Dysregulated activation of the CXCR4/CXCL12 Axisand its role in the malignant phenotype of neural crest-derived neuroblastoma tumours

Résumé: Le neuroblastome (NB) est un néoplasme dévastateur de la petite enfance, pour lequel il n'existe pas encore de traitement efficace. Les chimiokines et leurs récepteurs ont été impliqués dans la croissance des tumeurs et la formation de métastases, et en particulier, il a été rapporté que l'axe CXCR4/CXCL12 dirigeait le guidage, ainsi que l'invasion des cellules cancéreuses vers des organes spécifiques. Notre étude avait pour objectif d'analyser le rôle de CxCR4 exogène dans le comportement malin du NB, en étudiant la croissance des cellules tumorales, leur capacité de survie, de migration et d'invasion in vitro et en validant ces résultats grâce à un modèle orthotopique murin de la progression tumorale du NB in vivo. La surexpression de CXCR4 dans les cellules faiblement métastatiques IGR-NB8 n'exprimant pas CXCR4, a augmenté la mobilité des cellules vers CXCL12 in vitro. De plus, les cellules surexprimant CXCR4 ont été moins affectées par la privation de sérum que les cellules contrôles. Le volume des tumeurs chez les animaux greffés de manière orthotopique avec les cellules NB8-CXCR4-C3 était significativement plus élevé que celui des tumeurs issues des cellules contrôles NB8-E6 au moment du sacrifice des animaux. Cependant, aucune induction des métastases n'a été observée. La lignée cellulaire IGR-N91, aux propriétés invasives et métastatiques in vivo, exprime constitutivement des quantités modérées de CXCR4. La surexpression du récepteur dans cette lignée a accéléré la croissance tumorale in vivo, mais n'a pas augmenté pas l'occurrence des métastases. Les cellules IGR-N91, dans lesquelles l'expression de CXCR4 a été éteinte, suite à l'introduction de shRNA stable contre CXCR4, a présenté une croissance cellulaire plus lente, in vitro et in vivo. Afin d'identifier les gènes et les voies de signalisation impliqués dans les effets dépendants de CXCR4-CXCL12 dans le NB, des analyses du profil d'expression des gènes ont été effectuées sur les lignées cellulaires transfectées ou non (contrôle). Trois clones contrôles ont été comparés à 3 clones surexprimant CXCR4 pour chacune des lignées (IGR-NB8 et IGR-N91). Les analyses biostatiques ont identifié 10 gènes induits, dont CXCR4, et 31 gènes réprimés, communs entre tous les clones surexprimant CXCR4. Ces observations démontrent que la surexpression de CXCR4 dans le NB stimule la croissance, la survie et la migration chémotactique des cellules tumorales, mais est insuffisante pour induire ou augmenter leurs capacités invasives et métastatiques. Les voies de signalisation activées suite à la surexpression de CXCR4 et identifiées à travers le profil global de l'expression des gènes pourraient être des cibles intéressantes pour le développement de drogues capables d'inhiber la croissance tumorale. Abstact: Neuroblastoma (NB) is a devastating childhood neoplasm for which there is not yet an efficient treatment. Chemokines and their receptors have been involved in tumour growth and metastasis, and in particular the CXCR4/CXCL12 axis has been reported to mediate organ-specific cancer cells homing and invasion. The purpose of the study was to investigate the role of ectopic CXCR4 in the malignant behaviour of NB by studying tumour cell growth, survival, migration, and invasion in vitro and by validating these results using a murine orthotopic model of NB tumour progression in vivo. CXCR4 overexpression in the low metastatic, CXCR4-negative IGR-NB8 cells resulted in CXCL12-mediated chemotaxis in vitro. Furthermore, CXCR4 overexpressing cells were less affected by serum deprivation than mock-transduced cells. In vivo studies revealed that, at sacrifice, volumes of tumours developing in mice with orthotopically implanted NB8-CXCR4-C3 cells, were significantly increased compared to NB8-E6 control tumours. However, no induction of metastases was observed. The in vivo invasive and metastatic cell line IGR-N91 cell line constitutively expresses moderate levels of CXCR4. Overexpression of CXCR4 enhanced in vivo tumour growth but did not increase the occurrence of metastases. IGR-N91 cells where CXCR4 has been knocked-down by stable shRNA grew slower in vitro and in vivo. To identify genes and pathways involved in the CXCR4/CXCL12-mediated effects in NB expression, profiles analyses (Affymetrix) were performed on transduced and control cell lines. Three mock-transduced clones were compared to three CXCR4 overexpressing clones of either cell line IGR-NB8 and IGR-N91. Biostatistical analysis identified 10 commonly upregulated genes (including CXCR4) and 31 downregulated genes common to all CXCR4 overexpressing clones. These observations demonstrate that overexpression of CXCR4 in NB stimulates tumour cell growth, survival, and chemotactic migration but is not sufficient to induce or enhance invasive and metastatic capacities. Activated pathways upon CXCR4 overexpression, identified through global gene expression profiling may be interesting targets for drugs inhibiting tumour growth.

