<p>Excessive memory use may cause severe performance problems and system crashes. Without appropriate tools, it may be difficult or impossible to determine why a program is using too much memory. This applies even though Python provides automatic memory management --- garbage collection can help avoid many memory allocation bugs, but only to a certain extent due to the lack of information during program execution. There is still a need for tools helping the programmer to understand the memory behaviour of programs, especially in complicated situations. The primary motivation for Heapy is that there has been a lack of such tools for Python.</p><p>The main questions addressed by Heapy are how much memory is used by objects, what are the objects of most interest for optimization purposes, and why are objects kept in memory. Memory leaks are often of special interest and may be found by comparing snapshots of the heap population taken at different times. Memory profiles, using different kinds of classifiers that may include retainer information, can provide quick overviews revealing optimization possibilities not thought of beforehand. Reference patterns and shortest reference paths provide different perspectives of object access patterns to help explain why objects are kept in memory.</p>

<p>Cancer therapy operates by inflicting damage in malignant cells. The most lethal target is the genomic DNA. As a single double strand DNA break has the potential to kill the cell, mechanisms have evolved to detect and block propagation of the damage. Genes and their products function in a highly connected network-structure with ample cross-talk between different pathways. This interplay can be studied by genome-wide experiments, such as expression profiling. The aim of this thesis is to study the cellular effects of DNA damaging agents.</p><p>A theoretical framework is explored to improve understanding of expression profiling results. To analyse large datasets, computational methods were developed to model the data. Further, the response to DNA damage was investigated in different cellular systems. As late radiation toxicity is a severe limitation of radiotherapy of cancer patients, patients were enrolled in a study to search for a molecular signature to identify high-risk patients. Ex vivo irradiation of lymphocytes revealed a signature of functionally related gene sets that were capable to separate patients with regard to toxicity status. </p><p>The gene set analysis was also applied to a dataset where mouse embryonic stem cells had been exposed to various doses of cisplatin. At several time-points after administration of the drug, expression profiles were determined. In addition to the expected increase of genes related to apoptosis and cell cycle progression, damaged cells also seemed to have embarked upon a p53-dependent differentiation programme. Finally, in a study of cardiac rodent cells, the genotoxic treatment with irradiation was compared to the mechanical stress induced in heart tissue.</p><p>In conclusion, this thesis presents evidence for the advantage of using functionally related sets of genes in analysis and interpretation of genome-wide experiments. This strategy may improve clinical understanding of the effects of DNA damaging agents used for cancer therapeutics.</p>

<p>Type 2 diabetes mellitus (T2DM) is a heterogeneous disease affecting millions of people worldwide. Both genetic and environmental factors contribute to the pathogenesis. The disease is characterized by alterations in many genes and their products. Historically, genomic alterations have mainly been studied at the transcriptional level in diabetes research. However, transcriptional changes do not always lead to altered translation, which makes it important to measure changes at the protein level. Proteomic techniques offer the possibility of measuring multiple protein alterations simultaneously.</p><p>In this thesis, the proteomic technique surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS) has been applied and evaluated in the context of T2DM research. Protocols for pancreatic islet and serum/plasma protein profiling and identification have been developed. In addition, the technique was used to analyze the influence of genetic background versus diabetic environment by determining serum protein profiles of individuals with normal glucose tolerance (NGT) and T2DM with or without family history of diabetes. In total thirteen serum proteins displayed different levels in serum from persons with NGT versus patients with T2DM. Among these proteins, apolipoprotein CIII, albumin and one yet unidentified protein could be classified as being changed because of different genetic backgrounds. On the other hand, ten proteins for instance transthyretin, differed as a result of the diabetic environment.</p><p>When plasma protein patterns of NGT and T2DM individuals characterized by differences in early insulin responses (EIR) were compared, nine proteins were found to be varying between the two groups. Of these proteins five were identified, namely two forms of transthyretin, hemoglobin α-chain, hemoglobin β-chain and apolipoprotein H. However no individual protein alone could explain the differences in EIR. In conclusion, SELDI-TOF MS has been successfully used in the context of T2DM research to identify proteins associated with family history of diabetes and β-bell function. </p>

