From its first use in the field of biochemistry, instrumental analysis offered a variety of invaluable tools for the comprehensive description of biological systems. Multi-selective methods that aim to cover as many endogenous compounds as possible in biological samples use different analytical platforms and include methods like gene expression profile and metabolite profile analysis. The enormous amount of data generated in application of profiling methods needs to be evaluated in a manner appropriate to the question under investigation. The new field of system biology rises to the challenge to develop strategies for collecting, processing, interpreting, and archiving this vast amount of data; to make those data available in form of databases, tools, models, and networks to the scientific community.<br><br> On the background of this development a multi-selective method for the determination of phytohormones was developed and optimised, complementing the profile analyses which are already in use (Chapter I). The general feasibility of a simultaneous analysis of plant metabolites and phytohormones in one sample set-up was tested by studies on the analytical robustness of the metabolite profiling protocol. The recovery of plant metabolites proved to be satisfactory robust against variations in the extraction protocol by using common extraction procedures for phytohormones; a joint extraction of metabolites and hormones from plant tissue seems practicable (Chapter II).<br><br> Quantification of compounds within the context of profiling methods requires particular scrutiny (Chapter II). In Chapter III, the potential of stable-isotope in vivo labelling as normalisation strategy for profiling data acquired with mass spectrometry is discussed. First promising results were obtained for a reproducible quantification by stable-isotope in vivo labelling, which was applied in metabolomic studies.<br><br> In-parallel application of metabolite and phytohormone analysis to seedlings of the model plant Arabidopsis thaliana exposed to sulfate limitation was used to investigate the relationship between the endogenous concentration of signal elements and the ‘metabolic phenotype’ of a plant. An automated evaluation strategy was developed to process data of compounds with diverse physiological nature, such as signal elements, genes and metabolites – all which act in vivo in a conditional, time-resolved manner (Chapter IV). Final data analysis focussed on conditionality of signal-metabolome interactions. / Die instrumentelle Analytik stellt mit ihrem unschätzbaren Methodenreichtum Analysenwerkzeuge zur Verfügung, die seit ihrem Einzug in die Biologie die Aufzeichnung immer komplexerer ‚Momentaufnahmen’ von biologischen Systemen ermöglichen. Konkret hervorzuheben sind dabei vor allem die sogenannten ‚Profilmethoden’. Die Anwendung von Profilmethoden zielt darauf ab, aus einer bestimmten Stoffklasse so viele zugehörige Komponenten wie nur möglich gleichzeitig zu erfassen. <br><br> Für die Auswertung derart komplexer Daten müssen nun auch entsprechende Auswertungsmethoden bereit gestellt werden. Das neu entstandene Fachgebiet der Systembiologie erarbeitet deshalb Strategien zum Sammeln, Auswerten und Archivieren komplexer Daten, um dieses gesammelte Wissen in Form von Datenbanken, Modellen und Netzwerken der allgemeinen Nutzung zugänglich zu machen.<br><br> Vor diesem Hintergrund wurde den vorhandenen Profilanalysen eine Methode zur Erfassung von Pflanzenhormonen hinzugefügt. Verschiedene Experimente bestätigten die Möglichkeit zur Kopplung von Pflanzenhormon- und Pflanzeninhaltsstoff(=metabolit)-Profilanalyse. In weiteren Untersuchungen wurde das Potential einer innovativen Standardisierungstechnologie für die mengenmässige Erfassung von Pflanzeninhaltsstoffen in biologischen Proben betrachtet (in vivo labelling mit stabilen Isotopen).<br><br> Hormon- und Metabolitprofilanalyse wurden dann parallel angewandt, um Wechselwirkungen zwischen der Konzentration von Signalkomponenten und der Ausprägung des Stoffwechsels in Keimlingen der Modellpflanze Arabidopsis thaliana zu untersuchen. Es wurde eine Prozessierungsmethode entwickelt, die es auf einfache Art und Weise erlaubt, Daten oder Komponenten verschiedenen Ursprungs wie Signalelemente, Gene und Metabolite, die in biologischen Systemen zeitlich versetzt aktiv oder verändert erscheinen, im Zusammenhang zu betrachten. Die abschließende Analyse aller Daten richtet sich auf die Abschätzung der Bedingtheit von Signal-Metabolismus Interaktionen.

