Geological realism in hydrogeological and geophysical inverse modeling: A review

Scientific curiosity, exploration of georesources and environmental concerns are pushing the geoscientific research community toward subsurface investigations of ever-increasing complexity. This review explores various approaches to formulate and solve inverse problems in ways that effectively integrate geological concepts with geophysical and hydrogeological data. Modern geostatistical simulation algorithms can produce multiple subsurface realizations that are in agreement with conceptual geological models and statistical rock physics can be used to map these realizations into physical properties that are sensed by the geophysical or hydrogeological data. The inverse problem consists of finding one or an ensemble of such subsurface realizations that are in agreement with the data. The most general inversion frameworks are presently often computationally intractable when applied to large-scale problems and it is necessary to better understand the implications of simplifying (1) the conceptual geological model (e.g., using model compression); (2) the physical forward problem (e.g., using proxy models); and (3) the algorithm used to solve the inverse problem (e.g., Markov chain Monte Carlo or local optimization methods) to reach practical and robust solutions given today's computer resources and knowledge. We also highlight the need to not only use geophysical and hydrogeological data for parameter estimation purposes, but also to use them to falsify or corroborate alternative geological scenarios.

Quantitative integration of geophysical and hydraulic data : from the local towards the regional scale range

Des progrès significatifs ont été réalisés dans le domaine de l'intégration quantitative des données géophysique et hydrologique l'échelle locale. Cependant, l'extension à de plus grandes échelles des approches correspondantes constitue encore un défi majeur. Il est néanmoins extrêmement important de relever ce défi pour développer des modèles fiables de flux des eaux souterraines et de transport de contaminant. Pour résoudre ce problème, j'ai développé une technique d'intégration des données hydrogéophysiques basée sur une procédure bayésienne de simulation séquentielle en deux étapes. Cette procédure vise des problèmes à plus grande échelle. L'objectif est de simuler la distribution d'un paramètre hydraulique cible à partir, d'une part, de mesures d'un paramètre géophysique pertinent qui couvrent l'espace de manière exhaustive, mais avec une faible résolution (spatiale) et, d'autre part, de mesures locales de très haute résolution des mêmes paramètres géophysique et hydraulique. Pour cela, mon algorithme lie dans un premier temps les données géophysiques de faible et de haute résolution à travers une procédure de réduction déchelle. Les données géophysiques régionales réduites sont ensuite reliées au champ du paramètre hydraulique à haute résolution. J'illustre d'abord l'application de cette nouvelle approche dintégration des données à une base de données synthétiques réaliste. Celle-ci est constituée de mesures de conductivité hydraulique et électrique de haute résolution réalisées dans les mêmes forages ainsi que destimations des conductivités électriques obtenues à partir de mesures de tomographic de résistivité électrique (ERT) sur l'ensemble de l'espace. Ces dernières mesures ont une faible résolution spatiale. La viabilité globale de cette méthode est testée en effectuant les simulations de flux et de transport au travers du modèle original du champ de conductivité hydraulique ainsi que du modèle simulé. Les simulations sont alors comparées. Les résultats obtenus indiquent que la procédure dintégration des données proposée permet d'obtenir des estimations de la conductivité en adéquation avec la structure à grande échelle ainsi que des predictions fiables des caractéristiques de transports sur des distances de moyenne à grande échelle. Les résultats correspondant au scénario de terrain indiquent que l'approche d'intégration des données nouvellement mise au point est capable d'appréhender correctement les hétérogénéitées à petite échelle aussi bien que les tendances à gande échelle du champ hydraulique prévalent. Les résultats montrent également une flexibilté remarquable et une robustesse de cette nouvelle approche dintégration des données. De ce fait, elle est susceptible d'être appliquée à un large éventail de données géophysiques et hydrologiques, à toutes les gammes déchelles. Dans la deuxième partie de ma thèse, j'évalue en détail la viabilité du réechantillonnage geostatique séquentiel comme mécanisme de proposition pour les méthodes Markov Chain Monte Carlo (MCMC) appliquées à des probmes inverses géophysiques et hydrologiques de grande dimension . L'objectif est de permettre une quantification plus précise et plus réaliste des incertitudes associées aux modèles obtenus. En considérant une série dexemples de tomographic radar puits à puits, j'étudie deux classes de stratégies de rééchantillonnage spatial en considérant leur habilité à générer efficacement