Mixed Negative Binomial Distribution by Weighted Gamma Mixing Distribution

Павел Т. Стойнов - В тази работа се разглежда отрицателно биномното разпределение, известно още като разпределение на Пойа. Предполагаме, че смесващото разпределение е претеглено гама разпределение. Изведени са вероятностите в някои частни случаи. Дадени са рекурентните формули на Панжер.

On the Poisson Process of Order k Space

2010 Mathematics Subject Classification: 60E05, 62P05.

Modelling the size distribution of cosmic voids

The Standard Cosmological Model is generally accepted by the scientific community, there are still an amount of unresolved issues. From the observable characteristics of the structures in the Universe,it should be possible to impose constraints on the cosmological parameters. Cosmic Voids (CV) are a major component of the LSS and have been shown to possess great potential for constraining DE and testing theories of gravity. But a gap between CV observations and theory still persists. A theoretical model for void statistical distribution as a function of size exists (SvdW) However, the SvdW model has been unsuccesful in reproducing the results obtained from cosmological simulations. This undermines the possibility of using voids as cosmological probes. The goal of our thesis work is to cover the gap between theoretical predictions and measured distributions of cosmic voids. We develop an algorithm to identify voids in simulations,consistently with theory. We inspecting the possibilities offered by a recently proposed refinement of the SvdW (the Vdn model, Jennings et al., 2013). Comparing void catalogues to theory, we validate the Vdn model, finding that it is reliable over a large range of radii, at all the redshifts considered and for all the cosmological models inspected. We have then searched for a size function model for voids identified in a distribution of biased tracers. We find that, naively applying the same procedure used for the unbiased tracers to a halo mock distribution does not provide success- full results, suggesting that the Vdn model requires to be reconsidered when dealing with biased samples. Thus, we test two alternative exten- sions of the model and find that two scaling relations exist: both the Dark Matter void radii and the underlying Dark Matter density contrast scale with the halo-defined void radii. We use these findings to develop a semi-analytical model which gives promising results.

Stochastic descriptors to study the fate and potential of naive T cell clonotypes in the periphery

The population of naive T cells in the periphery is best described by determining both its T cell receptor diversity, or number of clonotypes, and the sizes of its clonal subsets. In this paper, we make use of a previously introduced mathematical model of naive T cell homeostasis, to study the fate and potential of naive T cell clonotypes in the periphery. This is achieved by the introduction of several new stochastic descriptors for a given naive T cell clonotype, such as its maximum clonal size, the time to reach this maximum, the number of proliferation events required to reach this maximum, the rate of contraction of the clonotype during its way to extinction, as well as the time to a given number of proliferation events. Our results show that two fates can be identified for the dynamics of the clonotype: extinction in the short-term if the clonotype experiences too hostile a peripheral environment, or establishment in the periphery in the long-term. In this second case the probability mass function for the maximum clonal size is bimodal, with one mode near one and the other mode far away from it. Our model also indicates that the fate of a recent thymic emigrant (RTE) during its journey in the periphery has a clear stochastic component, where the probability of extinction cannot be neglected, even in a friendly but competitive environment. On the other hand, a greater deterministic behaviour can be expected in the potential size of the clonotype seeded by the RTE in the long-term, once it escapes extinction.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Chiral-symmetry Breaking and Pion Structure in the Covariant Spectator Theory

We introduce a covariant approach in Minkowski space for the description of quarks and mesons that exhibits both chiral-symmetry breaking and confinement. In a simple model for the interquark interaction, the quark mass function is obtained and used in the calculation of the pion form factor. We study the effects of the mass function and the different quark pole contributions on the pion form factor.

