Notch signaling is important for development in Caenorhabditis elegans and the REF-1 family pro-teins, a set of the bHLH transcription factors, are the first targets of Notch signaling. Little is known about the molecular mechanisms employed by the REF-1 family to regulate development. In this project, I iden-tified novel targets of three REF-1 family proteins, HLH-25/HLH-28/HLH-29, and determined which target genes are activated and which are repressed by the REF-1 proteins. These targets were identified by gene expression microarray and were functionally categorized by Gene Oncology analysis. A systems biology approach was performed to identify networks associated with those targets. In addition to the mo-lecular genetics studies, I identified and better characterized the range of phenotypes induced by muta-tions in ref-1 family genes.

Technological and methodological advances in the fields of medical and life-sciences have, over the last 25 years, revolutionised the way in which cellular activity is measured at the molecular level. Three such advances have provided a means of accurately and rapidly quantifying mRNA, from the development of quantitative Polymerase Chain Reaction (qPCR), to DNA microarrays, and second-generation RNA-sequencing (RNA-seq). Despite consistent improvements in measurement precision and sample throughput, the data generated continue to be a ffected by high levels of variability due to the use of biologically distinct experimental subjects, practical restrictions necessitating the use of small sample sizes, and technical noise introduced during frequently complex sample preparation and analysis procedures. A series of experiments were performed during this project to pro le sources of technical noise in each of these three techniques, with the aim of using the information to produce more accurate and more reliable results. The mechanisms for the introduction of confounding noise in these experiments are highly unpredictable. The variance structure of a qPCR experiment, for example, depends on the particular tissue-type and gene under assessment while expression data obtained by microarray can be greatly influenced by the day on which each array was processed and scanned. RNA-seq, on the other hand, produces data that appear very consistent in terms of differences between technical replicates, however there exist large differences when results are compared against those reported by microarray, which require careful interpretation. It is demonstrated in this thesis that by quantifying some of the major sources of noise in an experiment and utilising compensation mechanisms, either pre- or post-hoc, researchers are better equipped to perform experiments that are more robust, more accurate, and more consistent.

Charles University in Prague Faculty of Pharmacy in Hradec Králové Department of Pharmacology and toxicology Student: Lucie Rozkydalová Supervisor of Diploma thesis: Prof. PharmDr. František Štaud, PhD. Specialized supervisor: Pr. Pierre Cuq PharmD. PhD., Laure-Anaïs Vincent Title of diploma thesis: Differential resistance of melanoma to vinca-alkaloids Malignant melanoma (MM) represents the most dangerous and very aggressive skin tumor with fast development of drug resistance which is the main obstacle in successful treatment of MM. According to previous studies (microarray data analysis), KIT gene, which plays key role in melanoma pathophysiology, was chosen as one of the potential causes of failure of treatment by vinca alkaloids (VAs) because of its complete underexpression in melanoma CAL1 resistant cells (CAL1R-VAs) in comparison with parental cells (CAL1-wt). Moreover, KIT also interacted with NF-κB and cyclin D1-2 proteins involved in chemoresistance of melanoma - inside molecular network built using IPA software. Although KIT underexpression in resistant CAL1 R-VAs cell lines were confirmed (qRTPCR), KIT repression using specific siRNA transfection did not show any effect on in vitro sensibility of CAL1-wt cells to VAs. It signifies that KIT is not directly involved in melanoma resistance...

As the volume of transcriptomic data continues to increase, so too does its potential to deepen our understanding of disease; for example, by revealing gene expression patterns shared between diseases. However, key questions remain around the strength of the transcriptomic signal of disease and the identification of meaningful commonalities between datasets, which are addressed in this thesis as follows. The first chapter, Concordance of Microarray Studies of Parkinson's Disease, examines the agreement between differential expression signatures across 33 studies of Parkinson's disease. Comparison of these studies, which cover a range of microarray platforms, tissues, and disease models, reveals a characteristic pattern of differential expression in the most highly-affected tissues in human patients. Using correlation and clustering analyses to measure the representativeness of different study designs to human disease, the work described acts as a guideline for the comparison of microarray studies in the following chapters. In the next chapter, Using Dysregulated Signalling Paths to Understand Disease, gene expression changes are linked on the human signalling network, enabling identification of network regions dysregulated in disease. Applying this method across a large dataset of 141 common and rare diseases identifies dysregulated processes shared between diverse conditions, which relate to known disease- and drug-sharing-relationships. The final chapter, Understanding and Predicting Disease Relationships Through Similarity Fusion, explores the integration of gene expression with other data types - in this case, ontological, phenotypic, literature co-occurrence, genetic, and drug data - to understand relationships between diseases. A similarity fusion approach is proposed to overcome the differences in data type properties between each space, resulting in the identification of novel disease relationships spanning multiple bioinformatic levels. The similarity of disease relationships between each data type is considered, revealing that relationships in differential expression space are distinct from those in other molecular and clinical spaces. In summary, the work described in this thesis sets out a framework for the comparative analysis of transcriptomic data in disease, including the integration of biological networks and other bioinformatic data types, in order to further our knowledge of diseases and the relationships between them.

