Stable isotope ratios of carbon (δ¹³C) and nitrogen (δ¹⁵N) in human tissues, which reflect the stable isotope composition of the diet, offer numerous applications in the field of nutrition. One of the biggest contributors to uncertainty in stable isotope dietary reconstruction applications is potential variability in the isotopic composition of foods. No prior studies have evaluated the existing food carbon and nitrogen stable isotope data in an effort to determine broad-scale patterns and characterize the degree of variability of stable isotopes within the American diet. The purpose of this investigation was to improve our understanding of the isotopic composition of the modern American food supply by 1.) Determining geographically representative means and inter-sample variability of animal foods 2.) Assessing the impact of cooking on food stable isotope composition.To define the range of δ¹³C and δ¹⁵N values of animal proteins within the American food supply, we analyzed nationally-collected milk, fish, and shellfish samples from the USDA’s National Nutrient Database for Standard Reference and compared these to previously published data from the international literature. USDA milk samples were characterized by low overall variability, although regional variations in δ¹³C values were present. In contrast, seafood samples exhibited high overall variability but were consistent throughout the domestic and international samples. No variations in δ¹³C or δ¹⁵N values were detected throughout the baking or fermentation process in yeast buns or cookies. The representative values determined in this study can be used as a foundation for interpreting the stable isotope composition of the American diet. / Master of Science

Rapid advances in high-throughput molecular profiling techniques enabled large-scale genomics, transcriptomics, and proteomics-based biomedical studies, generating an enormous amount of multi-omics data. Processing and summarizing multi-omics data, modeling interactions among biomolecules, and detecting condition-specific dysregulation using multi-omics data are some of the most important yet challenging analytics tasks. In the case of detecting somatic DNA copy number aberrations using bulk tumor samples in cancer research, normal cell contamination becomes one significant confounding factor that weakens the power regardless of whichever methods used for detection. To address this problem, we propose a computational approach – BACOM 2.0 to more accurately estimate normal cell fraction and accordingly reconstruct DNA copy number signals in cancer cells. Specifically, by introducing allele-specific absolute normalization, BACOM 2.0 can accurately detect deletion types and aneuploidy in cancer cells directly from DNA copy number data. Genes work through complex networks to support cellular processes. Dysregulated genes can cause structural changes in biological networks, also known as network rewiring. Genes with a large number of rewired edges are more likely to be associated with functional alteration leading phenotype transitions, and hence are potential biomarkers in diseases such as cancers. Differential dependency network (DDN) method was proposed to detect such network rewiring and biomarkers. However, the existing DDN method and software tool has two major drawbacks. Firstly, in imbalanced sample groups, DDN suffers from systematic bias and produces false positive differential dependencies. Secondly, the computational time of the block coordinate descent algorithm in DDN increases rapidly with the number of involved samples and molecular entities. To address the imbalanced sample group problem, we propose a sample-scale-wide normalized formulation to correct systematic bias and design a simulation study for testing the performance. To address high computational complexity, we propose several strategies to accelerate DDN learning, including two reformulated algorithms for block-wise coefficient updating in the DDN optimization problem. Specifically, one strategy on discarding predictors and one strategy on accelerating parallel computing. More importantly, experimental results show that new DDN learning speed with combined accelerating strategies is hundreds of times faster than that of the original method on medium-sized data. We applied the DDN method on several biomedical datasets of omics data and detected significant phenotype-specific network rewiring. With a random-graph-based detection strategy, we discovered the hub node defined biomarkers that helped to generate or validate several novel scientific hypotheses in collaborative research projects. For example, the hub genes detected by the DDN methods in proteomics data from artery samples are significantly enriched in the citric acid cycle pathway that plays a critical role in the development of atherosclerosis. To detect intra-omics and inter-omics network rewirings, we propose a method called multiDDN that uses a multi-layer signaling model to integrate multi-omics data. We adapt the block coordinate descent algorithm to solve the multiDDN optimization problem with accelerating strategies. The simulation study shows that, compared with the DDN method on single omics, the multiDDN method has considerable advantage on higher accuracy of detecting network rewiring. We applied the multiDDN method on the real multi-omics data from CPTAC ovarian cancer dataset, and detected multiple hub genes associated with histone protein deacetylation and were previously reported in independent ovarian cancer data analysis. / Doctor of Philosophy / We witnessed the start of the