Regensburg, University, Diss., 2005.

Techn. University, Diss., 2003--Berlin.

University, Diss., 2005--Kiel.

Universiẗat, Diss., 2004--Kiel.

Techn. University, Diss., 2006--Darmstadt.

The experiments described in this thesis aimed to investigate the neurophysiological consequences, at the cortical level, of the neurodegenerative condition, amyotrophic lateral sclerosis (ALS). A principle tenet of this study was that ALS is, first and foremost, a disorder of the cortical motor system, the precise pathological mechanisms of which remain incompletely understood. Furthermore, the degree to which neurodegeneration can be evidenced before the onset of symptoms is thus far uncertain, and the optimal means by which to measure therapeutic response has yet to be determined. Chapter 1 introduces relevant key concepts in ALS and briefly summarises three studies completed in the early phases of pursuit for the above degree. These studies respectively considered presmyptomatic cellular ALS pathology, quantitation of disease progression and eyetracking assessment of cognitive dysfunction. Chapter 2 describes magnetoencephalography, the investigative technology utilised in the subsequent experimental chapter. In chapter 3, the effects of ALS on movement related modulation of neuronal oscillations are determined. An excessive peri-movement desynchronisation and delayed post-movement rebound was described. Functional connectivity between cortical regions at rest is appraised in chapter 4. ALS appeared to result in quite striking increases in functional connectivity, in keeping with the fMRI literature and in support of diminished intracortical inhibitory influences. The functional communication from the motor cortices is directly considered during active motor performance in chapter 5. ALS related reductions in beta-band coherence were noted in both corticospinal and inter- hemispheric communication. In conclusion, the results demonstrated considerable support for proposed excitotoxic disease mechanisms and were in alignment with reported findings in other neurodegenerative diseases. Finally, a pilot study by which the neural mechanisms for cognitive impairment in ALS are explored via antisaccade performance is described. While underpowered, the experimental design showed promise for future application.

Les tumeurs adipocytaires (TA) bénignes sont majoritairement constituées par les lipomes, alors que les TA malignes sont principalement des Tumeurs Lipomateuses Atypiques (TLA)/ liposarcome (LPS) bien différenciés (LBD) et les LPS dédifférenciés (LDD). Le gène HMGA2 (High Mobility Group A2) est remanié dans certains lipomes et amplifié dans les TLA/LBD et LDD. Ainsi, nous avons émis l’hypothèse que HMGA2 jouait un rôle fondamental dans la genèse des TA bénignes et malignes. En faveur de cette hypothèse, nous avons observé une surexpression constante de HMGA2 dans les TLA/LBD et LDD avec amplification de HMGA2 et les lipomes avec remaniement de HMGA2. Dans un cas de lipomatose, hypertrophie pathologique du tissu adipeux sans anomalie du gène HMGA2, une surexpression de HMGA2 était associée à une inhibition de l’expression de plusieurs microARN let-7. En revanche, nos travaux ne sont pas en faveur d’un rôle prépondérant des microARN let7 dans la surexpression de HMGA2 dans les TA. Nous nous sommes également intéressés aux gènes partenaires de fusion avec HMGA2 dans les lipomes et avons notamment identifié une nouvelle fusion impliquant PPAP2B (Phosphatidic Acid Phosphatase type 2B) localisé en 1p32. Nous avons aussi confirmé le rôle du gène NFIB (9p22) dans les lipomes. Enfin, nous avons établi des corrélations pronostiques dans une grande série de 116 TLA/LBD et LDD : l’amplification de HMGA2 était associée à l’histotype TLA/LBD et à une survie longue alors que les amplifications de CDK4 et JUN sont associées au type LDD et une survie courte. Ainsi, nos données confortent l’hypothèse d’un rôle précoce et majeur de HMGA2 dans la genèse des TA bien différenciées. / Benign adipocytic tumors (AT) are mainly represented by lipomas whereas most malignant AT are Atypical Lipomatous Tumors/Well-differentiated liposarcomas (ALT/WDLPS) and dedifferentiated liposarcomas (DDLPS). HMGA2 gene (High Mobility Group A2) is rearranged in some lipomas and amplified in ALT/WDLPS and DDLPS. Thus, we hypothesized that HMGA2 played a fundamental role in benign and malignant AT genesis. In favor of this hypothesis, we observed a constant overexpression of HMGA2 in amplified ALT/WDLPS and DDLPS, and in rearranged lipomas. In a case of lipomatosis, that is a pathological proliferation of the adipocytic tissu without rearrrangement of HMGA2, the overexpression of HMGA2 was asssociated with an inhibition of the expression of several let-7 microRNAs. However, we did not find a leading role of let-7 microRNAs in the deregulation of HMGA2 expression in AT. We also studied partner fusion genes of HMGA2 in lipomas and have specifically identified a new fusion involving PPAP2B (Phosphatidic Acid Phosphatase type 2B) which is located in 1p32. We also confirmed the role of NFIB gene (9p22) in lipomas. Finally, we have established prognostic correlations in a series of 116 ALT/WDLPS and DDLPS: HMGA2 amplification was associated with ALT/WDLPS histotype and a longer survival whereas respective CDK4 and JUN amplification were associated with DDLPS and shorter survival. Thus, our data support the hypothesis of an early and major role of HMGA2 in the genesis well differentiated AT.