Cancer is a serious threat to human health and is now one of major causes of death worldwide. However, the complexity of the cancer makes the development of new and specific diagnostic tools particularly challenging. A number of different strategies have been developed for biomarker discovery in cancer using microarray data. The problem that typically needs to be addressed is the scale of the data sets; we simply do not have (or are likely to obtain) sufficient data for classical machine learning approaches for biomarker discovery to be properly validated. Obtaining a biomarker that is specific to a particular cancer is also very challenging. The initial promise that was held out for gene microarray work for the development of cancer biomarkers has not yet yielded the hoped for breakthroughs. This work discusses the construction of a strategy for a systematic approach to biomarker discovery validation using lung cancer gene expression microarray data based around non-small cell cancer and in patients which either stayed disease free after surgery (a five year window) or in which the disease progressed and re-occurred. As a means of assisting the validation purposes we have therefore looked at new methodologies for using existing biological knowledge to support machine learning biomarker discovery techniques. We employ text mining strategy using previously published literature for correlating biological concepts to a given phenotype. Pathway driven approaches through the use of Web Services and workflows, enabled the large-scale dataset to be analysed systematically. The results showed that it was possible, at least using this specific data set, to clearly differentiate between progressive disease and disease free patients using a set of biomarkers implicated in neuroendocrine signaling. A validation of the biomarkers identified was attempted in three separately published data sets. This analysis showed that although there was support for some of our findings in one of these data sets, this appeared to be a function of the close similarity in experimental design followed rather than through specific of the analysis method developed.

Thesis (Ph. D.) -- University of Texas Southwestern Medical Center at Dallas, 2005. / Vita. Bibliography: 197-219.