human genome project decades ago and stepped into the era of omics since then. Omics are comprehensive approaches for analyzing genome-wide biomolecular profiles. The rapid development of high-throughput technologies enables us to produce an enormous amount of omics data such as genomics, transcriptomics, and proteomics data, which makes researchers swim in a sea of omics information that once never imagined. Yet, the era of omics brings new challenges to us: to process the huge volumes of data, to summarize the data, to reveal the interactions between entities, to link various types of omics data, and to discover mechanisms hidden behind omics data. In processing omics data, one factor that weakens the strengths of follow up data analysis is sample impurity. We call impure tumor samples contaminated by normal cells as heterogeneous samples. The genomic signals measured from heterogeneous samples are a mixture of signals from both tumor cells and normal cells. To correct the mixed signals and get true signals from pure tumor cells, we propose a computational approach called BACOM 2.0 to estimate normal cell fraction and corrected genomics signals accordingly. By introducing a novel normalization method that identifies the neutral component in mixed signals of genomic copy number data, BACOM 2.0 could accurately detect genes' deletion types and abnormal chromosome numbers in tumor cells. In cells, genes connect to other genes and form complex biological networks to perform their functions. Dysregulated genes can cause structural change in biological networks, also known as network rewiring. In a biological network with network rewiring events, a large quantity of network rewiring linking to a single hub gene suggests concentrated gene dysregulation. This hub gene has more impact on the network and hence is more likely to associate with the functional change of the network, which ultimately leads to abnormal phenotypes such as cancer diseases. Therefore, the hub genes linked with network rewiring are potential indicators of disease status or known as biomarkers. Differential dependency network (DDN) method was proposed to detect network rewiring events and biomarkers from omics data. However, the DDN method still has a few drawbacks. Firstly, for two groups of data with unequal sample sizes, DDN consistently detects false targets of network rewiring. The permutation test, which uses the same method on randomly shuffled samples is supposed to distinguish the true targets from random effects, however, is also suffered from the same reason and could let pass those false targets. We propose a new formulation that corrects the mistakes brought by unequal group size and design a simulation study to test the new formulation's correctness. Secondly, the time used for computing in solving DDN problems is unbearably long when processing omics data with a large number of samples scale or a large number of genes. We propose several strategies to increase DDN's computation speed, including three redesigned formulas for efficiently updating the results, one rule to preselect predictor variables, and one accelerating skill of utilizing multiple CPU cores simultaneously. In the timing test, the DDN method with increased computing speed is much faster than the original method. To detect network rewirings within the same omics data or between different types of omics, we propose a method called multiDDN that uses an integrated model to process multiple types of omics data. We solve the new problem by adapting the block coordinate descending algorithm. The test on simulated data shows multiDDN is better than single omics DDN. We applied DDN or multiDDN method on several datasets of omics data and detected significant network rewiring associated with diseases. We detected hub nodes from the network rewiring events. These hub genes as potential biomarkers help us to ask new meaningful questions in related researches.

Yes / Circulating tumour cells (CTCs) have significant diagnostic potential as they can reflect both the presence and recurrence of a wide range of cancers. However, this potential continues to be limited by the lack of robust and accessible isolation technologies. An alternative to isolation might be their direct detection amongst other peripheral blood cells, although this would require markers that allow them to be distinguished from an exceptionally high background signal. This review assesses the potential role of HOX genes, a family of homeodomain containing transcription factors with key roles in both embryonic development and oncogenesis, as unique and possibly disease specific markers of CTCs.

Yes / The HOX and PBX gene families encode transcription factors that have key roles in establishing the identity of cells and tissues in early development. Over the last 20 years it has become apparent that they are also dysregulated in a wide range of solid and haematological malignancies and have a predominantly pro-oncogenic function. A key mode of transcriptional regulation by HOX and PBX proteins is through their interaction as a heterodimer or larger complex that enhances their binding affinity and specificity for DNA, and there is growing evidence that this interaction is a potential therapeutic target in malignancies that include prostate, breast, renal, ovarian and lung cancer, melanoma, myeloma, and acute myeloid leukaemia. This review summarizes the roles of HOX and PBX genes in cancer and assesses the therapeutic potential of HOX/PBX dimer inhibition, including the availability of biomarkers for its application in precision medicine.