et précisément des réalisations de la distribution postérieure bayésienne. Les résultats obtenus montrent que, malgré sa popularité, le réechantillonnage séquentiel est plutôt inefficace à générer des échantillons postérieurs indépendants pour des études de cas synthétiques réalistes, notamment pour le cas assez communs et importants où il existe de fortes corrélations spatiales entre le modèle et les paramètres. Pour résoudre ce problème, j'ai développé un nouvelle approche de perturbation basée sur une déformation progressive. Cette approche est flexible en ce qui concerne le nombre de paramètres du modèle et lintensité de la perturbation. Par rapport au rééchantillonage séquentiel, cette nouvelle approche s'avère être très efficace pour diminuer le nombre requis d'itérations pour générer des échantillons indépendants à partir de la distribution postérieure bayésienne. - Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending corresponding approaches beyond the local scale still represents a major challenge, yet is critically important for the development of reliable groundwater flow and contaminant transport models. To address this issue, I have developed a hydrogeophysical data integration technique based on a two-step Bayesian sequential simulation procedure that is specifically targeted towards larger-scale problems. The objective is to simulate the distribution of a target hydraulic parameter based on spatially exhaustive, but poorly resolved, measurements of a pertinent geophysical parameter and locally highly resolved, but spatially sparse, measurements of the considered geophysical and hydraulic parameters. To this end, my algorithm links the low- and high-resolution geophysical data via a downscaling procedure before relating the downscaled regional-scale geophysical data to the high-resolution hydraulic parameter field. I first illustrate the application of this novel data integration approach to a realistic synthetic database consisting of collocated high-resolution borehole measurements of the hydraulic and electrical conductivities and spatially exhaustive, low-resolution electrical conductivity estimates obtained from electrical resistivity tomography (ERT). The overall viability of this method is tested and verified by performing and comparing flow and transport simulations through the original and simulated hydraulic conductivity fields. The corresponding results indicate that the proposed data integration procedure does indeed allow for obtaining faithful estimates of the larger-scale hydraulic conductivity structure and reliable predictions of the transport characteristics over medium- to regional-scale distances. The approach is then applied to a corresponding field scenario consisting of collocated high- resolution measurements of the electrical conductivity, as measured using a cone penetrometer testing (CPT) system, and the hydraulic conductivity, as estimated from electromagnetic flowmeter and slug test measurements, in combination with spatially exhaustive low-resolution electrical conductivity estimates obtained from surface-based electrical resistivity tomography (ERT). The corresponding results indicate that the newly developed data integration approach is indeed capable of adequately capturing both the small-scale heterogeneity as well as the larger-scale trend of the prevailing hydraulic conductivity field. The results also indicate that this novel data integration approach is remarkably flexible and robust and hence can be expected to be applicable to a wide range of geophysical and hydrological data at all scale ranges. In the second part of my thesis, I evaluate in detail the viability of sequential geostatistical resampling as a proposal mechanism for Markov Chain Monte Carlo (MCMC) methods applied to high-dimensional geophysical and hydrological inverse problems in order to allow for a more accurate and realistic quantification of the uncertainty associated with the thus inferred models. Focusing on a series of pertinent crosshole georadar tomographic examples, I investigated two classes of geostatistical resampling strategies with regard to their ability to efficiently and accurately generate independent realizations from the Bayesian posterior distribution. The corresponding results indicate that, despite its popularity, sequential resampling is rather inefficient at drawing independent posterior samples for realistic synthetic case studies, notably for the practically common and important scenario of pronounced spatial correlation between model parameters. To address this issue, I have developed a new gradual-deformation-based perturbation approach, which is flexible with regard to the number of model parameters as well as the perturbation strength. Compared to sequential resampling, this newly proposed approach was proven to be highly effective in decreasing the number of iterations required for drawing independent samples from the Bayesian posterior distribution.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.

In vitro engineering of human stratified urothelium: analysis of its morphology and function.

