes revealed mechanistic control of ethanol and acetaldehyde levels linked to environmental oxygen. hPSC differentiation-linked differences in metabolic profiles included immediate reductions in acetaldehyde and DMS/ethanethiol levels upon onset of differentiation. For hMSCs, OPLS-DA score plots indicated that hMSCs cultured in a controlled, hermetic, workstation maintained at 2% O2 were distinct to those cultured in either 2% O2 or 21% O2. The VOC profile of hMSCs varied with oxygen condition and degree of differentiation during osteogenic differentiation. In summary, this thesis demonstrates that SIFT-MS can detect and discriminate VOC profiles in two distinct stem cell populations. These profiles are sensitive to oxygen and differentiation status and therefore provide a potential valuable tool for non-invasive explorations of stem cell biology.

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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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Emamzadeh, Yazdi Simin. "Metabolomic analysis on anti-HIV activity of selected Helichrysum species." Thesis, University of Pretoria, 2019. http://hdl.handle.net/2263/77900.

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Since the beginning of human civilization, medicinal plants have been used to treat a variety of infectious and non-infectious diseases. The therapeutic properties of phytochemicals have been recognized since ancient human history. The genus Helichrysum Mill. with its attractive flowers consist of an estimated 500‒600 species in the Asteraceae family. In South Africa and Namibia there are about 244‒250 species with tremendous morphological diversity. Several Helichrysum species are widely used by the indigenous population to treat various disorders such as wounds, infections, respiratory conditions, headaches, coughs, colds and fevers. Several of the Helichrysum species exhibit antiviral activity with the most relevant to this study being the discovery of anti-human immunodeficiency virus (anti-HIV) and anti-reverse transcriptase (anti-RT) activity of some species. Drug discovery and development, from the early stages of a promising compound to the final medication, is an intensive, expensive and incremental process. The ultimate goal is to identify a molecule with the desired effect in the human body and to establish its quality, safety and efficacy for treating patients. The ability to combine high-throughput analytical techniques like metabolomic and other experimental approaches with drug discovery will speed up the development of safer, more effective and better-targeted therapeutic agents. The rapidly emerging field of metabolomics and molecular docking analysis provides valuable information on drug activity, toxicity, customized drug treatments and can predict therapeutic outcomes. Extraction of the aerial parts of 32 Helichrysum species was done using polar [methanol (MeOH) 50%: distilled water (dH2O) 50%] and non-polar [hexane (Hex), dichloromethane (DCM) and acetone (Ace)] solvent systems. Anti-human immunodeficiency virus bioassays on the live HI virus revealed that polar extracts of H. mimetes and H. chrysargyrum at 2.5 μg/mL and 25 μg/mL, polar and non-polar extracts of H. infuscum at 25 μg/mL and polar and non-polar extracts of H. zeyheri, H. setosum, H. platypterum and H. kraussii at 2.5 and 25 μg/mL, had higher than 90% inhibitory activity. The polar extract of H. mimetes also exhibited reverse transcriptase (RT) inhibition as a possible indication of the mechanism of action. Proton nuclear magnetic resonance (1H NMR) spectra of the polar extracts exhibited the presence of aromatic compounds and carbohydrate moieties. Principal component analysis (PCA) of the polar extracts showed clustering related to the activity of the extracts with good predictability scores (Q2 > 0.5). However, orthogonal projections to latent structures discriminant analysis (OPLS-DA) predictability of the model was low based on the Q2 at approximately 0.25. Quinic acid (QA), isolated from H. mimetes showed promising anti-RT activity [50% inhibition concentration (IC50) = 53.82 μg/mL] which was comparable to the positive drug control, doxorubicin (IC50 = 40.31 μg/mL). The molecular docking study revealed the probable binding site and conformation of QA within cavity 4, with a docking score of -8.03. The docking score of doxorubicin within cavity 4 was -7.87. With this study, it was shown that metabolomic analysis as a tool to predict anti-HIV activity in Helichrysum species can be valuable to shorten the process. Moreover, the study of molecular docking revealed the mechanism action of quinic acid and doxorubicin against RT. Thesis (PhD)--University of Pretoria, 2019. Plant Production and Soil Science PhD Unrestricted

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Ohtani, Yuta. "Molecular breeding of functional spinaches rich in folate and betacyanin based on metabolome analysis." Kyoto University, 2020. http://hdl.handle.net/2433/253323.