Sepsis ist ein häufiges und akut lebensbedrohliches Syndrom, das eine Organfunktionsstörung in Folge einer dysregulierten Immunantwort auf eine Infektion beschreibt. Eine frühzeitige Diagnosestellung und Therapieeinleitung sind von zentraler Bedeutung für das Überleben der Patient:innen. In einer Pilotstudie konnte unsere Forschungsgruppe mittels Durchflusszytometrie eine ausgeprägte Hyporeaktivität der Thrombozyten bei Sepsis nachweisen, die einen potenziell neuen Biomarker zur Sepsis-Früherkennung darstellt. Zur Evaluation des Ausmaßes und Entstehungszeitpunktes der detektierten Thrombozytenfunktionsstörung wurden im Rahmen der vorliegenden Arbeit zusätzlich zu Patient:innen mit Sepsis (SOFA-Score ≥ 2; n=13) auch hospitalisierte Patient:innen mit einer Infektion ohne Sepsis (SOFA-Score < 2; n=12) rekrutiert. Beide Kohorten wurden zu zwei Zeitpunkten (t1: <24h; t2: Tag 5-7) im Krankheitsverlauf mittels Durchflusszytometrie und PFA-200 untersucht und mit einer gesunden Kontrollgruppe (n=28) verglichen. Phänotypische Auffälligkeiten der Thrombozyten bei Sepsis umfassten: (i) eine veränderte Expression verschiedener Untereinheiten des GPIb-IX-V-Rezeptorkomplexes, die auf ein verstärktes Rezeptor-Shedding hindeutet; (ii) ein ausgeprägtes Mepacrin-Beladungsdefizit, das auf eine zunehmend reduzierte Anzahl von δ-Granula entlang des Infektion-Sepsis Kontinuums hinweist; (iii) eine Reduktion endständig gebundener Sialinsäure im Sinne einer verstärkten Desialylierung. Die funktionelle Analyse der Thrombozyten bei Sepsis ergab bei durchflusszytometrischer Messung der Integrin αIIbβ3-Aktivierung (PAC-1-Bindung) eine ausgeprägte generalisierte Hyporeaktivität gegenüber multiplen Agonisten, die abgeschwächt bereits bei Infektion nachweisbar war und gemäß ROC-Analysen gut zwischen Infektion und Sepsis diskriminierte (AUC >0.80 für alle Agonisten). Im Gegensatz dazu zeigten Thrombozyten bei Sepsis und Analyse mittels PFA-200 unter Einfluss physiologischer Scherkräfte eine normale bis gar beschleunigte Aggregation. Die Reaktivitätsmessung von Thrombozyten mittels Durchflusszytometrie stellt weiterhin einen vielversprechenden Biomarker für die Sepsis-Früherkennung dar. Für weitere Schlussfolgerungen ist jedoch eine größere Kohorte erforderlich. In nachfolgenden Untersuchungen sollten zudem mechanistische Ursachen der beschriebenen phänotypischen und funktionellen Auffälligkeiten von Thrombozyten bei Infektion und Sepsis z.B. mittels Koinkubationsexperimenten untersucht werden. / Sepsis is a frequent and life-threatening condition that describes organ dysfunction resulting from a dysregulated host immune response to infection. Early diagnosis and treatment are essential to improve patient survival. In a previous pilot study with sepsis patients, our research identified a severe platelet hyporeactivity using flow cytometry which could become a potential new biomarker for early sepsis diagnosis. To evaluate onset and extend of the detected platelet dysfunction in this study, we extended our patient cohort in addition to sepsis (SOFA-score ≥2; n=13) also to hospitalized patients with infection without sepsis (SOFA-score <2; n=12). Both cohorts were assessed at two time points during the disease (t1: <24h; t2: day 5-7) by flow cytometry and PFA-200 and compared with a healthy control group (n=28). Platelet phenotypic abnormalities during sepsis included: (i) altered expression of subunits of the GPIb-IX-V receptor complex, pointing to increased receptor shedding; (ii) a severe mepacrine loading deficit, indicating an increasingly reduced number of δ-granules along the infection-sepsis continuum; (iii) a reduction of terminally bound sialic acid, suggesting increased desialylation. Functional analysis of platelets in sepsis revealed a marked and generalized hyporeactivity toward multiple agonists when integrin αIIbβ3 activation (PAC-1 binding) was measured by flow cytometry, which was already to a lesser extend present in patients with infection and discriminated well between infection and sepsis according to ROC analysis (AUC >0.80 for all agonists). In contrast, platelets from septic patients showed normal to even accelerated aggregation when measured under flow condition and physiological shear forces by PFA-200. Analysis of platelet reactivity by flow cytometry remains a promising biomarker for early sepsis detection, but a larger cohort is needed for further conclusions. In subsequent studies, mechanistic causes of the described alterations in platelet phenotype and function during infection and sepsis should be investigated, e.g. by means of co-incubation experiments.

