Regulatory RNA binding molecules : implication for diabetes pathogenesis

Le glucose est notre principale source d'énergie. Après un repas, le taux de glucose dans le sang (glycémie) augmente, ce qui entraine la sécrétion d'insuline. L'insuline est une hormone synthétisée au niveau du pancréas par des cellules dites bêta. Elle agit sur différents organes tels que les muscles, le foie ou le tissu adipeux, induisant ainsi le stockage du glucose en vue d'une utilisation future.¦Le diabète est une maladie caractérisée par un taux élevé de glucose dans le sang (hyperglycémie), résultant d'une incapacité de notre corps à utiliser ou à produire suffisamment d'insuline. A long terme, cette hyperglycémie entraîne une détérioration du système cardio-vasculaire ainsi que de nombreuses complications. On distingue principalement deux type de diabète : le diabète de type 1 et le diabète de type 2, le plus fréquent (environ 90% des cas). Bien que ces deux maladies diffèrent sur beaucoup de points, elles partagent quelques similitudes. D'une part, on décèle une diminution de la quantité de cellules bêta. Cette diminution est cependant partielle dans le cas d'un diabète de type 2, et totale dans celui d'un diabète de type 1. D'autre part, la présence dans la circulation de médiateurs de l'inflammation nommés cytokines est décelée aussi bien chez les patients de type 1 que de type 2. Les cytokines sont sécrétées lors d'une inflammation. Elles servent de moyen de communication entre les différents acteurs de l'inflammation et ont pour certaines un effet néfaste sur la survie des cellules bêta.¦L'objectif principal de ma thèse a été d'étudier en détail l'effet de petites molécules régulatrices de l'expression génique, appelées microARNs. Basé sur le fait que de nombreuses publications ont démontré que les microARNs étaient impliqués dans différentes maladies telles que le cancer, j'ai émis l'hypothèse qu'ils pouvaient également jouer un rôle important dans le développement du diabète.¦Nous avons commencé par mettre des cellules bêta en culture en présence de cytokines, imitant ainsi un environnement inflammatoire. Nous avons pu de ce fait identifier les microARNs dont les niveaux d'expression étaient modifiés. A l'aide de méthodes biochimiques, nous avons ensuite observé que la modulation de certains microARNs par les cytokines avaient des effets néfastes sur la cellule bêta : sur sa production et sa sécrétion d'insuline, ainsi que sur sa mort (apoptose). Nous avons en conséquence pu démontrer que ces petites molécules avaient un rôle important à jouer dans le dysfonctionnement des cellules bêta induit par les cytokines, aboutissant au développement du diabète.¦-¦La cellule bêta pancréatique est une cellule endocrine présente dans les îlots de Langerhans, dans le pancréas. L'insuline, une hormone sécrétée par ces cellules, joue un rôle essentiel dans la régulation de la glycémie. Le diabète se développe si le taux d'insuline relâché par les cellules bêta n'est pas suffisant pour couvrir les besoins métaboliques corporels. Le diabète de type 1, qui représente environ 5 à 10% des cas, est une maladie auto-immune qui se caractérise par une réaction inflammatoire déclenchée par notre système immunitaire envers les cellules bêta. La conséquence de cette attaque est une disparition progressive des cellules bêta. Le diabète de type 2 est, quant à lui, largement plus répandu puisqu'il représente environ 90% des cas. Des facteurs à la fois génétiques et environnementaux sont responsables d'une diminution de la sensibilité des tissus métabolisant l'insuline, ainsi que d'une réduction de la sécrétion de l'insuline par les cellules bêta, ce qui a pour conséquence le développement de la maladie. Malgré les différences entre ces deux types de diabète, ils ont pour points communs la présence d'infiltrat immunitaire et la diminution de l'état fonctionnel des cellules bêta.¦Une meilleure compréhension des mécanismes aboutissant à l'altération de la cellule bêta est primordiale, avant de pouvoir développer de nouvelles stratégies thérapeutiques capables de guérir cette maladie. Durant ma thèse, j'ai donc étudié l'implication de petites molécules d'ARN, régulatrices de l'expression génique, appelées microARNs, dans les conditions physiopathologiques qui aboutissent au développement du diabète. J'ai débuté mon étude par l'identification de microARNs dont le niveau d'expression était modifié lorsque les cellules bêta étaient exposées à des conditions favorisant à la fois le développement du diabète de type 1 (cytokines) et celui du diabète de type 2 (palmitate). Nous avons découvert qu'une modification de l'expression des miR-21, -34a et -146a était commune aux deux traitements. Ces changements d'expressions ont également été confirmés dans deux modèles animaux : les souris NOD qui développent un diabète s'apparentant au diabète de type 1 et les souris db/db qui développent plutôt un diabète de type 2. Puis, à l'aide de puces à ADN, nous avons comparé l'expression de microARNs chez des souris NOD pré-diabétiques. Nous avons alors retrouvé des changements au niveau de l'expression des mêmes microARNs mais également au niveau d'une famille de microARNs : les miR-29a, -29b et -29c. De manière artificielle, nous avons ensuite surexprimé ou inhibé en conditions physiopathologiques l'expression de tous ces microARNs et nous nous sommes intéressés à l'impact d'un tel changement sur différentes fonctions de la cellule bêta comme la synthèse et la sécrétion d'insulinè ainsi que leur survie. Nous avons ainsi pu démontrer que les miR-21, -34a, -29a, -29b, -29c avaient un effet délétère sur la sécrétion d'insuline et que la surexpression de tous ces microARNs (excepté le miR-21) favorisait la mort. Finalement, nous avons démontré que la plupart de ces microARNs étaient impliqués dans la régulation d'importantes voies de signalisation responsables de l'apoptose des cellules bêta telles que les voies de NFKB, BCL2 ou encore JNK.¦Par conséquent, nos résultats démontrent que les microARNs ont un rôle important à jouer dans le dysfonctionnement des cellules bêta lors de la mise en place du diabète.