An efficient Jeans modelling of axisymmetric galaxies with multiple stellar components

Dynamical models of stellar systems represent a powerful tool to study their internal structure and dynamics, to interpret the observed morphological and kinematical fields, and also to support numerical simulations of their evolution. We present a method especially designed to build axisymmetric Jeans models of galaxies, assumed as stationary and collisionless stellar systems. The aim is the development of a rigorous and flexible modelling procedure of multicomponent galaxies, composed of different stellar and dark matter distributions, and a central supermassive black hole. The stellar components, in particular, are intended to represent different galaxy structures, such as discs, bulges, halos, and can then have different structural (density profile, flattening, mass, scale-length), dynamical (rotation, velocity dispersion anisotropy), and population (age, metallicity, initial mass function, mass-to-light ratio) properties. The theoretical framework supporting the modelling procedure is presented, with the introduction of a suitable nomenclature, and its numerical implementation is discussed, with particular reference to the numerical code JASMINE2, developed for this purpose. We propose an approach for efficiently scaling the contributions in mass, luminosity, and rotational support, of the different matter components, allowing for fast and flexible explorations of the model parameter space. We also offer different methods of the computation of the gravitational potentials associated of the density components, especially convenient for their easier numerical tractability. A few galaxy models are studied, showing internal, and projected, structural and dynamical properties of multicomponent galaxies, with a focus on axisymmetric early-type galaxies with complex kinematical morphologies. The application of galaxy models to the study of initial conditions for hydro-dynamical and $N$-body simulations of galaxy evolution is also addressed, allowing in particular to investigate the large number of interesting combinations of the parameters which determine the structure and dynamics of complex multicomponent stellar systems.

Augmented β-cell Function And Mass In Glucocorticoid-treated Rodents Are Associated With Increased Islet Ir-β /akt/mtor And Decreased Ampk/acc And As160 Signaling.

Glucocorticoid (GC) therapies may adversely cause insulin resistance (IR) that lead to a compensatory hyperinsulinemia due to insulin hypersecretion. The increased β-cell function is associated with increased insulin signaling that has the protein kinase B (AKT) substrate with 160 kDa (AS160) as an important downstream AKT effector. In muscle, both insulin and AMP-activated protein kinase (AMPK) signaling phosphorylate and inactivate AS160, which favors the glucose transporter (GLUT)-4 translocation to plasma membrane. Whether AS160 phosphorylation is modulated in islets from GC-treated subjects is unknown. For this, two animal models, Swiss mice and Wistar rats, were treated with dexamethasone (DEX) (1 mg/kg body weight) for 5 consecutive days. DEX treatment induced IR, hyperinsulinemia, and dyslipidemia in both species, but glucose intolerance and hyperglycemia only in rats. DEX treatment caused increased insulin secretion in response to glucose and augmented β-cell mass in both species that were associated with increased islet content and increased phosphorylation of the AS160 protein. Protein AKT phosphorylation, but not AMPK phosphorylation, was found significantly enhanced in islets from DEX-treated animals. We conclude that the augmented β-cell function developed in response to the GC-induced IR involves inhibition of the islet AS160 protein activity.

Mass balance characterisation of Al-7Si-Mg alloy microstructures as a function of solution treatment time

Mass balance calculations were performed to model the effect of solution treatment time on A356 and A357 alloy microstructures. Image analysis and electron probe microanalysis were used to characterise microstructures and confirm model predictions. In as-cast microstructures, up to 8 times more Mg is tied up in the pi-phase than in Mg2Si. The dissolution of pi is accompanied by a corresponding increase in the amount of beta-phase. This causes the rate of pi dissolution to be limited by the rate of beta formation. It is predicted that solution treatments of the order of tens of minutes at 540degreesC produce near-maximum T6 yield strengths, and that Mg contents in excess of 0.52 wt% have no advantage.

Selective disruption of Tcf7l2 in the pancreatic β cell impairs secretory function and lowers β cell mass.