This study introduces a method for multiparallel analysis of small organic compounds in the unicellular green alga Chlamydomonas reinhardtii, one of the premier model organisms in cell biology. The comprehensive study of the changes of metabolite composition, or metabolomics, in response to environmental, genetic or developmental signals is an important complement of other functional genomic techniques in the effort to develop an understanding of how genes, proteins and metabolites are all integrated into a seamless and dynamic network to sustain cellular functions. The sample preparation protocol was optimized to quickly inactivate enzymatic activity, achieve maximum extraction capacity and process large sample quantities. As a result of the rapid sampling, extraction and analysis by gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF) more than 800 analytes from a single sample can be measured, of which over a 100 could be positively identified. As part of the analysis of GC-TOF raw data, aliquot ratio analysis to systematically remove artifact signals and tools for the use of principal component analysis (PCA) on metabolomic datasets are proposed. Cells subjected to nitrogen (N), phosphorus (P), sulfur (S) or iron (Fe) depleted growth conditions develop highly distinctive metabolite profiles with metabolites implicated in many different processes being affected in their concentration during adaptation to nutrient deprivation. Metabolite profiling allowed characterization of both specific and general responses to nutrient deprivation at the metabolite level. Modulation of the substrates for N-assimilation and the oxidative pentose phosphate pathway indicated a priority for maintaining the capability for immediate activation of N assimilation even under conditions of decreased metabolic activity and arrested growth, while the rise in 4-hydroxyproline in S deprived cells could be related to enhanced degradation of proteins of the cell wall. The adaptation to sulfur deficiency was analyzed with greater temporal resolution and responses of wild-type cells were compared with mutant cells deficient in SAC1, an important regulator of the sulfur deficiency response. Whereas concurrent metabolite depletion and accumulation occurs during adaptation to S deprivation in wild-type cells, the sac1 mutant strain is characterized by a massive incapability to sustain many processes that normally lead to transient or permanent accumulation of the levels of certain metabolites or recovery of metabolite levels after initial down-regulation. For most of the steps in arginine biosynthesis in Chlamydomonas mutants have been isolated that are deficient in the respective enzyme activities. Three strains deficient in the activities of N-acetylglutamate-5-phosphate reductase (arg1), N2 acetylornithine-aminotransferase (arg9), and argininosuccinate lyase (arg2), respectively, were analyzed with regard to activation of endogenous arginine biosynthesis after withdrawal of externally supplied arginine. Enzymatic blocks in the arginine biosynthetic pathway could be characterized by precursor accumulation, like the amassment of argininosuccinate in arg2 cells, and depletion of intermediates occurring downstream of the enzymatic block, e.g. N2-acetylornithine, ornithine, and argininosuccinate depletion in arg9 cells. The unexpected finding of substantial levels of the arginine pathway intermediates N-acetylornithine, citrulline, and argininosuccinate downstream the enzymatic block in arg1 cells provided an explanation for the residual growth capacity of these cells in the absence of external arginine sources. The presence of these compounds, together with the unusual accumulation of N-Acetylglutamate, the first intermediate that commits the glutamate backbone to ornithine and arginine biosynthesis, in arg1 cells suggests that alternative pathways, possibly involving the activity of ornithine aminotransferase, may be active when the default reaction sequence to produce ornithine via acetylation of glutamate is disabled. / Entwicklung und Anwendung von Methoden zur multiparallelen Analyse von Metaboliten in der einzelligen Grünalge Chlamydomonas reinhardtii, einem der wichtigsten