PURPOSE: Gastric or intestinal patches, commonly used for reconstructive cystoplasty, may induce severe metabolic complications. The use of bladder tissues reconstructed in vitro could avoid these complications. We compared cellular differentiation and permeability characteristics of human native with in vitro cultured stratified urothelium. MATERIALS AND METHODS: Human stratified urothelium was induced in vitro. Morphology was studied with light and electron microscopy and expression of key cellular proteins was assessed using immunohistochemistry. Permeability coefficients were determined by measuring water, urea, ammonia and proton fluxes across the urothelium. RESULTS: As in native urothelium the stratified urothelial construct consisted of basal membrane and basal, intermediate and superficial cell layers. The apical membrane of superficial cells formed villi and glycocalices, and tight junctions and desmosomes were developed. Immunohistochemistry showed similarities and differences in the expression of cytokeratins, integrin and cellular adhesion proteins. In the cultured urothelium cytokeratin 20 and integrin subunits alpha6 and beta4 were absent, and symplekin was expressed diffusely in all layers. Uroplakins were clearly expressed in the superficial umbrella cells of the urothelial constructs, however, they were also present in intermediate and basal cells. Symplekin and uroplakins were expressed only in the superficial cells of native bladder tissue. The urothelial constructs showed excellent viability, and functionally their permeabilities for water, urea and ammonia were no different from those measured in native human urothelium. Proton permeability was even lower in the constructs compared to that of native urothelium. CONCLUSIONS: Although the in vitro cultured human stratified urothelium did not show complete terminal differentiation of its superficial cells, it retained the same barrier characteristics against the principal urine components. These results indicate that such in vitro cultured urothelium, after being grown on a compliant degradable support or in coculture with smooth muscle cells, is suitable for reconstructive cystoplasty.

A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization.

The goal of the present work was assess the feasibility of using a pseudo-inverse and null-space optimization approach in the modeling of the shoulder biomechanics. The method was applied to a simplified musculoskeletal shoulder model. The mechanical system consisted in the arm, and the external forces were the arm weight, 6 scapulo-humeral muscles and the reaction at the glenohumeral joint, which was considered as a spherical joint. The muscle wrapping was considered around the humeral head assumed spherical. The dynamical equations were solved in a Lagrangian approach. The mathematical redundancy of the mechanical system was solved in two steps: a pseudo-inverse optimization to minimize the square of the muscle stress and a null-space optimization to restrict the muscle force to physiological limits. Several movements were simulated. The mathematical and numerical aspects of the constrained redundancy problem were efficiently solved by the proposed method. The prediction of muscle moment arms was consistent with cadaveric measurements and the joint reaction force was consistent with in vivo measurements. This preliminary work demonstrated that the developed algorithm has a great potential for more complex musculoskeletal modeling of the shoulder joint. In particular it could be further applied to a non-spherical joint model, allowing for the natural translation of the humeral head in the glenoid fossa.

Zerebrale Probleme in der pädiatrischen Intensivmedizin [Cerebral problems in pediatric intensive care]

This paper presents a review of different methods enabling the monitoring of cerebral function in neonatal and paediatric intensive care. EEG, evoked potentials, conventional radiological studies, computerized tomography, ultrasound, intracranial pressure measurements, nuclear magnetic resonance, Doppler ultrasound, radioisotope studies, angiography, infra-red spectral analysis and last, but not least, clinical examination produce information regarding the neurological state of the patient which must be critically analysed in order to ensure optimal management of the case. Unfortunately, and in spite of impressive progress in non-invasive monitoring of the cerebral function, we are still forced to make important medical and ethical decisions without precise information about the neurological state of our patients.

Neuropeptide-inducible upregulation of proteasome activity precedes nuclear factor kappa B activation in androgen-independent prostate cancer cells.

ABSTRACT: BACKGROUND: Upregulation of nuclear factor kappa B (NFκB) activity and neuroendocrine differentiation are two mechanisms known to be involved in prostate cancer (PC) progression to castration resistance. We have observed that major components of these pathways, including NFκB, proteasome, neutral endopeptidase (NEP) and endothelin 1 (ET-1), exhibit an inverse and mirror image pattern in androgen-dependent (AD) and -independent (AI) states in vitro. METHODS: We have now investigated for evidence of a direct mechanistic connection between these pathways with the use of immunocytochemistry (ICC), western blot analysis, electrophoretic mobility shift assay (EMSA) and proteasome activity assessment. RESULTS: Neuropeptide (NP) stimulation induced nuclear translocation of NFκB in a dose-dependent manner in AI cells, also evident as reduced total inhibitor κB (IκB) levels and increased DNA binding in EMSA. These effects were preceded by increased 20 S proteasome activity at lower doses and at earlier times and were at least partially reversed under conditions of NP deprivation induced by specific NP receptor inhibitors, as well as NFκB, IκB kinase (IKK) and proteasome inhibitors. AD cells showed no appreciable nuclear translocation upon NP stimulation, with less intense DNA binding signal on EMSA. CONCLUSIONS: Our results support evidence for a direct mechanistic connection between the NPs and NFκB/proteasome signaling pathways, with a distinct NP-induced profile in the more aggressive AI cancer state.