Dynamic drag modeling of submerged aquatic vegetation canopy flows

Vegetation has a profound effect on flow and sediment transport processes in natural rivers, by increasing both skin friction and form drag. The increase in drag introduces a drag discontinuity between the in-canopy flow and the flow above, which leads to the development of an inflection point in the velocity profile, resembling a free shear layer. Therefore, drag acts as the primary driver for the entire canopy system. Most current numerical hydraulic models which incorporate vegetation rely either on simple, static plant forms, or canopy-scaled drag terms. However, it is suggested that these are insufficient as vegetation canopies represent complex, dynamic, porous blockages within the flow, which are subject to spatially and temporally dynamic drag forces. Here we present a dynamic drag methodology within a CFD framework. Preliminary results for a benchmark cylinder case highlight the accuracy of the method, and suggest its applicability to more complex cases.

Development and application of computational methodologies in qualitative modeling

La présente thèse s'intitule "Développent et Application des Méthodologies Computationnelles pour la Modélisation Qualitative". Elle comprend tous les différents projets que j'ai entrepris en tant que doctorante. Plutôt qu'une mise en oeuvre systématique d'un cadre défini a priori, cette thèse devrait être considérée comme une exploration des méthodes qui peuvent nous aider à déduire le plan de processus regulatoires et de signalisation. Cette exploration a été mue par des questions biologiques concrètes, plutôt que par des investigations théoriques. Bien que tous les projets aient inclus des systèmes divergents (réseaux régulateurs de gènes du cycle cellulaire, réseaux de signalisation de cellules pulmonaires) ainsi que des organismes (levure à fission, levure bourgeonnante, rat, humain), nos objectifs étaient complémentaires et cohérents. Le projet principal de la thèse est la modélisation du réseau de l'initiation de septation (SIN) du S.pombe. La cytokinèse dans la levure à fission est contrôlée par le SIN, un réseau signalant de protéines kinases qui utilise le corps à pôle-fuseau comme échafaudage. Afin de décrire le comportement qualitatif du système et prédire des comportements mutants inconnus, nous avons décidé d'adopter l'approche de la modélisation booléenne. Dans cette thèse, nous présentons la construction d'un modèle booléen étendu du SIN, comprenant la plupart des composantes et des régulateurs du SIN en tant que noeuds individuels et testable expérimentalement. Ce modèle utilise des niveaux d'activité du CDK comme noeuds de contrôle pour la simulation d'évènements du SIN à différents stades du cycle cellulaire. Ce modèle a été optimisé en utilisant des expériences d'un seul "knock-out" avec des effets phénotypiques connus comme set d'entraînement. Il a permis de prédire correctement un set d'évaluation de "knock-out" doubles. De plus, le modèle a fait des prédictions in silico qui ont été validées in vivo, permettant d'obtenir de nouvelles idées de la régulation et l'organisation hiérarchique du SIN. Un autre projet concernant le cycle cellulaire qui fait partie de cette thèse a été la construction d'un modèle qualitatif et minimal de la réciprocité des cyclines dans la S.cerevisiae. Les protéines Clb dans la levure bourgeonnante présentent une activation et une dégradation caractéristique et séquentielle durant le cycle cellulaire, qu'on appelle communément les vagues des Clbs. Cet évènement est coordonné avec la courbe d'activation inverse du Sic1, qui a un rôle inhibitoire dans le système. Pour l'identification des modèles qualitatifs minimaux qui peuvent expliquer ce phénomène, nous avons sélectionné des expériences bien définies et construit tous les modèles minimaux possibles qui, une fois simulés, reproduisent les résultats attendus. Les modèles ont été filtrés en utilisant des simulations ODE qualitatives et standardisées; seules celles qui reproduisaient le phénotype des vagues ont été gardées. L'ensemble des modèles minimaux peut être utilisé pour suggérer des relations regulatoires entre les molécules participant qui peuvent ensuite être testées expérimentalement. Enfin, durant mon doctorat, j'ai participé au SBV Improver Challenge. Le but était de déduire des réseaux spécifiques à des espèces (humain et rat) en utilisant des données de phosphoprotéines, d'expressions des gènes et des cytokines, ainsi qu'un réseau de référence, qui était mis à disposition comme donnée préalable. Notre solution pour ce concours a pris la troisième place. L'approche