Einleitung<br /> Säugetiere haben zwei verschiedene Arten von Fettgewebe: das weiße Fettgewebe, welches vorwiegend zur Lipidspeicherung dient, und das braune Fettgewebe, welches sich durch seine Fähigkeit zur zitterfreien Thermogenese auszeichnet. Weiße und braune Adipozyten sind beide mesodermalen Ursprungs. Die Mechanismen, die zur Entwicklung von Vorläuferzellen in den weißen oder braunen Fettzellphenotyp führen, sind jedoch unbekannt. Durch verschiedene experimentelle Ansätze konnte gezeigt werden, daß diese Adipocyten vermutlich durch die Differenzierung zweier Typen unterschiedlicher Vorläuferzellen entstehen: weiße und braune Preadipozyten. Von dieser Hypothese ausgehend, war das Ziel dieser Studie, die Genexpression weißer und brauner Preadipozyten auf Unterschiede systematisch zu analysieren.<br /> <br /> Methoden<br /> Die zu vergleichenden Zellen wurden aus primären Zellkulturen weißer und brauner Preadipozyten des dsungarischen Zwerghamsters gewonnen. „Representational Difference Analysis“ wurde angewandt, um potentiell unterschiedlich exprimierte Gene zu isolieren. Die daraus resultierenden cDNA Fragmente von Kandidatengenen wurden mit Hilfe der Microarraytechnik untersucht. Die Expression dieser Gene wurde in braunen und weißen Fettzellen in verschiedenen Differenzierungsstadien und in braunem und weißem Fettgewebe verglichen.<br /> <br /> Ergebnisse<br /> 12 Gene, die in braunen und weißen Preadipozyten unterschiedlich exprimiert werden, konnten identifiziert werden. Drei Komplement Faktoren und eine Fettsäuren Desaturase werden in weißen Preadipozyten höher exprimiert; drei Struktur Gene (Fibronectin, Metargidin und a Actinin 4), drei Gene verbunden mit transkriptioneller Regulation (Necdin, Vigilin und das „small nuclear ribonucleoprotein polypeptide A“) sowie zwei Gene unbekannter Funktion werden in braunen Preadipozyten höher exprimiert. Mittels Clusteranalyse (oder Gruppenanalyse) wurden die gesamten Genexpressionsdaten charakterisiert. Dabei konnten die Gene in 4 typischen Expressionsmuster aufgeteilt werden: in weißen Preadipozyten höher exprimierte Gene, in braunen Preadipozyten höher exprimierte Gene, während der Differenzierung herunter regulierte Gene und während der Differenzierung hoch regulierte Gene.<br /> <br /> Schlußfolgerungen<br /> In dieser Studie konnte gezeigt werden, daß weiße und braune Preadipozyten aufgrund der Expression verschiedener Gene unterschieden werden können. Es wurden mehrere Kandidatengene zur Bestimmung weißer und brauner Preadipozyten identifiziert. Außerdem geht aus den Genexpressionsdaten hervor, daß funktionell unterschiedliche Gruppen von Genen eine wichtige Rolle bei der Differenzierung von weißen und braunen Preadipozyten spielen könnten, wie z.B. Gene des Komplementsystems und der extrazellulären Matrix. / Introduction<br /> Mammals have two types of adipose tissue: the lipid storing white adipose tissue and the brown adipose tissue characterised by its capacity for non-shivering thermogenesis. White and brown adipocytes have the same origin in mesodermal stem cells. Yet nothing is known so far about the commitment of precursor cells to the white and brown adipose lineage. Several experimental approaches indicate that they originate from the differentiation of two distinct types of precursor cells, white and brown preadipocytes. Based on this hypothesis, the aim of this study was to analyse the gene expression of white and brown preadipocytes in a systematic approach. <br /> <br /> Experimental approach<br /> The white and brown preadipocytes to compare were obtained from primary cell cultures of preadipocytes from the Djungarian dwarf hamster. Representational difference analysis was used to isolate genes potentially differentially expressed between the two cell types. The thus obtained cDNA libraries were spotted on microarrays for a large scale gene expression analysis in cultured preadipocytes and adipocytes and in tissue samples.<br /> <br /> Results<br /> 4 genes with higher expression in white preadipocytes (3 members of the complement system and a fatty acid desaturase) and 8 with higher expression in brown preadipocytes were identified. From the latter 3 coded for structural proteins (fibronectin, metargidin and a actinin 4), 3 for proteins involved in transcriptional regulation (necdin, vigilin and the small nuclear ribonucleoprotein polypeptide A) and 2 are of unknown function. Cluster analysis was applied to the gene expression data in order to characterise them and led to the identification of four major typical expression profiles: genes up-regulated during differentiation, genes down-regulated during differentiation, genes higher expressed in white preadipocytes and genes higher expressed in brown preadipocytes.<br /> <br /> Conclusion<br /> This study shows that white and brown preadipocytes can be distinguished by different expression levels of several genes. These results draw attention to interesting candidate genes for the determination of white and brown preadipocytes (necdin, vigilin and others) and furthermore indicate that potential importance of several functional groups in the differentiation of white and brown preadipocytes, mainly the complement system and extracellular matrix.