Monocyte differentiation toward regulatory dendritic cells is not affected by respiratory syncytial virus-induced inflammatory mediators.

Airway epithelial cells were shown to drive the differentiation of monocytes into dendritic cells (DCs) with a suppressive phenotype. In this study, we investigated the impact of virus-induced inflammatory mediator production on the development of DCs. Monocyte differentiation into functional DCs, as reflected by the expression of CD11c, CD123, BDCA-4, and DC-SIGN and the capacity to activate T cells, was similar for respiratory syncytial virus (RSV)-infected and mock-infected BEAS-2B and A549 cells. RSV-conditioned culture media resulted in a partially mature DC phenotype, but failed to up-regulate CD80, CD83, CD86, and CCR7, and failed to release proinflammatory mediators upon Toll-like receptor (TLR) triggering. Nevertheless, these DCs were able to maintain an antiviral response by the release of Type I IFN. Collectively, these data indicate that the airway epithelium maintains an important suppressive DC phenotype under the inflammatory conditions induced by infection with RSV.

Properties of functional brain networks correlate with frequency of psychogenic non-epileptic seizures.

Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and small-world structure, estimated with clustering coefficient, modularity, global efficiency, and small-worldness (SW) metrics, respectively. Yet the number of PNES attacks per month correlated with a weakness of local connectedness and a skewed balance between local and global connectedness quantified with SW, all in EEG alpha band. In beta band, patients demonstrated above-normal resiliency, measured with assortativity coefficient, which also correlated with the frequency of PNES attacks. This interictal EEG phenotype may help improve differentiation between PNES and epilepsy. The results also suggest that local connectivity could be a target for therapeutic interventions in PNES. Selective modulation (strengthening) of local connectivity might improve the skewed balance between local and global connectivity and so prevent PNES events.