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Kyoto University (京都大学) 0048 新制・課程博士 博士(農学) 甲第22487号 農博第2391号 新制||農||1076(附属図書館) 学位論文||R2||N5267(農学部図書室) 京都大学大学院農学研究科応用生命科学専攻 (主査)教授植田充美, 教授梅澤俊明, 教授栗原達夫 学位規則第4条第1項該当

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Haggarty, Jennifer. "Biofilm metabolomics : the development of mass spectrometry and chromatographic methodology for the analysis of dual-species pathogenic biofilms." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8616/.

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Polymicrobial diseases arise when multiple microorganisms colonize a host and form multi-species biofilms. Within polymicrobial communities bacteria, fungi, viruses and/or parasites directly and indirectly interact with one another in a multitude of ways. The composition and the interactions between organisms within polymicrobial biofilms govern disease severity and patient outcomes. Polymicrobial infections are of significant interest because of the escalating development of antimicrobial resistance and the increasing involvement polymicrobial biofilms in chronic and systemic infections. The Gram-positive bacteria Staphylococcus aureus and dimorphic fungi Candida albicans have been shown to coexist within the human host in polymicrobial biofilm communities which often result in increased disease severity and mortality. Both of these commensals are opportunistic human pathogens that cause a plethora of infections ranging from relatively non-lethal local infections to life-threatening systemic infections in immunocompromised individuals. S. aureus and C. albicans have been co-associated with a number of polymicrobial diseases including cystic fibrosis and polymicrobial sepsis. Furthermore, S. aureus and C. albicans dual-infections have been associated with increased virulence and antimicrobial resistance. Although an effort has been made to unravel the relationship between S. aureus and C. albicans, metabolomics offers a powerful analytical tool to gain a better understanding of the interactions between this bacteria and fungus. To gain a better understanding of these interactions novel methods must be developed to modulate biofilm growth. Metabolomics is intended to analyse the complete small molecule component of a biological system. Analytically, the diversity present in these compounds presents huge opportunities for improvement. The overall aim of this research was to develop novel metabolomics methods and to apply these methods to the analysis of a S. aureus/C. albicans dual species biofilm to aid in the understanding of the relationship between this bacteria and fungi. Characterisation of the S. aureus/C. albicans biofilm in comparison in to the mono-species was carried out using a number of techniques, including fluorescence microscopy, SEM imaging, qPCR and transcriptional analysis, which indicated that these two organisms interact with each other on a physical and molecular lever. Although the presence of C. albicans facilitates S. aureus biofilm formation in sera, the presence of the bacteria reduced the number of C. albicans within the dual-species biofilm compared to the fungal mono-species and caused ‘crinkled’ hyphae which suggested possible antagonistic behaviour towards the fungi. An untargeted liquid chromatography-mass spectrometry separation method was developed that effectively retained both polar and nonpolar compounds by serially coupling a reversed-phase liquid chromatography (RPLC) column to a hydrophilic interaction liquid chromatography (HILIC) column via a T-piece. Two independent pumps were incorporated into the system to allow independent gradient control of the two columns. The high dilution between the columns, achieved by the difference in flow rates, enabled the retention and separation of both polar and nonpolar standards and numerous polar and non-polar metabolites extracted from beer. Good peak shapes and retention time reproducibility was achieved across all compound classes analysed. Next, a targeted ion-chromatography mass-spectrometry method was developed for the analysis of central carbon metabolism intermediates, specifically those involved in glycolysis, the tricarboxylic acid (TCA) cycle and the Electron Transport Chain (ETC). A total mix of all of the energy metabolites standards analysed were able to be separated and detected using IC-MS, with the exception of DHAP, G3P, oxaloacetate, acetyl-CoA, succinyl-CoA, NAD and NADP. The method displayed good reproducibility and limits of detection. The complexity of the extracted biofilms proved challenging to the IC-MS. Sample variation and low intensities in some sample groups (particularly the S. aureus samples) resulted in lower detection than expected. The RPLC/HILIC method provided hundreds of metabolite detections, but suffered in comparison to the conventional HILIC method, likely due to far greater optimisation of the original technique, leading to the utilisation of the routine pHILIC method in place of the serially combined method. Untargeted metabolomics analysis highlighted significant changes in a number of metabolic pathways including purine, pyrimidine, methionine and cysteine metabolism between the S. aureus and C. albicans mono- species and the dual-species biofilms. The differences detected within individual pathways suggest a difference in behaviour when the microorganisms are cultured with one another. The dramatic downregulation of a large portion of essential metabolites within purine, pyrimidine, cysteine and methionine pathways is indicative of the bacteria struggling to proliferate and form strong biofilms in sera. Down-regulation of many of the pathways in the dual-species biofilm compared to the C. albicans mono-species biofilm suggests that the presence of S. aureus within the biofilm could be having an adverse effect on the C. albicans. The results and conclusions herein provide greater understanding of the clinically important interaction between S. aureus and C. albicans. Microscopic and molecular characterisation enabled visualisation of the dual-species biofilm. The development and application of metabolomics techniques highlighted changes in metabolism between the mono- and dual-species biofilms, indicating that the relationship between S. aureus & C. albicans may not be completely synergistic, as previously suggested. Although the metabolomics methods developed during this study performed well, with regards to the separation of simple standard mixes and the complex beer sample, were not suitable for biofilm analysis. Through continued sample preparation and chromatographic optimisation these novel methods could offer relatively simple alternatives to more time consuming and complex chromatographic procedures.