Type 2 diabetes (T2D) is characterized by β cell dysfunction and loss. Single nucleotide polymorphisms in the T-cell factor 7-like 2 (TCF7L2) gene, associated with T2D by genome-wide association studies, lead to impaired β cell function. While deletion of the homologous murine Tcf7l2 gene throughout the developing pancreas leads to impaired glucose tolerance, deletion in the β cell in adult mice reportedly has more modest effects. To inactivate Tcf7l2 highly selectively in β cells from the earliest expression of the Ins1 gene (∼E11.5) we have therefore used a Cre recombinase introduced at the Ins1 locus. Tcfl2(fl/fl)::Ins1Cre mice display impaired oral and intraperitoneal glucose tolerance by 8 and 16 weeks, respectively, and defective responses to the GLP-1 analogue liraglutide at 8 weeks. Tcfl2(fl/fl)::Ins1Cre islets displayed defective glucose- and GLP-1-stimulated insulin secretion and the expression of both the Ins2 (∼20%) and Glp1r (∼40%) genes were significantly reduced. Glucose- and GLP-1-induced intracellular free Ca(2+) increases, and connectivity between individual β cells, were both lowered by Tcf7l2 deletion in islets from mice maintained on a high (60%) fat diet. Finally, analysis by optical projection tomography revealed ∼30% decrease in β cell mass in pancreata from Tcfl2(fl/fl)::Ins1Cre mice. These data demonstrate that Tcf7l2 plays a cell autonomous role in the control of β cell function and mass, serving as an important regulator of gene expression and islet cell coordination. The possible relevance of these findings for the action of TCF7L2 polymorphisms associated with Type 2 diabetes in man is discussed.

Control of pancreatic β-cell mass and function by gluco-incretin hormones : identification of novel regulatory mechanisms for the treatment of diabetes