Nuclear receptor Rev-erb alpha (Nr1d1) functions in concert with Nr2e3 to regulate transcriptional networks in the retina.

The majority of diseases in the retina are caused by genetic mutations affecting the development and function of photoreceptor cells. The transcriptional networks directing these processes are regulated by genes such as nuclear hormone receptors. The nuclear hormone receptor gene Rev-erb alpha/Nr1d1 has been widely studied for its role in the circadian cycle and cell metabolism, however its role in the retina is unknown. In order to understand the role of Rev-erb alpha/Nr1d1 in the retina, we evaluated the effects of loss of Nr1d1 to the developing retina and its co-regulation with the photoreceptor-specific nuclear receptor gene Nr2e3 in the developing and mature retina. Knock-down of Nr1d1 expression in the developing retina results in pan-retinal spotting and reduced retinal function by electroretinogram. Our studies show that NR1D1 protein is co-expressed with NR2E3 in the outer neuroblastic layer of the developing mouse retina. In the adult retina, NR1D1 is expressed in the ganglion cell layer and is co-expressed with NR2E3 in the outer nuclear layer, within rods and cones. Several genes co-targeted by NR2E3 and NR1D1 were identified that include: Nr2c1, Recoverin, Rgr, Rarres2, Pde8a, and Nupr1. We examined the cyclic expression of Nr1d1 and Nr2e3 over a twenty-four hour period and observed that both nuclear receptors cycle in a similar manner. Taken together, these studies reveal a novel role for Nr1d1, in conjunction with its cofactor Nr2e3, in regulating transcriptional networks critical for photoreceptor development and function.

Using active learning for monitoring networks design: The example of wind power plants sites evaluation

Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highly nonlinear and related to complex topographical features. In this paper, we propose both a nonparametric model to estimate wind speed at different time instants and a procedure to discover underrepresented topographic conditions, where new measuring stations could be added. Compared to space filling techniques, this last approach privileges optimization of the output space, thus locating new potential measuring sites through the uncertainty of the model itself.

Modeling resting-state functional networks when the cortex falls asleep: local and global changes.

The transition from wakefulness to sleep represents the most conspicuous change in behavior and the level of consciousness occurring in the healthy brain. It is accompanied by similarly conspicuous changes in neural dynamics, traditionally exemplified by the change from "desynchronized" electroencephalogram activity in wake to globally synchronized slow wave activity of early sleep. However, unit and local field recordings indicate that the transition is more gradual than it might appear: On one hand, local slow waves already appear during wake; on the other hand, slow sleep waves are only rarely global. Studies with functional magnetic resonance imaging also reveal changes in resting-state functional connectivity (FC) between wake and slow wave sleep. However, it remains unclear how resting-state networks may change during this transition period. Here, we employ large-scale modeling of the human cortico-cortical anatomical connectivity to evaluate changes in resting-state FC when the model "falls asleep" due to the progressive decrease in arousal-promoting neuromodulation. When cholinergic neuromodulation is parametrically decreased, local slow waves appear, while the overall organization of resting-state networks does not change. Furthermore, we show that these local slow waves are structured macroscopically in networks that resemble the resting-state networks. In contrast, when the neuromodulator decrease further to very low levels, slow waves become global and resting-state networks merge into a single undifferentiated, broadly synchronized network.

Transplantation tolerance induced by regulatory T cells: in vivo mechanisms and sites of action.