Modellorganismen der Zellbiologie, sind Gegenstand dieser Arbeit. Metabolomanalyse, die umfassende Analyse von Veränderungen der Konzentrationen von Stoffwechselprodukten durch Umweltreize oder genetische und entwicklungsbedingte Signale, ist ein wichtiges Komplement anderer Genomanalysemethoden, um die Integration von Genen, Proteinen und Metaboliten in ein nahtloses und dynamisches Netzwerk zur Aufrechterhaltung der Lebensfunktionen eines Organismus zu verstehen. Die Methode wurde im Hinblick auf schnelle Inaktivierung enzymatischer Aktivität, Maximierung der Extraktionskapazität und Behandlung großer Probenmengen optimiert. Im Ergebnis der Probenaufarbeitung, Extraktion und Analyse mittels Gaschromatographie und Time-Of-Flight-Massenspektrometrie konnten mehr als 800 analytische Signale in Einzelproben dargestellt werden, von denen über 100 identifiziert werden konnten. Die Arbeit stellt methodische Innovationen zur systematischen Erkennung von Artefakten in GC-MS Chromatogrammen und Werkzeuge zur Anwendung der Hauptkomponentenanalyse auf Metabolom-Daten vor. Zellen unter Stickstoff- (N), Phosphor- (P), Schwefel- (S), oder Eisen- (Fe) Mangel zeigen deutliche Unterschiede in ihrer Metabolitenausstattung. Die Anpassung an die einzelnen Nährstoffmangelsituationen ist durch spezifische Änderungen einer Reihe von Metaboliten zentraler Prozesse des Primärstoffwechsels gekennzeichnet. Die Konzentrationsänderungen von Substraten für die Stickstoffassimilation und den oxidativen Pentosephosphatweg deuten darauf hin, dass die Fähigkeit zur schnellen Aktivierung der N-Assimilation auch unter Bedingungen herabgesetzter Stoffwechsel- und Wachstumsaktivität aufrechterhalten wird. Die Akkumulation von 4-Hydroxyprolin unter Schwefelmangel könnte im Zusammenhang stehen mit der Degradation von Proteinen der Chlamydomonas-Zellwand, deren wesentlicher Bestandteil hydroxyprolinreiche Glykoproteine sind und die unter Schwefelmangel aktiv umgebaut wird. Die Anpassung an Schwefelmangel wurde mit größerer zeitlicher Auflösung in Wildtyp-Zellen und Zellen des sac1-Stammes untersucht. SAC1 ist ein zentraler Regulator der Schwefelmangelantwort in Chlamydomonas. Zeitgleiche Ab- und Zunahme von Metaboliten ist ein charakteristisches Element der Anpassung an Schwefelmangel in Wildtypzellen. Die Reaktion von SAC1-Mutanten auf Schwefelmangel ist durch weit reichenden Verlust zur Steuerung von Prozessen gekennzeichnet, die normalerweise zur vorübergehenden oder dauerhaften Anreicherung bestimmter Metabolite führen. Die Verfügbarkeit von Chlamydomonas-Stämmen mit fehlender Enzymaktivität für fast jeden der Schritte der Argininbiosynthese eröffnet die Möglichkeit, das Potential der Metabolitenanalyse zur Untersuchung der Regulation der Aminosäurebiosynthese in photosynthetischen Eukaryoten zur Anwendung zu bringen. Drei Stämme, mit fehlender Aktivität für N-Acetylglutamat-5-phosphat Reduktase (arg1), N2 Acetylornithin-Aminotransferase (arg9) beziehungsweise Argininosuccinat Lyase (arg2) wurden in Bezug auf die Aktivierung ihrer endogenen Argininbiosynthese nach Entzug externer Argininquellen analysiert. Die einzelnen enzymatischen Blocks konnten durch Precursor-Anreicherung, wie die Anhäufung von Argininosuccinat in arg2-Zellen, und Erschöpfung von Intermediaten nachgelagerter Reaktionen, beispielsweise die deutliche Abnahme von N2-Acetylornithin, Ornithin und Argininosuccinat in arg9-Zellen charakterisiert werden. Das unerwartete Vorhandensein von zum Teil das Wildtyp-Niveau überschreitender Mengen von N2-Acetylornithin, Citrullin und Argininosuccinat, die Produkte bzw. Substrate dem enzymatischen Block nachgelagerter Reaktionen in arg1-Zellen sind, bot eine Erklärung für eine noch vorhandene Restkapazität zum Wachstum des arg1-Stamms auch ohne äußere Arginingabe. Der Nachweis dieser Verbindungen sowie die ungewöhnliche Anreicherung von N-Acetylglutamat, der ersten Verbindung, die das Glutamat-Gerüst für die Ornithin- und Argininsynthese bindet, in arg1-Zellen könnte auf alternative Reaktionen, möglicherweise unter Beteiligung von Ornithin-Aminotransferase, zur Synthese von Ornithin hindeuten, die in Erscheinung treten, wenn die Synthesekette nach Acetylierung von Glutamat blockiert ist.