Summary : Control of pancreatic ß-cell mass and function by gluco-incretin hormones: Identification of novel regulatory mechanisms for the treatment of diabetes The ß-cells of islets of Langerhans secrete insulin to reduce hyperglycemia. The number of pancreatic islet ß-cells and their capacity to secrete insulin is modulated in normal physiological conditions to respond to the metabolic demand of the organism. A failure of the endocrine pancreas to maintain an adequate insulin secretory capacity due to a reduced ß-cell number and function underlies the pathogenesis of both type 1 and type 2 diabetes. The molecular mechanisms controlling the glucose competence of mature ß-cells, i.e., the magnitude of their insulin secretion response to glucose, ß-cell replication, their differentiation from precursor cells and protection against apoptosis are poorly understood. To investigate these mechanisms, we studied the effects on ß-cells of the gluco-incretin hormones, glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) which are secreted by intestinal endocrine cells after food intake. Besides acutely potentiating glucose-stimulated insulin secretion, these hormones induce ß-cell differentiation from precursor cells, stimulate mature ß-cell replication, and protect them against apoptosis. Therefore, understanding the molecular basis for gluco-incretin action may lead to the uncovering of novel ß-cell regulatory events with potential application for the treatment or prevention of diabetes. Islets from mice with inactivation of both GIP and GLP-1 receptor genes (dK0) present a defect in glucose-induced insulin secretion and are more sensitive than control islets to cytokine-induced apoptosis. To search for regulatory genes, that may control both glucose competence and protection against apoptosis, we performed comparative transcriptomic analysis of islets from control and dK0 mice. We found a strong down-regulation of the IGF1 Rexpression in dK0 islets. We demonstrated in both a mouse insulin-secreting cell line and primary islets, that GLP-1 stimulated IGF-1R expression and signaling. Importantly, GLP-1induced IGF-1R-dependent Akt phosphorylation required active secretion, indicating the presence of an autocrine activation mechanism. We further showed that activation of IGF-1R signaling was dependent on the secretion of IGF-2 and IGF-2 expression was regulated by nutrients. Finally, we demonstrated that the IGF-Z/IGF-1R autocrine loop was required for GLP-1 i) to protect ß-cells against cytokine-induced apoptosis, ii) to enhance their glucose competence and iii) to increase ß-cell proliferation. Résumé : Contrôle de la masse des cellules ß pancréatiques et de leur fonction par les hormones glucoincrétines: Identification de nouveaux mécanismes régulateurs pour le traitement du diabète Les cellules ß des îlots de Langerhans sécrètent l'insuline pour diminuer l'hyperglycémie. Le nombre de cellules ß et leur capacité à sécréter l'insuline sont modulés dans les conditions physiologiques normales pour répondre à la demande métabolique de l'organisme. Un échec du pancréas endocrine à maintenir sa capacité sécrétoire d'insuline dû à une diminution du nombre et de la fonction des cellules ß conduit au diabète de type 1 et de type 2. Les mécanismes moléculaires contrôlant la compétence au glucose des cellules ß matures, tels que, l'augmentation de la sécrétion d'insuline en réponse au glucose, la réplication des cellules ß, leur différentiation à partir de cellules précurseurs et la protection contre l'apoptose sont encore peu connus. Afin d'examiner ces mécanismes, nous avons étudié les effets sur les cellules ß des hormones gluco-incrétines, glucose-dépendent insulinotropic polypeptide (G1P) et glucagon-like peptide-1 (GLP-1) qui sont sécrétées par les cellules endocrines de l'intestin après la prise alimentaire. En plus de potentialiser la sécrétion d'insuline induite par le glucose, ces hormones induisent la différentiation de cellules ß à partir de cellules précurseurs, stimulent leur prolifération et les protègent contre l'apoptose. Par conséquent, comprendre les mécanismes d'action des gluco-incrétines permettrait de découvrir de nouveaux processus régulant les cellules ß avec d'éventuelles applications dans le traitement ou la prévention du diabète. Les îlots de souris ayant une double inactivation des gènes pour les récepteurs du GIP et du GLP-1 (dK0) présentent un défaut de sécrétion d'insuline stimulée par le glucose et une sensibilité accrue à l'apoptose induite par les cytokines. Afin de déterminer les gènes régulés, qui pourraient contrôler à la fois la compétence au glucose et la protection contre l'apoptose, nous avons effectué une analyse comparative transcriptomique sur des îlots de souris contrôles et dKO. Nous avons constaté une forte diminution de l'expression d'IGF-1R dans les îlots dKO. Nous avons démontré, à la fois dans une lignée cellulaire murine sécrétant l'insuline et dans îlots primaires, que le GLP-1 stimulait l'expression d'IGF-1R et sa voie de signalisation. Par ailleurs, la phosphorylation d'Akt dépendante d'IGF1-R induite parle GLP-1 nécessite une sécrétion active, indiquant la présence d'un mécanisme d'activation autocrine. Nous avons ensuite montré que l'activation de la voie de signalisation d'IGF-1R était dépendante de la sécrétion d'IGF-2, dont l'expression est régulée par les nutriments. Finalement, nous avons démontré que la boucle autocrine IGF-2/IGF-1R est nécessaire pour le GLP-1 i) pour protéger les cellules ß contre l'apoptose induite par les cytokines, ii) pour améliorer la compétence au glucose et iii) pour augmenter la prolifération des cellules ß. Résumé tout public : Contrôle de la masse des cellules ß pancréatiques et de leur fonction par les hormones gluco-incrétines: Identification de nouveaux mécanismes régulateurs pour le traitement du diabète Chez les mammifères, la concentration de glucose sanguine (glycémie) est régulée et maintenue à une valeur relativement constante d'environ 5 mM. Cette régulation est principalement contrôlée par 2 hormones produites par les îlots pancréatiques de Langerhans: l'insuline sécrétée par les cellules ß et le glucagon sécrété par les cellules a. A la suite d'un repas, l'augmentation de la glycémie entraîne la sécrétion d'insuline ce qui permet le stockage du glucose dans le foie, les muscles et le tissu adipeux afin de diminuer le taux de glucose circulant. Lors d'un jeûne, la diminution de la glycémie permet la sécrétion de glucagon favorisant alors la production de glucose par le foie, normalisant ainsi la glycémie. Le nombre de cellules ß et leur capacité sécrétoire s'adaptent aux variations de la demande métabolique pour assurer une normoglycémie. Une destruction complète ou partielle des cellules ß conduit respectivement au diabète de type 1 et de type 2. Bien que l'augmentation de la glycémie soit le facteur stimulant de la sécrétion d'insuline, des hormones gluco-incrétines, principalement le GLP-1 (glucagon-like peptide-1) et le GIP (glucose-dependent insulinotropic polypeptide) sont libérées par l'intestin en réponse aux nutriments (glucose, acides gras) et agissent au niveau des cellules ß, potentialisant la sécrétion d'insuline induite par le glucose, stimulant leur prolifération, induisant la différentiation de cellules précurseurs en cellules ß matures et les protègent contre la mort cellulaire (apoptose). Afin d'étudier plus en détail ces mécanismes, nous avons généré des souris déficientes pour les récepteurs du GIP et du GLP-l. Les îlots pancréatiques de ces souris présentent un défaut de sécrétion d'insuline stimulée par le glucose et une sensibilité accrue à l'apoptose par rapport aux îlots de souris contrôles. Nous avons donc cherché les gènes régulés pas ces hormones contrôlant la sécrétion d'insuline et la protection contre l'apoptose. Nous avons constaté une forte diminution de l'expression du récepteur à l'IGF-1 (IGF-1R) dans les îlots de souris déficientes pour les récepteurs des gluco-incrétines. Nous avons démontré dans un model de cellules ß en culture et d'îlots que le GLP-1 augmentait l'expression d'IGF-1R et la sécrétion de son ligand (IGF-2) permettant l'activation de la voie de signalisation. Finalement, nous avons montré que l'activation de la boucle IGF-2/IGF-1R induite par le GLP-1 était nécessaire pour la protection contre l'apoptose, l'augmentation de la sécrétion et la prolifération des cellules ß.