The advent of large-scale and high-throughput technologies has recently caused a shift in focus in contemporary biology from decades of reductionism towards a more systemic view. Alongside the availability of genome sequences the exploration of organisms utilizing such approach should give rise to a more comprehensive understanding of complex systems. Domestication and intensive breeding of crop plants has led to a parallel narrowing of their genetic basis. The potential to improve crops by conventional breeding using elite cultivars is therefore rather limited and molecular technologies, such as marker assisted selection (MAS) are currently being exploited to re-introduce allelic variance from wild species. Molecular breeding strategies have mostly focused on the introduction of yield or resistance related traits to date. However given that medical research has highlighted the importance of crop compositional quality in the human diet this research field is rapidly becoming more important. Chemical composition of biological tissues can be efficiently assessed by metabolite profiling techniques, which allow the multivariate detection of metabolites of a given biological sample.<br><br> Here, a GC/MS metabolite profiling approach has been applied to investigate natural variation of tomatoes with respect to the chemical composition of their fruits. The establishment of a mass spectral and retention index (MSRI) library was a prerequisite for this work in order to establish a framework for the identification of metabolites from a complex mixture. As mass spectral and retention index information is highly important for the metabolomics community this library was made publicly available. Metabolite profiling of tomato wild species revealed large differences in the chemical composition, especially of amino and organic acids, as well as on the sugar composition and secondary metabolites. Intriguingly, the analysis of a set of <i>S. pennellii</i> introgression lines (IL) identified 889 quantitative trait loci of compositional quality and 326 yield-associated traits. These traits are characterized by increases/decreases not only of single metabolites but also of entire metabolic pathways, thus highlighting the potential of this approach in uncovering novel aspects of metabolic regulation. Finally the biosynthetic pathway of the phenylalanine-derived fruit volatiles phenylethanol and phenylacetaldehyde was elucidated via a combination of metabolic profiling of natural variation, stable isotope tracer experiments and reverse genetic experimentation. / Die Einführung von Hochdurchsatzmethoden zur Analyse von biologischen Systemen, sowie die umfangreiche Sequenzierung von Genomen haben zu einer Verlagerung der Forschung „im Detail“ zu einer ganzheitlicheren Betrachtungsweise auf Systemebene geführt. Aus einer jahrhundertlangen, intensiven Züchtung und Selektion von Nutzpflanzen resultierte gleichzeitig eine Abnahme der genetischen Varianz. Daraus resultierend sind Nutzpflanzen anfälliger gegenüber Stressfaktoren, wie Pathogenen, hohen Salzkonzentrationen oder Trockenheit, als ihre Wildarten. Das Potential konventioneller Züchtung scheint somit heute an seine Grenzen gekommen zu sein. Daher versucht man mittels moderner Molekulartechnik, wie zum Beispiel Marker-gestützte Selektion, Gene oder ganze Genombereiche von Wildarten mit hoher genetischer Variation in Nutzpflanzen einzukreuzen, vornehmlich mit dem Ziel einer Ertrags- bzw. Resistenzsteigerung. Neueste medizinische Studien belegen, dass die Ernährung eine wesentliche Rolle für die menschliche Gesundheit spielt. Besonders wichtig sind hierbei die gesundheitsfördernden Substanzen in pflanzlichen Nahrungsmitteln. Aus diesem Grund kommt der Erforschung der biochemischen Zusammensetzung von biologischen Proben eine immer größere Bedeutung zu. Diese Untersuchung kann elegant durch Metabolitenprofile, welche die multivariate Analyse komplexer biologischer Proben erlauben, durchgeführt werden.<br><br> In dieser Arbeit wurde zur Untersuchung der biochemischen Zusammensetzung von Tomatenwildarten und interspezifischen <i>S. pennellii</i> Tomatenintrogressionslinien (IL) eine GC/MS basierte Metabolitenanalyseplattform verwendet. Hierzu war es zunächst notwendig eine Massenspektrenbibliothek, zur Annotierung von Massenspektren und Retentionsindices von, in pflanzlichen Proben vorkommenden, Metaboliten anzulegen. Die Analyse der Tomatenwildarten ergab große Unterschiede gegenüber der Kulturtomate im Hinblick auf den Gehalt an Amino- und organischen Säuren, sowie der Zuckerzusammensetzung und den Gehalt an Sekundärmetaboliten. Die darauf folgende Analyse der ILs, von den jede ein genau definiertes genomisches Segment von <i>S. pennellii</i> beinhaltet, bestätigte diese enorme Variation mit 889 metabolischen und 326 ertragsassozierten-Veränderungen in den ILs. Die metabolischen Veränderungen zeichneten sich durch abnehmende bzw. steigende Gehalte von einzelnen Metaboliten, aber auch durch eine koordinierte Änderung aus. In dieser Arbeit wurde weiterhin der Biosyntheseweg der Volatilenstoffe Phenylethanol und Phenylacetaldehyd mit Hilfe einer IL untersucht. Hierbei konnten durch stabile Isotopenmarkierung und eines „reverse genetics“-Ansatzes Gene bzw. Enzyme identifiziert werden, die für die Dekarboxylierung des Eduktes Phenylalanin verantwortlich sind. Diese Arbeit beschreibt erstmals die umfassende Analyse von biochemischen Komponenten auf Genombasis in Tomatenintrogressionslinien und zeigt damit ein Werkzeug auf zur Identifizierung von qualitativen biochemischen Merkmalen in der modernen molekularen Züchtung.