Source wavelet estimation for waveform inversion of crosshole georadar data

AbstractFor a wide range of environmental, hydrological, and engineering applications there is a fast growing need for high-resolution imaging. In this context, waveform tomographic imaging of crosshole georadar data is a powerful method able to provide images of pertinent electrical properties in near-surface environments with unprecedented spatial resolution. In contrast, conventional ray-based tomographic methods, which consider only a very limited part of the recorded signal (first-arrival traveltimes and maximum first-cycle amplitudes), suffer from inherent limitations in resolution and may prove to be inadequate in complex environments. For a typical crosshole georadar survey the potential improvement in resolution when using waveform-based approaches instead of ray-based approaches is in the range of one order-of- magnitude. Moreover, the spatial resolution of waveform-based inversions is comparable to that of common logging methods. While in exploration seismology waveform tomographic imaging has become well established over the past two decades, it is comparably still underdeveloped in the georadar domain despite corresponding needs. Recently, different groups have presented finite-difference time-domain waveform inversion schemes for crosshole georadar data, which are adaptations and extensions of Tarantola's seminal nonlinear generalized least-squares approach developed for the seismic case. First applications of these new crosshole georadar waveform inversion schemes on synthetic and field data have shown promising results. However, there is little known about the limits and performance of such schemes in complex environments. To this end, the general motivation of my thesis is the evaluation of the robustness and limitations of waveform inversion algorithms for crosshole georadar data in order to apply such schemes to a wide range of real world problems.One crucial issue to making applicable and effective any waveform scheme to real-world crosshole georadar problems is the accurate estimation of the source wavelet, which is unknown in reality. Waveform inversion schemes for crosshole georadar data require forward simulations of the wavefield in order to iteratively solve the inverse problem. Therefore, accurate knowledge of the source wavelet is critically important for successful application of such schemes. Relatively small differences in the estimated source wavelet shape can lead to large differences in the resulting tomograms. In the first part of my thesis, I explore the viability and robustness of a relatively simple iterative deconvolution technique that incorporates the estimation of the source wavelet into the waveform inversion procedure rather than adding additional model parameters into the inversion problem. Extensive tests indicate that this source wavelet estimation technique is simple yet effective, and is able to provide remarkably accurate and robust estimates of the source wavelet in the presence of strong heterogeneity in both the dielectric permittivity and electrical conductivity as well as significant ambient noise in the recorded data. Furthermore, our tests also indicate that the approach is insensitive to the phase characteristics of the starting wavelet, which is not the case when directly incorporating the wavelet estimation into the inverse problem.Another critical issue with crosshole georadar waveform inversion schemes which clearly needs to be investigated is the consequence of the common assumption of frequency- independent electromagnetic constitutive parameters. This is crucial since in reality, these parameters are known to be frequency-dependent and complex and thus recorded georadar data may show significant dispersive behaviour. In particular, in the presence of water, there is a wide body of evidence showing that the dielectric permittivity can be significantly frequency dependent over the GPR frequency range, due to a variety of relaxation processes. The second part of my thesis is therefore dedicated to the evaluation of the reconstruction limits of a non-dispersive crosshole georadar waveform inversion scheme in the presence of varying degrees of dielectric dispersion. I show that the inversion algorithm, combined with the iterative deconvolution-based source wavelet estimation procedure that is partially able to account for the frequency-dependent effects through an "effective" wavelet, performs remarkably well in weakly to moderately dispersive environments and has the ability to provide adequate tomographic reconstructions.

Expression of neutral endopeptidase, endothelin-1, and nuclear factor kappa B in prostate cancer: interrelations and associations with prostate-specific antigen recurrence after radical prostatectomy.

Objective. To study the impact of the neutral endopeptidase (NEP)/neuropeptides (NPs) axis and nuclear factor kappa B (NFκB) as predictors of prostate-specific antigen (PSA) recurrence after radical prostatectomy (RP). Patients and Methods. 70 patients with early-stage PC were treated with RP and their tumor samples were evaluated for expression of NEP, endothelin-1 (ET-1) and NFκB (p65). Time to PSA recurrence was correlated with the examined parameters and combined with preoperative PSA level, Gleason score, pathological TNM (pT) stage, and surgical margin (SM) assessment. Results and Limitations. Membranous expression of NEP (P < 0.001), cytoplasmic ET-1 (P = 0.002), and cytoplasmic NFκB (P < 0.001) were correlated with time to PSA relapse. NEP was associated with ET-1 (P < 0.001) and NFκB (P < 0.001). ET-1 was also correlated with NFκB (P < 0.001). NEP expression (P = 0.017), pT stage (P = 0.013), and SMs (P = 0.036) were independent predictors of time to PSA recurrence. Conclusions. There seems to be a clinical model of NEP/NPs and NFκB pathways interconnection, with their constituents following inverse patterns of expression in accordance with their biological roles and molecular interrelations.