utilisée est expliquée en détail dans le dernier chapitre de la thèse. -- The present dissertation is entitled "Development and Application of Computational Methodologies in Qualitative Modeling". It encompasses the diverse projects that were undertaken during my time as a PhD student. Instead of a systematic implementation of a framework defined a priori, this thesis should be considered as an exploration of the methods that can help us infer the blueprint of regulatory and signaling processes. This exploration was driven by concrete biological questions, rather than theoretical investigation. Even though the projects involved divergent systems (gene regulatory networks of cell cycle, signaling networks in lung cells), as well as organisms (fission yeast, budding yeast, rat, human), our goals were complementary and coherent. The main project of the thesis is the modeling of the Septation Initiation Network (SIN) in S.pombe. Cytokinesis in fission yeast is controlled by the SIN, a protein kinase signaling network that uses the spindle pole body as scaffold. In order to describe the qualitative behavior of the system and predict unknown mutant behaviors we decided to adopt a Boolean modeling approach. In this thesis, we report the construction of an extended, Boolean model of the SIN, comprising most SIN components and regulators as individual, experimentally testable nodes. The model uses CDK activity levels as control nodes for the simulation of SIN related events in different stages of the cell cycle. The model was optimized using single knock-out experiments of known phenotypic effect as a training set, and was able to correctly predict a double knock-out test set. Moreover, the model has made in silico predictions that have been validated in vivo, providing new insights into the regulation and hierarchical organization of the SIN. Another cell cycle related project that is part of this thesis was to create a qualitative, minimal model of cyclin interplay in S.cerevisiae. CLB proteins in budding yeast present a characteristic, sequential activation and decay during the cell cycle, commonly referred to as Clb waves. This event is coordinated with the inverse activation curve of Sic1, which has an inhibitory role in the system. To generate minimal qualitative models that can explain this phenomenon, we selected well-defined experiments and constructed all possible minimal models that, when simulated, reproduce the expected results. The models were filtered using standardized qualitative ODE simulations; only the ones reproducing the wave-like phenotype were kept. The set of minimal models can be used to suggest regulatory relations among the participating molecules, which will subsequently be tested experimentally. Finally, during my PhD I participated in the SBV Improver Challenge. The goal was to infer species-specific (human and rat) networks, using phosphoprotein, gene expression and cytokine data and a reference network provided as prior knowledge. Our solution to the challenge was selected as in the final chapter of the thesis.

Neural detection of complex sound sequences in the absence of consciousness.

The neural response to a violation of sequences of identical sounds is a typical example of the brain's sensitivity to auditory regularities. Previous literature interprets this effect as a pre-attentive and unconscious processing of sensory stimuli. By contrast, a violation to auditory global regularities, i.e. based on repeating groups of sounds, is typically detectable when subjects can consciously perceive them. Here, we challenge the notion that global detection implies consciousness by testing the neural response to global violations in a group of 24 patients with post-anoxic coma (three females, age range 45-87 years), treated with mild therapeutic hypothermia and sedation. By applying a decoding analysis to electroencephalographic responses to standard versus deviant sound sequences, we found above-chance decoding performance in 10 of 24 patients (Wilcoxon signed-rank test, P < 0.001), despite five of them being mildly hypothermic, sedated and unarousable. Furthermore, consistently with previous findings based on the mismatch negativity the progression of this decoding performance was informative of patients' chances of awakening (78% predictive of awakening). Our results show for the first time that detection of global regularities at neural level exists despite a deeply unconscious state.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Elevated hypertension risk for African-origin populations in biracial societies : Modeling the Epidemiologic Transition Study.