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.

Renewable fibers produced by forest trees provide excellentraw material of high economic value for industrialapplications. Despite this, the genes and corresponding enzymesinvolved in wood fiber biosynthesis in trees are poorlycharacterized. This thesis describes a functional genomicsapproach for the identification of carbohydrate-active enzymesinvolved in secondary cell wall (wood) formation in hybridaspen. First, a 3' target amplification method was developed toenable microarray-based gene expression analysis on minuteamounts of RNA. The amplification method was evaluated usingboth a smaller microarray containing 192 cDNA clones and alarger microarray containing 2995 cDNA clones that werehybridized with targets isolated from xylem and phloem.Moreover, a gene expression study of phloem differentiation wasperformed to show the usefulness of the amplificationmethod. A microarray containing 2995 cDNA clones representing aunigene set of a cambial region EST library was used to studygene expression during wood formation. Transcript populationsfrom thin tissue sections representing different stages ofxylem development were hybridized onto the microarrays. It wasdemonstrated that genes encoding lignin and cellulosebiosynthetic enzymes, as well as a number of genes withoutassigned function, were differentially expressed across thedevelopmental gradient. Microarrays were also used to track changes in geneexpression in the developing xylem of transgenic, GA-20 oxidaseoverexpressing hybrid aspens that had increased secondarygrowth. The study revealed that a number of genes encoding cellwall related enzymes were upregulated in the transgenic trees.Moreover, most genes with high transcript changes could beassigned a role in the early events of xylogenesis. Ten genes encoding putative cellulose synthases (CesAs) wereidentified in our ownPopulusESTdatabase. Full length cDNA sequences wereobtained for five of them. Expression analyses performed withreal-time PCR and microarrays in normal wood undergoingxylogenesis and in tension wood revealed xylem specificexpression of four putative CesA isoenzymes. Finally, an approach combining expressionprofiling,bioinformatics as well as EST and full length sequencing wasadopted to identify secondary cell wall related genes encodingcarbohydrate-active enzymes, such as glycosyltransferases andglycoside hydrolases. As expected, glycosyltransferasesinvolved in the carbohydrate biosynthesis dominated thecollection of the secondary cell wall related enzymes that wereidentified. Key words:Populus, xylogenesis, secondary cell wall,cellulose, hemicellulose, microarrays, transcript profiling,carbohydrate-active enzyme, glycosyltransferase, glycosidehydrolase

The human being and other animals contain immensely complex biochemical processes that govern their function on a cellular level. It is estimated that several thousand small molecules (metabolites) are produced by various biochemical pathways in humans. Pathological processes can introduce perturbations in these biochemical pathways which can lead to changes in the amounts of some metabolites.Developments in analytical chemistry have made it possible measure a large number metabolites in a single blood sample, which gives a metabolic profile. In this thesis I have worked on establishing and understanding metabolic profiles from patients with rheumatoid arthritis (RA) and from animal models of the autoimmune diseases diabetes mellitus type 1 (T1D) and RA.Using multivariate statistical methods it is possible to identify differences between metabolic profiles of different groups. As an example we identified differences between patients with RA and healthy volunteers. This can be used to elucidate the biochemical processes that are active in a given pathological condition.Metabolite concentrations are affected by a many other things than the presence or absence of a disease. Both genomic and environmental factors are known to influence metabolic profiles. A main focus of my work has therefore been on finding strategies for ensuring that the results obtained when comparing metabolic profiles were valid and relevant. This strategy has included repetition of experiments and repeated measurement of individuals’ metabolic profiles in order to understand the sources of variation.Finding the most stable and reproducible metabolic effects has allowed us to better understand the biochemical processes seen in the metabolic profiles. This makes it possible to relate the metabolic profile differences to pathological processes and to genes and proteins involved in these.The hope is that metabolic profiling in the future can be an important tool for finding biomarkers useful for disease diagnosis, for identifying new targets for drug design and for mapping functional changes of genomic mutations. This has the potential to revolutionize our understanding of disease pathology and thus improving health care.