The biogeography of mitochondrial and nuclear discordance in animals.

Combining nuclear (nuDNA) and mitochondrial DNA (mtDNA) markers has improved the power of molecular data to test phylogenetic and phylogeographic hypotheses and has highlighted the limitations of studies using only mtDNA markers. In fact, in the past decade, many conflicting geographic patterns between mitochondrial and nuclear genetic markers have been identified (i.e. mito-nuclear discordance). Our goals in this synthesis are to: (i) review known cases of mito-nuclear discordance in animal systems, (ii) to summarize the biogeographic patterns in each instance and (iii) to identify common drivers of discordance in various groups. In total, we identified 126 cases in animal systems with strong evidence of discordance between the biogeographic patterns obtained from mitochondrial DNA and those observed in the nuclear genome. In most cases, these patterns are attributed to adaptive introgression of mtDNA, demographic disparities and sex-biased asymmetries, with some studies also implicating hybrid zone movement, human introductions and Wolbachia infection in insects. We also discuss situations where divergent mtDNA clades seem to have arisen in the absence of geographic isolation. For those cases where foreign mtDNA haplotypes are found deep within the range of a second taxon, data suggest that those mtDNA haplotypes are more likely to be at a high frequency and are commonly driven by sex-biased asymmetries and/or adaptive introgression. In addition, we discuss the problems with inferring the processes causing discordance from biogeographic patterns that are common in many studies. In many cases, authors presented more than one explanation for discordant patterns in a given system, which indicates that likely more data are required. Ideally, to resolve this issue, we see important future work shifting focus from documenting the prevalence of mito-nuclear discordance towards testing hypotheses regarding the drivers of discordance. Indeed, there is great potential for certain cases of mitochondrial introgression to become important natural systems within which to test the effect of different mitochondrial genotypes on whole-animal phenotypes.

Nuclear well logging in soils. Calibration of slim hole nuclear tools gamma-gamma and neutron-thermal neutron.

The calculation of elasticity parameters by sonic and ultra sonic wave propagation in saturated soils using Biot's theory needs the following variables : forpiation density and porosity (p, ø), compressional and shear wave velocities (Vp, Vs), fluid density, viscosity and compressibility (Pfi Ilfi Ki), matrix density and compressibility (p" K), The first four parameters can be determined in situ using logging probes. Because fluid and matrix characteristics are not modified during core extraction, they can be obtained through laboratory measurements. All parameters necessitate precise calibrations in various environments and for specific range of values encountered in soils. The slim diameter of boreholes in shallow geophysics and the high cost of petroleum equipment demand the use of specific probes, which usually only give qualitative results. The measurement 'of density is done with a gamma-gamma probe and the measurement of hydrogen index, in relation to porosity, by a neutron probe. The first step of this work has been carried out in synthetic formations in the laboratory using homogeneous media of known density and porosity. To establish borehole corrections different casings have been used. Finally a comparison between laboratory and in situ data in cored holes of known geometry and casing has been performed.

The biology of cancer stem cells : part 1: nuclear translocation of CD44 : part 2: IMP2 in glioblastoma cancer stem cells