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50

Huang, Zhengyan. "Differential Abundance and Clustering Analysis with Empirical Bayes Shrinkage Estimation of Variance (DASEV) for Proteomics and Metabolomics Data." UKnowledge, 2019. https://uknowledge.uky.edu/epb_etds/24.

Full text

Abstract:

Mass spectrometry (MS) is widely used for proteomic and metabolomic profiling of biological samples. Data obtained by MS are often zero-inflated. Those zero values are called point mass values (PMVs). Zero values can be further grouped into biological PMVs and technical PMVs. The former type is caused by the absence of components and the latter type is caused by detection limit. There is no simple solution to separate those two types of PMVs. Mixture models were developed to separate the two types of zeros apart and to perform the differential abundance analysis. However, we notice that the mixture model can be unstable when the number of non-zero values is small. In this dissertation, we propose a new differential abundance (DA) analysis method, DASEV, which applies an empirical Bayes shrinkage estimation on variance. We hypothesized that performance on variance estimation could be more robust and thus enhance the accuracy of differential abundance analysis. Disregarding the issue the mixture models have, the method has shown promising strategies to separate two types of PMVs. We adapted the mixture distribution proposed in the original mixture model design and assumed that the variances for all components follow a certain distribution. We proposed to calculate the estimated variances by borrowing information from other components via applying the assumed distribution of variance, and then re-estimate other parameters using the estimated variances. We obtained better and more stable estimations on variance, means abundances, and proportions of biological PMVs, especially where the proportion of zeros is large. Therefore, the proposed method achieved obvious improvements in DA analysis. We also propose to extend the method for clustering analysis. To our knowledge, commonly used cluster methods for MS omics data are only K-means and Hierarchical. Both methods have their own limitations while being applied to the zero-inflated data. Model-based clustering methods are widely used by researchers for various data types including zero-inflated data. We propose to use the extension (DASEV.C) as a model-based cluster method. We compared the clustering performance of DASEV.C with K-means and Hierarchical. Under certain scenarios, the proposed method returned more accurate clusters than the standard methods. We also develop an R package dasev for the proposed methods presented in this dissertation. The major functions DASEV.DA and DASEV.C in this R package aim to implement the Bayes shrinkage estimation on variance then conduct the differential abundance and cluster analysis. We designed the functions to allow the flexibility for researchers to specify certain input options.

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