The Tibetan Plateau is the largest elevated landmass in the world and profoundly influences atmospheric circulation patterns such as the Asian monsoon system. Therefore this area has been increasingly in focus of palaeoenvironmental studies. This thesis evaluates the applicability of organic biomarkers for palaeolimnological purposes on the Tibetan Plateau with a focus on aquatic macrophyte-derived biomarkers. Submerged aquatic macrophytes have to be considered to significantly influence the sediment organic matter due to their high abundance in many Tibetan lakes. They can show highly 13C-enriched biomass because of their carbon metabolism and it is therefore crucial for the interpretation of δ13C values in sediment cores to understand to which extent aquatic macrophytes contribute to the isotopic signal of the sediments in Tibetan lakes and in which way variations can be explained in a palaeolimnological context. Additionally, the high abundance of macrophytes makes them interesting as potential recorders of lake water δD. Hydrogen isotope analysis of biomarkers is a rapidly evolving field to reconstruct past hydrological conditions and therefore of special relevance on the Tibetan Plateau due to the direct linkage between variations of monsoon intensity and changes in regional precipitation / evaporation balances. A set of surface sediment and aquatic macrophyte samples from the central and eastern Tibetan Plateau was analysed for composition as well as carbon and hydrogen isotopes of n-alkanes. It was shown how variable δ13C values of bulk organic matter and leaf lipids can be in submerged macrophytes even of a single species and how strongly these parameters are affected by them in corresponding sediments. The estimated contribution of the macrophytes by means of a binary isotopic model was calculated to be up to 60% (mean: 40%) to total organic carbon and up to 100% (mean: 66%) to mid-chain n-alkanes. Hydrogen isotopes of n-alkanes turned out to record δD of meteoric water of the summer precipitation. The apparent enrichment factor between water and n-alkanes was in range of previously reported ones (≈-130‰) at the most humid sites, but smaller (average: -86‰) at sites with a negative moisture budget. This indicates an influence of evaporation and evapotranspiration on δD of source water for aquatic and terrestrial plants. The offset between δD of mid- and long-chain n-alkanes was close to zero in most of the samples, suggesting that lake water as well as soil and leaf water are affected to a similar extent by those effects. To apply biomarkers in a palaeolimnological context, the aliphatic biomarker fraction of a sediment core from Lake Koucha (34.0° N; 97.2° E; eastern Tibetan Plateau) was analysed for concentrations, δ13C and δD values of compounds. Before ca. 8 cal ka BP, the lake was dominated by aquatic macrophyte-derived mid-chain n-alkanes, while after 6 cal ka BP high concentrations of a C20 highly branched isoprenoid compound indicate a predominance of phytoplankton. Those two principally different states of the lake were linked by a transition period with high abundances of microbial biomarkers. δ13C values were relatively constant for long-chain n-alkanes, while mid-chain n-alkanes showed variations between -23.5 to -12.6‰. Highest values were observed for the assumed period of maximum macrophyte growth during the late glacial and for the phytoplankton maximum during the middle and late Holocene. Therefore, the enriched values were interpreted to be caused by carbon limitation which in turn was induced by high macrophyte and primary productivity, respectively. Hydrogen isotope signatures of mid-chain n-alkanes have been shown to be able to track a previously deduced episode of reduced moisture availability between ca. 10 and 7 cal ka BP, indicated by a 20‰ shift towards higher δD values. Indications for cooler episodes at 6.0, 3.1 and 1.8 cal ka BP were gained from drops of biomarker concentrations, especially microbial-derived hopanoids, and from coincidental shifts towards lower δ13C values. Those episodes correspond well with cool events reported from other locations on the Tibetan Plateau as well as in the Northern Hemisphere. To conclude, the study of recent sediments and plants improved the understanding of factors affecting the composition and isotopic signatures of aliphatic biomarkers in sediments. Concentrations and isotopic signatures of the biomarkers in Lake Koucha could be interpreted