SummaryCancer stem cells (CSC) are poorly differentiated, slowly proliferating cells, with high tumorigenic potential. Some of these cells, as it has been shown in leukemia, evade chemo- and radiotherapy and recapitulate the tumor composed of CSC and their highly proliferative progeny. Therefore, understanding the molecular biology of those cells is crucial for improvement of currently used anti-cancer therapies.This work is composed of two CSC-related projects. The first deals with CD44, a frequently used marker of CSC; the second involves Imp2 and its role in CSC bioenergetics. PART 1. CD44 is a multifunctional transmembrane protein involved in migration, homing, adhesion, proliferation and survival. It is overexpressed in many cancers and its levels are correlated with poor prognosis. CD44 is also highly expressed by CSC and in many malignancies it is used for CSC isolation.In the present work full-lenght CD44 nuclear localization was studied, including the mechanism of nuclear translocation and its functional role in the nucleus. Full-length CD44 can be found in nuclei of various cell types, regardless of their tumorigenic potential. For nuclear localization, CD44 needs to be first inserted into the cell membrane, from which it is transported via the endocytic pathway. Upon binding to transportinl it is translocated to the nucleus. The nuclear localization signal recognized by transportinl has been determined as the first 20 amino acids of the membrane proximal intracellular domain. Nuclear export of CD44 is facilitated by exportin Crml. Investigation of the function of nuclear CD44 revealed its implication in de novo RNA synthesis.PART 2. Glioblastoma multiforme is the most aggressive and most frequent brain malignancy. It was one of the first solid tumors from which CSC have been isolated. Based on the similarity between GBM CSC and normal stem cells expression of an oncofetal mRNA binding protein Imp2 has been investigated.Imp2 is absent in normal brain as well as in low grade gliomas, but is expressed in over 75% GBM cases and its expression is higher in CSC compared to their more differentiated counterparts. Analysis of mRNA transcripts bound by Imp2 and its protein interactors revealed that in GBM CSC Imp2 may be implicated in mitochondrial metabolism. Indeed, shRNA mediated silencing of protein expression led to decreased mitochondrial activity, decreased oxygen consumption and decreased activity of respiratory chain protein complex I. Moreover, lack of Imp2 severely affected self-renewal and tumorigenicity of GBM CSC. Experimental evidence suggest that GBM CSC depend on mitochondrial oxidative phosphorylation as an energy producing pathway and that Imp2 is a novel regulator of this pathway.RésuméLes cellules cancéreuses souches sont des cellules peu différentiées, à proliferation lente et hautement tumorigénique. Ces cellules sont radio-chimio résistantes et sont capable reformer la tumeur dans sont intégralité, reproduisant l'hétérogénéité cellulaire présent dans la tumeur d'origine. Pour améliorer les therapies antitumorales actuelles il est crucial de comprendre les mécanismes moléculaires qui caractérisent cette sous-population de cellules hautement malignes.Ce travail de thèse se compose de deux projets s'articulant autour du même axe :Le CD44 est une protéine multifonctionnelle et transmembranaire très souvent utilisée comme marqueur de cellules souches tumorales dans différents cancers. Elle est impliquée dans la migration, l'adhésion, la prolifération et la survie des cellules. Lors de ce travail de recherche, nous nous sommes intéressés à la localisation cellulaire du CD44, ainsi qu'aux mécanismes permettant sa translocation nucléaire. En effet, bien que principalement décrit comme un récepteur de surface transmembranaire, le CD44 sous sa forme entière, non clivée en peptides, peut également être observé à l'intérieur du noyau de diverses cellules, quel que soit leur potentiel tumorigénique. Pour passer ainsi d'un compartiment cellulaire à un autre, le CD44 doit d'abord être inséré dans la membrane plasmique, d'où il est transporté par endocytose jusqu'à l'intérieur du cytoplasme. La transportai permet ensuite la translocation nucléaire du CD44 via une « séquence signal » contenue dans les 20 acides aminés du domaine cytoplasmique qui bordent la membrane. A l'inverse, le CD44 est exporté du noyau grâce à l'exportin Crml. En plus des mécanismes décrits ci-dessus, cette étude a également mis en évidence l'implication du CD44 dans la synthèse des ARN, d'où sa présence dans le noyau.Le glioblastome est la plus maligne et la plus fréquente des tumeurs cérébrales. Dans ce second projet de recherche, le rôle de IMP2 dans les cellules souches tumorales de glioblastomes a été étudié. La présence de cette protéine oncofoetale a d'abord été mise en évidence dans 75% des cas les plus agressifs des gliomes (grade IV, appelés glioblastomes), tandis qu'elle n'est pas exprimée dans les grades I à III de ces tumeurs, ni dans le cerveau sain. De plus, IMP2 est apparue comme étant davantage exprimée dans les cellules souches tumorales que dans les cellules déjà différenciées. La baisse de l'expression de IMP2 au moyen de shRNA a résulté en une diminution de l'activité mitochondriale, en une réduction de la consommation d'oxygène ainsi qu'en une baisse de l'activité du complexe respiratoire I.L'inhibition de IMP2 a également affecté la capacité de renouvellement de la population des cellules souches tumorales ainsi que leur aptitude à former des tumeurs.Lors de ce travail de thèse, une nouvelle fonction d'un marqueur de cellules souches tumorales a été mise en évidence, ainsi qu'un lien important entre la bioénergétique de ces cellules et l'expression d'une protéine oncofoetale.