Les TSA représentent un groupe de troubles neurodéveloppementaux défini par des déficits des interactions sociales, de la communication et des comportements restreints et répétitifs. A ce jour le diagnostic de l’autisme se fait uniquement sur la base de symptômes cliniques. Il n’existe aucun biomarqueur des TSA. Ce travail s’inscrit donc (1) dans la recherche de biomarqueurs prédictifs dans les TSA et (2) dans la mise en évidence des dysfonctions métaboliques cérébrales dans un modèle de rat (rat valproate, VPA). Pour mettre en évidence des biomarqueurs urinaires prédictifs nous avons analysé simultanément les données issues de plusieurs technologies analytiques afin d’améliorer la robustesse et la capacité prédictive des modèles statistiques. L’autre partie de la thèse consistait à caractériser et à comparer au cours du développement l’évolution du métabolome du rat VPA. Nous avons mis en évidence des perturbations des voies de la neurotransmission, énergétique, du stress oxydatif etc. Même si on ne peut pas transposer les résultats obtenus chez le rat, à l’homme, le modèle VPA permet néanmoins de mieux comprendre les perturbations physiologiques cérébrales induites par ce médicament. / ASDs are a group of neurodevelopmental disorders defined by deficits in social interaction, communication and restricted and repetitive behaviors. To date, the diagnosis of autism is made only on the basis of clinical symptoms. There is no biomarker of ASD. The aim of this work is (1) the search of predictive biomarkers in ASD and (2) the better understanding of brain metabolic dysfunctions in a rat model (valproate rat, VPA). To highlight urinary predictive biomarkers we analyzed together data from different analytical technologies in order to improve the robustness and predictive power of statistical models. The second part of the thesis was to characterize and compare the cerebral metabolome of VPA rat during development. We showed disturbances of neurotransmission, energy, oxidative stress pathways. Even if results obtained in rats cannot be transposed to humans, the VPA model still allows a better understanding the brain physiological disturbances induced by the drug.

Placental dysfunction affects approximately 1 in 10 pregnant women in both the developed and developing worlds. Most commonly, it is manifested as preeclampsia or fetal growth restriction. Over the past two decades, an increasing body of research into the developmental biology of the placenta has been amassed, which points to defects in the differentiation of the trophoblast cell lineage as a key player in the pathophysiology of placental dysfunction. A number of clinical parameters are known to be associated with an elevated risk of placental dysfunction. These include maternal risk factors (such as chronic hypertension, renal disease, and lupus), history of placental dysfunction in a prior pregnancy, abnormalities in the levels of certain proteins in the maternal blood that are commonly used to estimate the risk of fetal genetic defects, and abnormalities in uterine artery Doppler waveforms. These current methods have significant drawbacks, including low specificity and sensitivity, high cost, lack of widespread availability, and lack of validity early in pregnancy. In order to provide a more cost-effective and reliable method to detect an elevated risk for placental dysfunction early in pregnancy, we explored the potential for extracellular RNAs (exRNA) in the maternal serum to be used as biomarkers. In our study, we used next generation sequencing technologies to compare extracellular microRNA (miRNA) levels in serum samples of pregnant women of different gestational ages, nonpregnant women, and placental tissue samples. We discovered that the large majority of microRNAs that were present at higher levels in pregnant serum samples than nonpregnant serum samples and were likely of placental origin. We also found that these pregnancy-specific miRNAs were enriched for miRNAs encoded on chromosomes (Chr) 14 and 19, with changes in the relative expression of these two groups of miRNAs throughout pregnancy. Moreover, the miRNA signatures of late gestational pregnant samples correlated more closely with placental tissue samples than those of early pregnant samples, which could be related to the increasing impact of a larger placenta on the maternal serum exRNA profile. Our results demonstrate the potential utility of next generation sequencing technologies in regards to differentiating between different conditions using clinical samples.

Modern healthcare research demands collaboration across disciplines to build preventive measures and innovate predictive capabilities for curing diseases. Along with the emergence of cutting-edge computational and statistical methodologies, data generation and analysis has become cheaper in the last ten years. However, the complexity of big data due to its variety, volume, and velocity creates new challenges for biologists, physicians, bioinformaticians, statisticians, and computer scientists. Combining data from complex multiple profiles is useful to better understand cellular functions and pathways that regulates cell function to provide insights that could not have been obtained using the individual profiles alone. However, current normalization and artifact correction methods are platform and data type specific, and may require both the training and test sets for any application (e.g. biomarker development). This often leads to over-fitting and reduces the reproducibility of genomic findings across studies. In addition, many bias correction and integration approaches require renormalization or reanalysis if additional samples are later introduced. The motivation behind this research was to develop and evaluate strategies for addressing data integration issues across data types and profiling platforms, which should improve healthcare-informatics research and its application in personalized medicine. We have demonstrated a comprehensive and coordinated framework for data standardization across tissue types and profiling platforms. This allows easy integration of data from multiple data generating consortiums. The main goal of this research was to identify regions of genetic-epigenetic co-ordination that are independent of tissue type and consistent across epigenomics profiling data platforms. We developed multi-‘omic’ therapeutic biomarkers for epigenetic drug efficacy by combining our biomarker regions with drug perturbation data generated in our previous studies. We used an adaptive Bayesian factor analysis approach to develop biomarkers for multiple HDACs simultaneously, allowing for predictions of comparative efficacy between the drugs. We showed that this approach leads to different predictions across breast cancer subtypes compared to profiling the drugs separately. We extended this approach on patient samples from multiple public data resources containing epigenetic profiling data from cancer and normal tissues (The Cancer Genome Atlas, TCGA; NIH Roadmap epigenomics data).