Shiga Toxin produzierende Escherichia coli (STEC) sind wichtige Erreger von Lebensmittelinfektionen und gelten als Hauptverursacher für die Ausbildung einer hämorrhagischen Colitis und dem lebensbedrohlichen hämolytisch-urämischen Syndrom. Als Hauptvirulenzfaktor und wichtigstes Charakteristikum der STEC wird die Fähigkeit angesehen, Shiga Toxine (Stx) zu produzieren. Die genetische Information für deren Produktion ist im Genom lambdoider Prophagen kodiert. Eine Antibiotikatherapie bei STEC-assoziierten Infektionen wird sehr kontrovers diskutiert, da sowohl die Produktion der Stx als auch die Freisetzung der Bakteriophagen in vitro durch antibiotisch wirkende Substanzen stimuliert werden kann. Der enterohämorrhagische E. coli O157:H7 Stamm EDL933 beherbergt insgesamt 18 lambdoide Prophagen und prophagenähnliche Elemente, darunter den Stx1-kodierenden Phagen CP-933V und den Stx2-kodierenden Phagen BP-933W. Ziel der vorliegenden Arbeit war es, den Einfluss des Gyrasehemmers Norfloxacin auf das Gesamttranskriptom von EDL933 mit Hilfe der DNA-Microarraytechnologie und unter Verwendung des MWG E. coli O157 Arrays umfassend zu untersuchen und insbesondere die Expression der Prophagengene zu analysieren. Hierfür wurde der E. coli O157:H7 Stamm EDL933 mit 200 ng/ml Norfloxacin inkubiert, Gesamt-RNA isoliert und diese mittels reverser Transkription in cDNA synthetisiert, wobei ein Einbau von fluoreszenzmarkierten Nukleotiden erfolgte. Diese cDNA wurde mit den auf dem E. coli O157 Array befindlichen Oligonukleotiden hybridisiert. Die Auswertung der Fluoreszenzintensitäten ermöglicht eine Analyse der Genexpression. Infolge der Induktion von EDL933 mit 200 ng/ml Norfloxacin zeigten 118 Spots eine Hochregulation und 122 eine Deregulation von Genen an. Bei genauerer Betrachtung resultierte auf Grund der Inkubation mit Norfloxacin eine erhöhte Expression von 52 Genen der Stx-kodierenden Phagen CP-933V und BP-933W. Insbesondere erfolgte eine erhöhte Regulation von Genen der späten Region des BP-933W. Das stxA2-Gen wurde dabei im Vergleich zur nicht-induzierten Kultur 158-fach stärker exprimiert. Im Falle des Stx1-Phagen wiesen nur einige Gene der frühen Region eine gesteigerte Genaktivität auf. Auffallend war die Hochregulation einzelner Gene der nicht Stx-kodierenden Phagen. Gene des Primärstoffwechsels, u. a. Gene der Aminosäurebiosynthese, des Energiehaushaltes und der Zellteilung zeigten eine verminderte Genaktivität nach Induktion. Diese Ergebnisse weisen darauf hin, dass die verwendete Konzentration von Norfloxacin große Auswirkungen auf das Gesamttranskriptom des untersuchten Stammes haben und insbesondere die Genexpression von zehn im Genom des EDL933 befindlichen Prophagen erhöht wurde. Die durch die Microarrayversuche erhaltenen Expressionsraten wurden durch Quantifizierung der cDNA mittels RT Real-Time PCR Untersuchungen überprüft. Zusammenfassend ist festzustellen, dass Norfloxacin neben der antibiotischen Wirkung in der hier verwendeten geringen Konzentration multiple Effekte auf E. coli O157:H7 ausübt und die Auswirkungen in Zukunft noch detaillierter untersucht werden müssen. Die DNA-Microarraytechnologie und der kommerziell erhältliche E. coli O157 Array ermöglichen diese umfassenden Analyse des Transkriptionsprofils. Im Rahmen dieser Arbeit konnte diese Technologie für Untersuchungen der Genexpression von E. coli O157 etabliert und validiert werden. / Infection with Shiga toxin producing Escherichia coli (STEC) is a serious cause of bloody diarrhea and sporadic cases and outbreaks of food-related diseases such hemorrhagic colitis and the hemolytic uremic syndrome (HUS) worldwide. The use of antibiotics in therapy of STEC-associated diseases has been discussed controversially. Release of phage-encoded Shiga toxins is the major virulence factor of enterhemorrhagic Escherichia coli (EHEC). The genome of the EHEC strain E. coli O157:H7 EDL933 contains 18 prophages or prophages elements, including the Stx1- and Stx2-encoding phages CP-933V and BP-933W. Stx-production and Stx-prophage induction can be stimulated by certain antibiotics, e.g. mitomycinC or UV light. The aim of this study was to investigate the influence of a low concentration of the gyrase inhibitor norfloxacin on the whole transcriptom of E. coli O157:H7 strain EDL933 and particularly on the gene expression of prophages using the DNA-microarraytechnology and the commercial available MWG E. coli O157 Array. To determine this, E. coli O157:H7 cultures were incubated with 200 ng/ml norfloxacin. Total RNA was isolated and labelled with fluorescence dyes during reverse transcription. Following this, the labelled cDNA was hybridized with the commercial E. coli O157 Arrays and the fluorescence intensities were measured, analysed and evaluated with appropriate software. Results of this study have indicated that a low concentration of norfloxacin have profound effects on the trancriptome of E. coli O157:H7. Under the conditions applied (200 ng/ml norfloxacin) and an incubation time of 120 minutes, 118 spots indicated a transcriptional upregulation and 122 spots a transcriptional downregulation of E. coli O157:H7 genes present on the array. In detail, 85 spots could be ascribed to EDL933 phage genes. Fifty-two of them could be assigned to the Shiga toxin encoding phages CP-933V and BP-933W, the others belonged to the non-Stx encoding phages or prophages elements existing in the EDL933 genome. Conspicuous, genes present in the late region of the BP-933W prophage were induced most strongly, up to 158-fold in the case of stxA2. Only some genes present in the early region of the Stx1 encoding phage CP-933V were induced upon induction with norfloxacin. Notably, only some genes of the non-Stx phages of EDL933 appeared to be induced after induction. The additional upregulated genes were related to recombination and stress functions and to E. coli O157:H7 RIMD0509952 genes. The majority of downregulated genes belonging to primary metabolism, such as amino acid biosynthesis, cell division and energy metabolism. In conclusion, an induction of E. coli O157:H7 strain EDL933 with 200 ng/ml norfloxacin has profound effects on the transcriptome of E. coli O157:H7, in particular on the global gene expression of more then ten prophages. The DNA-microarray technology and especially the E. coli O157 Array offer a modern tool for analysis of transcription profiles of the serious pathogen EHEC O157 in response to stress, e.g. antibiotics. In the context of this work, the DNA-microarraytechnology could be established and validated to provide the opportunity for further studies about the global effects on the whole transcriptome of E. coli O157.