in a palaeolimnological context and contribute to the knowledge about the history of the lake. Aquatic macrophyte-derived mid-chain n-alkanes were especially useful, due to their high abundance in many Tibetan Lakes and their ability to record major changes of lake productivity and palaeo-hydrological conditions. Therefore, they have the potential to contribute to a fuller understanding of past climate variability in this key region for atmospheric circulation systems. / Das tibetische Hochplateau ist die größte gehobene Landmasse der Erde und beeinflusst maßgeblich atmosphärische Zirkulationsmuster wie den Asiatischen Monsun. Um die Auswirkungen zukünftiger Schwankungen der Monsundynamik auf das regionale Klima besser einschätzen zu können, ist es wichtig, ein fundiertes Verständnis vergangener Klimaänderungen zu entwickeln. Daher ist das Tibetplateau in den letzten Jahren mehr und mehr in den Fokus paläoklimatischer Studien gerückt. Die große Anzahl an Seen in der Region bietet ein unerschöpfliches Klimaarchiv und viele Studien haben sich bereits mit Seesedimenten zur Klimarekonstruktion befasst. Dabei wurde in erster Linie auf biologische, sedimentologische und geochemische Parameter zurückgegriffen, wohingegen organische Biomarker bisher recht selten benutzt wurden. Die vorliegende Arbeit untersucht die Anwendbarkeit dieser potentiellen Klimaindikatoren auf dem Tibetplateau. Hierbei wurde ein Schwerpunkt auf die Analyse kutikularer Blattwachse von Wasserpflanzen gelegt, da diese wegen ihres starken Auftretens in tibetischen Seen einen erheblichen Beitrag zur organischen Substanz im Sediment leisten. Um den Einfluss von Wasserpflanzen auf das Sediment über einen weiten klimatischen Gradienten zu untersuchen, wurden Oberflächensedimente und Wasserpflanzen vom zentralen und östlichen Tibetplateau auf ihre Biomarkerzusammensetzung sowie auf ihre Kohlen- und Wasserstoffisotopensignatur untersucht. Dadurch wurde das Verständnis über beeinflussende Faktoren auf diese Parameter in Sedimenten vertieft. In einem Sedimentbohrkern des Koucha-Sees (östliches Tibetplateau) konnten diese Parameter dann im Hinblick auf Änderungen der Produktivität im See sowie der hydrologischen und klimatischen Bedingungen der letzten 15000 Jahre interpretiert werden. Es zeigte sich, dass der See bis 8000 Jahre vor Heute stark mit Wasserpflanzen bewachsen war, während die letzten 6000 Jahre Algen dominierten. Mit Hilfe von Wasserstoffisotopen wurden eine Zunahme des Monsuns und steigende Niederschläge zwischen 15000 und 10000 Jahren vor Heute sowie eine relativ trockene Periode zwischen 10000 und 7000 Jahren vor Heute rekonstruiert. Durch Kombination von Biomarkerkonzentrationen sowie deren Kohlenstoffisotopensignal wurden außerdem kurzzeitige Kälteperioden um ca. 6000, 3100 und 1800 Jahren vor Heute nachgewiesen, die vorher bereits in anderen Klimaarchiven in Tibet sowie auf der nördlichen Hemisphäre belegt wurden. Mit Hilfe von organischen Biomarkern konnte so ein detailliertes Bild über die Entwicklung des Koucha-Sees seit dem letzten Glazial gewonnen werden. Organische Biomarker haben sich somit als geeignet erwiesen, einen Beitrag zur Klimarekonstruktion auf dem Tibetplateau zu leisten.

Das seit 1957 als essentiell klassifizierte Spurenelement Selen vermittelt seine Funktion hauptsächlich durch seinen Einbau in Selenoproteine in Form der 21. proteinogenen Aminosäure Selenocystein. Insgesamt wurden 25 humane Gene für Selenoproteine identifiziert, deren genaue Funktion häufig noch nicht bekannt ist. Selen ist das einzige Mitglied aus der Gruppe der Mikronährstoffe, für das nach wie vor eine antikanzerogene Funktion vor allem in Bezug auf Darmkrebs postuliert wird. Die Grundlage dafür liefert eine Interventionsstudie, bei der 1.312 Probanden für 4,5 Jahre mit 200 μg Selen/Tag supplementiert wurden. Dies resultierte in einer Senkung der Gesamtkrebsmortalität um 50 %. Die Fragen einer optimalen Selenzufuhr, die nicht nur den Bedarf deckt, sondern auch die Entfaltung der antikanzerogenen Wirkung von Selen gewährleistet und die zugrunde liegenden molekularen Mechanismen sind noch ungeklärt. Zudem liegt die Selenzufuhr bei einem Großteil der europäischen Bevölkerung unter den Empfehlungen. Deshalb wurden in der vorliegenden Arbeit vier Wochen alte Mäuse für sechs Wochen marginal defizient (0,086 mg/kg Futter) bzw. selenadäquat (0,15 mg/kg Futter) gefüttert. Dieser geringe Unterschied im Selengehalt resultierte in einer Senkung des