There is an ever increasing need of new technologies for identification of molecular markers for early diagnosis of fatal diseases to allow efficient treatment. In addition, there is great value in finding patterns of metabolites, proteins or genes altered in relation to specific disease conditions to gain a deeper understanding of the underlying mechanisms of disease development. If successful, scientific achievements in this field could apart from early diagnosis lead to development of new drugs, treatments or preventions for many serious diseases.  Metabolites are low molecular weight compounds involved in the chemical reactions taking place in the cells of living organisms to uphold life, i.e. metabolism. The research field of metabolomics investigates the relationship between metabolite alterations and biochemical mechanisms, e.g. disease processes. To understand these associations hundreds of metabolites present in a sample are quantified using sensitive bioanalytical techniques. In this way a unique chemical fingerprint is obtained for each sample, providing an instant picture of the current state of the studied system. This fingerprint or picture can then be utilized for the discovery of biomarkers or biomarker patterns of biological and clinical relevance. In this thesis the focus is set on evaluation and application of strategies for studying metabolic alterations in human tissues associated with disease. A chemometric methodology for processing and modeling of gas chromatography-mass spectrometry (GC-MS) based metabolomics data, is designed for developing predictive systems for generation of representative data, validation and result verification, diagnosis and screening of large sample sets. The developed strategies were specifically applied for identification of metabolite markers and metabolic pathways associated with prostate cancer disease progression. The long-term goal was to detect new sensitive diagnostic/prognostic markers, which ultimately could be used to differentiate between indolent and aggressive tumors at diagnosis and thus aid in the development of personalized treatments. Our main finding so far is the detection of high levels of cholesterol in prostate cancer bone metastases. This in combination with previously presented results suggests cholesterol as a potentially interesting therapeutic target for advanced prostate cancer. Furthermore we detected metabolic alterations in plasma associated with metastasis development. These results were further explored in prospective samples attempting to verify some of the identified metabolites as potential prognostic markers.

Lumnitzera littorea and Lumnitzera racemosa are mangrove species distributed widely along the Indonesian coasts. Besides their ecological importance, both are of interest owing to their wealth of natural products, some of which constitute potential sources for medicinal applications. We aimed to discover and characterize new anti-infective compounds, based on population-level sampling of both species from across the Indonesian Archipelago. Root metabolites were investigated by TLC, hyphenated LC-MS/MS and isolation, the internal transcribed spacer (ITS) region of rDNA was used for genetic characterization. Phytochemical characterization of both species revealed an unusual diversity in sulfated constituents with 3,3’,4’-tri-O-methyl-ellagic acid 4-sulfate representing the major compound in most samples. None of these compounds was previously reported for mangroves. Chemophenetic comparison of L. racemosa populations from different localities provided evolutionary information, as supported by molecular phylogenetic evidence. Samples of both species from particular locations exhibited anti-bacterial potential (Southern Nias Island and East Java against Gram-negative bacteria, Halmahera and Ternate Island against Gram-positive bacteria). In conclusion, Lumnitzera roots from natural mangrove stands represent a promising source for sulfated ellagic acid derivatives and further sulfur containing plant metabolites with potential human health benefits.

Capillary electrophoresis mass spectrometry (CE-MS) is a versatile instrumental method for metabolomics, which allows for comprehensive metabolite profiling of volume-limited biological specimens in order to better understand the molecular mechanisms associated with chronic diseases, including an alarming epidemic of obesity worldwide. Multiplexed CE separations enable high-throughput metabolite screening with quality assurance to prevent false discoveries when combined with rigorous method validation, robust experimental designs, complementary statistical methods, and high-resolution tandem mass spectrometry (MS/MS) for unknown metabolite identification. In this thesis, multiplexed CE-MS technology is applied for both targeted and untargeted metabolite profiling of various biological fluids, including covalently bound thiol-protein conjugates, as well as free circulating metabolites in serum and plasma, and excreted/bio-transformed compounds in urine due to complex host-gut microflora co-metabolism. This work was applied to characterize aberrant metabolic responses of obese subjects in response to dietary challenges, and measure the benefits of dietary interventions that reduce adiposity without deleterious muscle loss. Chapter 2 presents, a simple, sensitive yet robust analytical protocol to expand metabolome coverage in CE-MS for the discovery of labile protein thiols in human plasma using a rapid chemical derivatization method based on N-tert-butylmaleimide (NTBM). Chapter 3 describes targeted metabolite profiling of serum and plasma to investigate the differential metabolic responses between healthy and unhealthy obese individuals before and after consumption of a standardized high-caloric meal, respectively. Chapter 4 of this thesis describes an untargeted metabolite profiling strategy for urine using multisegment-injection (MSI)-CE-MS for elucidating the effects of protein supplementation following a short-term dietary weight-loss intervention study. This work revealed six urinary metabolites that were classified as top-ranking treatment response biomarkers useful for discriminating between subjects consuming carbohydrate (control), soy, and whey supplemented diets. In summary, this thesis demonstrated the successful implementation of multiplexed CE-MS technology for biomarker discovery in nutritional-based metabolomic studies as required for more effective treatment and prevention of obesity for innovations in public health. / Thesis / Doctor of Philosophy (PhD)

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.