The mechanisms by which CD4(+)CD25(+)Foxp3(+) T (Treg) cells regulate effector T cells in a transplantation setting and their in vivo homeostasis still remain to be clarified. Using a mouse adoptive transfer model, we analyzed the in vivo expansion, trafficking, and effector function of alloreactive T cells and donor-specific Treg cells, in response to a full-thickness skin allograft. Fluorescent-labeled CD4(+)CD25(-) and antigen-specific Treg cells were transferred alone or co-injected into syngeneic BALB/c-Nude recipients transplanted with skins from (C57BL/6 x BALB/c) F1 donors. Treg cells divided in vivo, migrated and accumulated in the allograft draining lymph nodes as well as within the graft. The co-transfer of Treg cells did not modify the early activation and homing of CD4(+)CD25(-) T cells in secondary lymphoid organs. However, in the presence of Treg cells, alloreactive CD4(+)CD25(-) T cells produced significantly less IFN-gamma and were present in reduced numbers in the secondary lymphoid organs. Furthermore, time-course studies showed that Treg cells were recruited into the allograft at a very early stage after transplantation and effectively prevented the infiltration of effector T cells. In conclusion, suppression of rejection requires the early recruitment to the site of antigenic challenge of donor-specific Treg cells, which then mainly regulate the effector arm of T cell alloresponses.

HIV-1 drug resistance transmission networks in southwest Switzerland.

To determine viral subtypes and resistance mutations to antiretroviral treatment (ART) in untreated HIV-1 acutely infected subjects from Southwest Switzerland. Clinical samples were obtained from the HIV primary infection cohort from Lausanne. Briefly, pol gene was amplified by nested PCR and sequenced to generate a 1?kb sequence spanning protease and reverse transcriptase key protein regions. Nucleotide sequences were used to assess viral genotype and ART resistance mutations. Blood specimens and medical information were obtained from 30 patients. Main viral subtypes corresponded to clade B, CRF02_AG, and F1. Resistant mutations to PIs consisted of L10V and accessory mutations 16E and 60E present in all F1 clades. The NNRTI major resistant mutation 103N was detected in all F1 viruses and in other 2 clades. Additionally, we identified F1 sequences from other 6 HIV infected and untreated individuals from Southwest Switzerland, harboring nucleotide motifs and resistance mutations to ART as observed in the F1 strains from the cohort. These data reveal a high transmission rate (16.6%) for NNRTI resistant mutation 103N in a cohort of HIV acute infection. Three of the 5 resistant strains were F1 clades closely related to other F1 isolates from HIV-1 infection untreated patients also coming from Southwest Switzerland. Overall, we provide strong evidence towards an HIV-1 resistant transmission network in Southwest Switzerland. These findings have relevant implications for the local molecular mapping of HIV-1 and future ART surveillance studies in the region.

Methylation profile of TP53 regulatory pathway and mtDNA alterations in breast cancer patients lacking TP53 mutations.

The present study investigated promoter hypermethylation of TP53 regulatory pathways providing a potential link between epigenetic changes and mitochondrial DNA (mtDNA) alterations in breast cancer patients lacking a TP53 mutation. The possibility of using the cancer-specific alterations in serum samples as a blood-based test was also explored. Triple-matched samples (cancerous tissues, matched adjacent normal tissues and serum samples) from breast cancer patients were screened for TP53 mutations, and the promoter methylation profile of P14(ARF), MDM2, TP53 and PTEN genes was analyzed as well as mtDNA alterations, including D-loop mutations and mtDNA content. In the studied cohort, no mutation was found in TP53 (DNA-binding domain). Comparison of P14(ARF) and PTEN methylation patterns showed significant hypermethylation levels in tumor tissues (P < 0.05 and <0.01, respectively) whereas the TP53 tumor suppressor gene was not hypermethylated (P < 0.511). The proportion of PTEN methylation was significantly higher in serum than in the normal tissues and it has a significant correlation to tumor tissues (P < 0.05). mtDNA analysis revealed 36.36% somatic and 90.91% germline mutations in the D-loop region and also significant mtDNA depletion in tumor tissues (P < 0.01). In addition, the mtDNA content in matched serum was significantly lower than in the normal tissues (P < 0.05). These data can provide an insight into the management of a therapeutic approach based on the reversal of epigenetic silencing of the crucial genes involved in regulatory pathways of the tumor suppressor TP53. Additionally, release of significant aberrant methylated PTEN in matched serum samples might represent a promising biomarker for breast cancer.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.