Plasmaselenspiegels der selenarmen Tiere auf 13 % und der GPx-Aktivität in der Leber auf 35 %. Zunächst wurde der Einfluss von Selen auf die globale Genexpression im murinen Colon mittels Microarray untersucht. Von den im Colon exprimierten Selenoproteinen reagierte die mRNA von SelW, SelH, GPx1 und SelM im Selenmangel besonders deutlich mit Expressionsverlust. Da diese Selenoproteine nicht nur im Colon, sondern auch in Leukozyten reguliert waren, sind sie auch als humane Biomarker für die in dieser Studie gewählte Schwankung des Selengehalts geeignet. Des Weiteren wurde auf Basis der Microarraydaten eine Signalweganalyse durchgeführt, die der Identifizierung krebsrelevanter Signalwege diente, um mögliche molekularbiologische Erklärungsansätze für die Rolle von Selen im Krebsgeschehen zu finden. Es zeigte sich, dass die mRNA von Schlüsselgenen des Wnt-Signalwegs wie β-Catenin, Gsk3β, Dvl2, Tle2, Lef1 und c-Myc auf Schwankungen des Selengehalts reagiert. Vor allem die Induktion von c-Myc, einem Zielgen des Wnt-Signalwegs, deutet darauf hin, dass dieser im Selenmangel tatsächlich aktiver ist als bei selenadäquater Versorgung. Ein weiterer möglicher Erklärungsansatz für die postulierte präventive Funktion von Selen gegenüber Darmkrebs ist die gastrointestinale Glutathionperoxidase (GPx2), die physiologisch in den proliferierenden Zellen des Kryptengrunds exprimiert wird. Die Regulation dieses Enzyms durch den Wnt-Signalweg, der ebenfalls in proliferierenden Zellen aktiv ist, konnte mittels Reportergenanalyse und endogen auf mRNA- und Proteinebene in Zellkultur gezeigt werden. Die Aktivierung verkürzter Promotorkonstrukte und die Mutation eines potentiellen Bindeelements identifizierten den für die Bindung von TCF und β-Catenin verantwortlichen Bereich. Als Zielgen des Wnt-Signalwegs scheint GPx2 zu den an Proliferationsprozessen beteiligten Genen zu gehören, was unter physiologischen Bedingungen die Aufrechterhaltung des intestinalen Epithels gewährleistet. Bei der Entstehung intestinaler Tumore, die in der Initiationsphase zu über 90 % mit einer konstitutiven Aktivierung des Wnt-Signalwegs einhergeht, wirkt GPx2 möglicherweise prokanzerogen. Die genaue Funktion von GPx2 während der Kanzerogenese bleibt weiter zu untersuchen. / Suboptimal selenium (Se) status has been suggested to be associated with a higher risk of developing various cancers, especially colon cancer. In mammals, Se exerts its functions through selenoproteins into which it is incorporated as selenocysteine. Since the function of many selenoproteins has not been identified the underlying mechanisms of the anti-carcinogenic function of Se remains unclear. Therefore, mice were fed either a marginal Se-deficient diet (0.086 mg Se/kg) or a Se-adequate diet (0.15 mg Se/kg) for six weeks. The plasma Se level was reduced to 13 % in the Se-deficient group while GPx activity in the liver was reduced to 35 %. The influence of Se on the global gene expression pattern was analysed using microarray technology. Among selenoproteins SelW, GPx1, SelH and SelM were consistently lower expressed in animals fed with the Se-deficient diet. As the mRNA of these genes was regulated in leucocytes as well, they are possible new biomarkers for the Se status in human studies. In addition, pathway analysis revealed that the cancer-relevant Wnt pathway was affected by the Se status, indicated by changes in the mRNA expression of key proteins like β-catenin, Gsk3β, Dvl2, Tle2, Lef1 and the Wnt target gene c-Myc. The regulation of these genes by Se points to a slightly increased basal activity level of the Wnt pathway in the Se poor state and may therefore contribute to the higher cancer risk in a marginal Se deficiency. Another possible explanation for anti-carcinogenic effects of Se is the gastrointestinal glutathione peroxidase GPx2, a selenoprotein predominantly expressed in proliferating cells at the crypt grounds of the intestine. The regulation of GPx2 via the Wnt pathway was confirmed by reporter gene experiments and by analysing endogenous GPx2 expression on the mRNA as well as on the protein level in different cell culture systems. Shortened promoter constructs and the mutation of a potential TCF binding element identified the area responsible for β-catenin/TCF binding. GPx2 is the first selenoprotein identified as a target of the Wnt pathway. This finding suggests a function of GPx2 in the maintenance of normal renewal of the intestinal epithelium as well as in cancer development.