Children with high-risk SHH/TP53-mut and Group 3 medulloblastoma (MB) have a 5-year overall survival of only 40%. Innovative approaches to enhance survival while preventing adverse effects are urgently needed. We investigated an innovative therapy approach combining irradia- tion (RT), decitabine (DEC), and abacavir (ABC) in a patient-derived orthotopic SHH/TP53-mut and Group 3 MB mouse model. MB-bearing mice were treated with DEC, ABC and RT. Mouse survival, tumor growth (BLI, MRT) tumor histology (H/E), proliferation (Ki-67), and endothelial (CD31) staining were analyzed. Gene expression was examined by microarray and RT-PCR (Ki-67, VEGF, CD31, CD15, CD133, nestin, CD68, IBA). The RT/DEC/ABC therapy inhibited tumor growth and enhanced mouse survival. Ki-67 decreased in SHH/TP53-mut MBs after RT, DEC, RT/ABC, and RT/DEC/ABC therapy. CD31 was higher in SHH/TP53-mut compared to Group 3 MBs and decreased after RT/DEC/ABC. Microarray analyses showed a therapy-induced downregulation of cell cycle genes. By RT-PCR, no therapy-induced effect on stem cell fraction or immune cell inva- sion/activation could be shown. We showed for the first time that RT/DEC/ABC therapy improves survival of orthotopic SHH/TP53-mut and Group 3 MB-bearing mice without inducing adverse effects suggesting the potential for an adjuvant application of this multimodal therapy approach in the human clinic.

Gene expression can serve as a powerful predictor for disease progression and other phenotypes. Consequently, microarrays, which capture gene expression genome-wide, have been used widely over the past two decades to derive biomarker signatures for tasks such as cancer grading, prognosticating the formation of metastases, survival, and others. Each of these signatures was selected and optimized for a very specific phenotype, tissue type, and experimental set-up. While all of these differences may naturally contribute to very heterogeneous and different biomarker signatures, all cancers share characteristics regardless of particular cell types or tissue as summarized in the hallmarks of cancer. These commonalities could give rise to biomarker signatures, which perform well across different phenotypes, cell and tissue types. Here, we explore this possibility by employing a network-based approach for pan-cancer biomarker discovery. We implement a random surfer model, which integrates interaction, expression, and phenotypic information to rank genes by their suitability for outcome prediction. To evaluate our approach, we assembled 105 high-quality microarray datasets sampled from around 13,000 patients and covering 13 cancer types. We applied our approach (NetRank) to each dataset and aggregated individual signatures into one compact signature of 50 genes. This signature stands out for two reasons. First, in contrast to other signatures of the 105 datasets, it is performant across nearly all cancer types and phenotypes. Second, It is interpretable, as the majority of genes are linked to the hallmarks of cancer in general and proliferation specifically. Many of the identified genes are cancer drivers with a known mutation burden linked to cancer. Overall, our work demonstrates the power of network-based approaches to compose robust, compact, and universal biomarker signatures for cancer outcome prediction.