OBJECTIVES: Blood pressures in persons of African descent exceed those of other racial/ethnic groups in the United States. Whether this trait is attributable to the genetic factors in African-origin populations, or a result of inadequately measured environmental exposures, such as racial discrimination, is not known. To study this question, we conducted a multisite comparative study of communities in the African diaspora, drawn from metropolitan Chicago, Kingston, Jamaica, rural Ghana, Cape Town, South Africa, and the Seychelles. METHODS: At each site, 500 participants between the age of 25 and 49 years, with approximately equal sex balance, were enrolled for a longitudinal study of energy expenditure and weight gain. In this study, we describe the patterns of blood pressure and hypertension observed at baseline among the sites. RESULTS: Mean SBP and DBP were very similar in the United States and South Africa in both men and women, although among women, the prevalence of hypertension was higher in the United States (24 vs. 17%, respectively). After adjustment for multiple covariates, relative to participants in the United States, SBP was significantly higher among the South Africans by 9.7 mmHg (P < 0.05) and significantly lower for each of the other sites: for example, Jamaica: -7.9 mmHg (P = 0.06), Ghana: -12.8 mmHg (P < 0.01) and Seychelles: -11.1 mmHg (P = 0.01). CONCLUSION: These data are consistent with prior findings of a blood pressure gradient in societies of the African diaspora and confirm that African-origin populations with lower social status in multiracial societies, such as the United States and South Africa, experience more hypertension than anticipated based on anthropometric and measurable socioeconomic risk factors.

Angiogenic activity of breast cancer patients' monocytes reverted by combined use of systems modeling and experimental approaches.

Angiogenesis plays a key role in tumor growth and cancer progression. TIE-2-expressing monocytes (TEM) have been reported to critically account for tumor vascularization and growth in mouse tumor experimental models, but the molecular basis of their pro-angiogenic activity are largely unknown. Moreover, differences in the pro-angiogenic activity between blood circulating and tumor infiltrated TEM in human patients has not been established to date, hindering the identification of specific targets for therapeutic intervention. In this work, we investigated these differences and the phenotypic reversal of breast tumor pro-angiogenic TEM to a weak pro-angiogenic phenotype by combining Boolean modelling and experimental approaches. Firstly, we show that in breast cancer patients the pro-angiogenic activity of TEM increased drastically from blood to tumor, suggesting that the tumor microenvironment shapes the highly pro-angiogenic phenotype of TEM. Secondly, we predicted in silico all minimal perturbations transitioning the highly pro-angiogenic phenotype of tumor TEM to the weak pro-angiogenic phenotype of blood TEM and vice versa. In silico predicted perturbations were validated experimentally using patient TEM. In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes. Finally, the relapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM. In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity. Results showed the successful in vitro reversion of such an activity by perturbation of in silico predicted target genes in tumor derived TEM, and indicated that targeting tumor TEM plasticity may constitute a novel valid therapeutic strategy in breast cancer.

Conditioning of multiple-point statistics facies simulations to tomographic images

Geophysical tomography captures the spatial distribution of the underlying geophysical property at a relatively high resolution, but the tomographic images tend to be blurred representations of reality and generally fail to reproduce sharp interfaces. Such models may cause significant bias when taken as a basis for predictive flow and transport modeling and are unsuitable for uncertainty assessment. We present a methodology in which tomograms are used to condition multiple-point statistics (MPS) simulations. A large set of geologically reasonable facies realizations and their corresponding synthetically calculated cross-hole radar tomograms are used as a training image. The training image is scanned with a direct sampling algorithm for patterns in the conditioning tomogram, while accounting for the spatially varying resolution of the tomograms. In a post-processing step, only those conditional simulations that predicted the radar traveltimes within the expected data error levels are accepted. The methodology is demonstrated on a two-facies example featuring channels and an aquifer analog of alluvial sedimentary structures with five facies. For both cases, MPS simulations exhibit the sharp interfaces and the geological patterns found in the training image. Compared to unconditioned MPS simulations, the uncertainty in transport predictions is markedly decreased for simulations conditioned to tomograms. As an improvement to other approaches relying on classical smoothness-constrained geophysical tomography, the proposed method allows for: (1) reproduction of sharp interfaces, (2) incorporation of realistic geological constraints and (3) generation of multiple realizations that enables uncertainty assessment.