Die Qualität von Nutzpflanzen ist von zahlreichen Einflussfaktoren wie beispielsweise Lagerbedingungen und Sorteneigenschaften abhängig. Um Qualitätsmängel zu minimieren und Absatzchancen von Nutzpflanzen zu steigern sind umfangreiche Analysen hinsichtlich ihrer stofflichen Zusammensetzung notwendig. Chromatographische Techniken gekoppelt an ein Massenspektrometer und die Kernspinresonanzspektroskopie wurden dafür bislang verwendet. In der vorliegenden Arbeit wurde ein Gaschromatograph an ein Flugzeitmassenspektrometer (GC-TOF-MS) gekoppelt, um physiologische Prozesse bzw. Eigenschaften (die Schwarzfleckigkeit, die Chipsbräunung, das Physiologische Alter und die Keimhemmung) von Nutzpflanzen aufzuklären. Als Pflanzenmodell wurde dafür die Kartoffelknolle verwendet. Dazu wurden neue analytische Lösungsansätze entwickelt, die eine zielgerichtete Auswertung einer Vielzahl von Proben, die Etablierung einer umfangreichen Referenzspektrenbibliothek und die sichere Archivierung aller experimentellen Daten umfassen. Das Verfahren der Probenvorbereitung wurde soweit modifiziert, dass gering konzentrierte Substanzen mittels GC-TOF-MS analysiert werden können. Dadurch wurde das durch die Probenvorbereitung limitierte Substanzspektrum erweitert. Anhand dieser Lösungsansätze wurden physiologisch relevante Stoffwechselprodukte identifiziert, welche indikativ (klassifizierend) bzw. prädiktiv (vorhersagend) für die physiologischen Prozesse sind. Für die Schwarzfleckigkeitsneigung und die Chipseignung wurde jeweils ein biochemisches Modell zur Vorhersage dieser Prozesse aufgestellt und auf eine Züchtungspopulation übertragen. Ferner wurden für die Schwarzfleckigkeit Stoffwechselprodukte des Respirationsstoffwechsels identifiziert sowie Aminosäuren, Glycerollipide und Phenylpropanoide für das Physiologische Alter als relevant erachtet. Das physiologische Altern konnte durch die Anwendung höherer Temperaturen beschleunigt werden. Durch Anwendung von Keimhemmern (Kümmelöl, Chlorpropham) wurde eine Verzögerung des physiologischen Alterns beobachtet. Die Applikation von Kümmelöl erwies sich dabei als besonders vorteilhaft. Kümmelöl behandelte Knollen wiesen im Vergleich zu unbehandelten Knollen nur Veränderungen im Aminosäure-, Zucker- und Sekundärstoffwechsel auf. Chlorpropham behandelte Knollen wiesen einen ähnlichen Stoffwechsel wie die unbehandelten Knollen auf. Für die bislang noch nicht identifizierten Stoffwechselprodukte wurden im Rahmen dieser Arbeit das Verfahren der „gezielten An-/Abreicherung“, der „gepaarten NMR/GC-TOF-MS Analyse“ und das „Entscheidungsbaumverfahren“ entwickelt. Diese ermöglichen eine Klassifizierung von GC-MS Signalen im Hinblick auf ihre chemische Funktionalität. Das Verfahren der gekoppelten NMR/GC-TOF-MS Analyse erwies sich dabei als besonders erfolgversprechend, da es eine Aufklärung bislang unbekannter gaschromatographischer Signale ermöglicht. In der vorliegenden Arbeit wurden neue Stoffwechselprodukte in der Kartoffelknolle identifiziert, wodurch ein wertvoller Beitrag zur Analytik der Metabolomik geleistet wurde. / Several factors influence the quality of crops. These include particular storage conditions and cultivar properties. Minimization of quality defects requires the employment of comprehensive metabolic analysis to enhance the marketing potential of crops. From this point of view chromatographic techniques coupled either with a mass spectrometer or the combination with nuclear magnetic resonance spectroscopy have been successfully applied to solve the main tasks. In the present work, a gas chromatograph was coupled to a time of flight mass spectrometer (GC-TOF-MS) to analyze physiological processes and attitudes of crops like black spot bruising, chips tanning, physiological aging, and sprouting inhibition. For this purpose the potato tuber was employed as a model plant. Therefore, new analytical approaches were developed comprising the targeted analysis of a multitude of samples, the establishment of a comprehensive mass spectral reference library and the built up of a secure archival storage system. Furthermore, the sample preparation protocol was modified to analyze trace components with the help of GC-TOF-MS as well. This helped to extend the discovery of more endogenous metabolites. These analytical approaches were required to identify physiological relevant indicative and predictive metabolites. Consequently, a biochemical model was build up for the process of black spot bruising and chips tanning respectively. These models could be applied to an unknown breeding progeny. Metabolites of the respiratory chain were identified as relevant for the process of black spot bruising whereas amino acids, lipids and phenylpropanoids were of high importance for the process of physiological aging.  The process of physiological aging could be accelerated while applying higher temperatures and could be delayed while applying sprouting inhibitors, like caraway oil and chlorpropham. Compared to chlorpropham, caraway oil exhibited more advantages with respect to storage attitudes although it caused significant changes in the amino acid, sugar and secondary metabolism during a common storage period. However, the chlorpropham treated tubers showed a similar phenotype in comparison to the control tubers. In addition, several methods were developed with respect to the classification of yet unidentified signals. These cover the decision tree process, the targeted enrichment and depletion of specific metabolites with the help of solid phase extraction and the paired NMR and GC-MS analyses. The paired NMR and GC-MS analysis appears very promising because it allows for the identification of unknown GC-MS signals. Thus, this work makes a valuable contribution to the analytics of the metabolome, as new metabolites could be identified which are of physiological relevance for the potato tuber.