Today’s rapid development of powerful tools for geneexpression analysis provides unprecedented resources forelucidating complex molecular events. The objective of this workhas been to apply, combine andevaluate tools for analysis of differential gene expressionusing atherosclerosis as a model system. First, an optimisedsolid-phase protocol for representational difference analysis(RDA) was applied to twoin vitromodel systems. Initially, The RDA enrichmentprocedure was investigated by shotgun cloning and sequencing ofsuccessive difference products. In the subsequent steps,combinations of RDA and microarray analysis were used tocombine the selectivity and sensitivity of RDA with thehigh-throughput nature of microarrays. This was achieved byimmobilization of RDA clones onto microarrays dedicated forgene expression analysis in atherosclerosis as well ashybridisation of labelled RDA products onto global microarrayscontaining more than 32,000 human clones. Finally, RDA wasapplied for the investigation of the focal localisation ofatherosclerotic plaques in mice usingin vivotissue samples as starting material. A large number of differentially expressed clones wereisolated and confirmed by real time PCR. A very diverse rangeof gene fragments was identified in the RDA products especiallywhen they were screened with global microarrays. However, themicroarray data also seem to contain some noise which is ageneral problem using microarrays and should be compensated forby careful verification of the results. Quite a large number of candidate genes related to theatherosclerotic process were found by these studies. Inparticular several nuclear receptors with altered expression inresponse to oxidized LDL were identified and deserve furtherinvestigation. Extended functional annotation does not liewithin the scope of this thesis but raw data in the form ofnovel sequences and accession numbers of known sequences havebeen made publicly available in GenBank. Parts of the data arealso available for interactive exploration on-line through aninteractive software tool. The data generated thus constitute abase for new hypotheses to be tested in the field ofatherosclerosis. <b>Keywords:</b>representational difference analysis, geneexpression profiling, microarray analysis, atherosclerosis,foam cell formation

Cancer therapy operates by inflicting damage in malignant cells. The most lethal target is the genomic DNA. As a single double strand DNA break has the potential to kill the cell, mechanisms have evolved to detect and block propagation of the damage. Genes and their products function in a highly connected network-structure with ample cross-talk between different pathways. This interplay can be studied by genome-wide experiments, such as expression profiling. The aim of this thesis is to study the cellular effects of DNA damaging agents. A theoretical framework is explored to improve understanding of expression profiling results. To analyse large datasets, computational methods were developed to model the data. Further, the response to DNA damage was investigated in different cellular systems. As late radiation toxicity is a severe limitation of radiotherapy of cancer patients, patients were enrolled in a study to search for a molecular signature to identify high-risk patients. Ex vivo irradiation of lymphocytes revealed a signature of functionally related gene sets that were capable to separate patients with regard to toxicity status. The gene set analysis was also applied to a dataset where mouse embryonic stem cells had been exposed to various doses of cisplatin. At several time-points after administration of the drug, expression profiles were determined. In addition to the expected increase of genes related to apoptosis and cell cycle progression, damaged cells also seemed to have embarked upon a p53-dependent differentiation programme. Finally, in a study of cardiac rodent cells, the genotoxic treatment with irradiation was compared to the mechanical stress induced in heart tissue. In conclusion, this thesis presents evidence for the advantage of using functionally related sets of genes in analysis and interpretation of genome-wide experiments. This strategy may improve clinical understanding of the effects of DNA damaging agents used for cancer therapeutics.

Type 2 diabetes mellitus (T2DM) is a heterogeneous disease affecting millions of people worldwide. Both genetic and environmental factors contribute to the pathogenesis. The disease is characterized by alterations in many genes and their products. Historically, genomic alterations have mainly been studied at the transcriptional level in diabetes research. However, transcriptional changes do not always lead to altered translation, which makes it important to measure changes at the protein level. Proteomic techniques offer the possibility of measuring multiple protein alterations simultaneously. In this thesis, the proteomic technique surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS) has been applied and evaluated in the context of T2DM research. Protocols for pancreatic islet and serum/plasma protein profiling and identification have been developed. In addition, the technique was used to analyze the influence of genetic background versus diabetic environment by determining serum protein profiles of individuals with normal glucose tolerance (NGT) and T2DM with or without family history of diabetes. In total thirteen serum proteins displayed different levels in serum from persons with NGT versus patients with T2DM. Among these proteins, apolipoprotein CIII, albumin and one yet unidentified protein could be classified as being changed because of different genetic backgrounds. On the other hand, ten proteins for instance transthyretin, differed as a result of the diabetic environment. When plasma protein patterns of NGT and T2DM individuals characterized by differences in early insulin responses (EIR) were compared, nine proteins were found to be varying between the two groups. Of these proteins five were identified, namely two forms of transthyretin, hemoglobin α-chain, hemoglobin β-chain and apolipoprotein H. However no individual protein alone could explain the differences in EIR. In conclusion, SELDI-TOF MS has been successfully used in the context of T2DM research to identify proteins associated with family history of diabetes and β-bell function.