In this thesis, we address the identification of biomarkers in high-dimensional omics data. The identification of valid biomarkers is especially relevant for personalized medicine that depends on accurate prediction rules. Moreover, biomarkers elucidate the provenance of disease, or molecular changes related to disease. From a statistical point of view the identification of biomarkers is best cast as variable selection. In particular, we refer to variables as the molecular attributes under investigation, e.g. genes, genetic variation, or metabolites; and we refer to observations as the specific samples whose attributes we investigate, e.g. patients and controls. Variable selection in high-dimensional omics data is a complicated challenge due to the characteristic structure of omics data. For one, omics data is high-dimensional, comprising cellular information in unprecedented details. Moreover, there is an intricate correlation structure among the variables due to e.g internal cellular regulation, or external, latent factors. Variable selection for uncorrelated data is well established. In contrast, there is no consensus on how to approach variable selection under correlation. Here, we introduce a multivariate framework for variable selection that explicitly accounts for the correlation among markers. In particular, we present two novel quantities for variable importance: the correlation-adjusted t (CAT) score for classification, and the correlation-adjusted (marginal) correlation (CAR) score for regression. The CAT score is defined as the Mahalanobis-decorrelated t-score vector, and the CAR score as the Mahalanobis-decorrelated correlation between the predictor variables and the outcome. We derive the CAT and CAR score from a predictive point of view in linear discriminant analysis and regression; both quantities assess the weight of a decorrelated and standardized variable on the prediction rule. Furthermore, we discuss properties of both scores and relations to established quantities. Above all, the CAT score decomposes Hotelling’s T 2 and the CAR score the proportion of variance explained. Notably, the decomposition of total variance into explained and unexplained variance in the linear model can be rewritten in terms of CAR scores. To render our approach applicable on high-dimensional omics data we devise an efficient algorithm for shrinkage estimates of the CAT and CAR score. Subsequently, we conduct extensive simulation studies to investigate the performance of our novel approaches in ranking and prediction under correlation. Here, CAT and CAR scores consistently improve over marginal approaches in terms of more true positives selected and a lower model error. Finally, we illustrate the application of CAT and CAR score on real omics data. In particular, we analyze genomics, transcriptomics, and metabolomics data. We ascertain that CAT and CAR score are competitive or outperform state of the art techniques in terms of true positives detected and prediction error.

Although human biomedical and physiological information is readily available, such information for great apes is limited. We analyzed clinical chemical biomarkers in serum samples from 277 wild- and captive-born great apes and from 312 healthy human volunteers as well as from 20 rhesus macaques. For each individual, we determined a maximum of 33 markers of heart, liver, kidney, thyroid and pancreas function, hemoglobin and lipid metabolism and one marker of inflammation. We identified biomarkers that show differences between humans and the great apes in their average level or activity. Using the rhesus macaques as an outgroup, we identified human-specific differences in the levels of bilirubin, cholinesterase and lactate dehydrogenase, and bonobo-specific differences in the level of apolipoprotein A-I. For the remaining twenty-nine biomarkers there was no evidence for lineage-specific differences. In fact, we find that many biomarkers show differences between individuals of the same species in different environments. Of the four lineagespecific biomarkers, only bilirubin showed no differences between wild- and captive-born great apes. We show that the major factor explaining the human-specific difference in bilirubin levels may be genetic. There are human-specific changes in the sequence of the promoter and the protein-coding sequence of uridine diphosphoglucuronosyltransferase 1 (UGT1A1), the enzyme that transforms bilirubin and toxic plant compounds into water-soluble, excretable metabolites. Experimental evidence that UGT1A1 is down-regulated in the human liver suggests that changes in the promoter may be responsible for the human-specific increase in bilirubin. We speculate that since cooking reduces toxic plant compounds, consumption of cooked foods, which is specific to humans, may have resulted in relaxed constraint on UGT1A1 which has in turn led to higher serum levels of bilirubin in humans.