NCoR1 is a conserved physiological modulator of muscle mass and oxidative function.

Transcriptional coregulators control the activity of many transcription factors and are thought to have wide-ranging effects on gene expression patterns. We show here that muscle-specific loss of nuclear receptor corepressor 1 (NCoR1) in mice leads to enhanced exercise endurance due to an increase of both muscle mass and of mitochondrial number and activity. The activation of selected transcription factors that control muscle function, such as MEF2, PPARβ/δ, and ERRs, underpins these phenotypic alterations. NCoR1 levels are decreased in conditions that require fat oxidation, resetting transcriptional programs to boost oxidative metabolism. Knockdown of gei-8, the sole C. elegans NCoR homolog, also robustly increased muscle mitochondria and respiration, suggesting conservation of NCoR1 function. Collectively, our data suggest that NCoR1 plays an adaptive role in muscle physiology and that interference with NCoR1 action could be used to improve muscle function.

Glutamine Stimulates Biosynthesis and Secretion of Insulin-like Growth Factor 2 (IGF2), an Autocrine Regulator of Beta Cell Mass and Function.

IGF2 is an autocrine ligand for the beta cell IGF1R receptor and GLP-1 increases the activity of this autocrine loop by enhancing IGF1R expression, a mechanism that mediates the trophic effects of GLP-1 on beta cell mass and function. Here, we investigated the regulation of IGF2 biosynthesis and secretion. We showed that glutamine rapidly and strongly induced IGF2 mRNA translation using reporter constructs transduced in MIN6 cells and primary islet cells. This was followed by rapid secretion of IGF2 via the regulated pathway, as revealed by the presence of mature IGF2 in insulin granule fractions and by inhibition of secretion by nimodipine and diazoxide. When maximally stimulated by glutamine, the amount of secreted IGF2 rapidly exceeded its initial intracellular pool and tolbutamide, and high K(+) increased IGF2 secretion only marginally. This indicates that the intracellular pool of IGF2 is small and that sustained secretion requires de novo synthesis. The stimulatory effect of glutamine necessitates its metabolism but not mTOR activation. Finally, exposure of insulinomas or beta cells to glutamine induced Akt phosphorylation, an effect that was dependent on IGF2 secretion, and reduced cytokine-induced apoptosis. Thus, glutamine controls the activity of the beta cell IGF2/IGF1R autocrine loop by increasing the biosynthesis and secretion of IGF2. This autocrine loop can thus integrate changes in feeding and metabolic state to adapt beta cell mass and function.

Neural regulation of pancreatic islet cell mass and function.

Intracellular glucose signalling pathways control the secretion of glucagon and insulin by pancreatic islet α- and β-cells, respectively. However, glucose also indirectly controls the secretion of these hormones through regulation of the autonomic nervous system that richly innervates this endocrine organ. Both parasympathetic and sympathetic nervous systems also impact endocrine pancreas postnatal development and plasticity in adult animals. Defects in these autonomic regulations impair β-cell mass expansion during the weaning period and β-cell mass adaptation in adult life. Both branches of the autonomic nervous system also regulate glucagon secretion. In type 2 diabetes, impaired glucose-dependent autonomic activity causes the loss of cephalic and first phases of insulin secretion, and impaired suppression of glucagon secretion in the postabsorptive phase; in diabetic patients treated with insulin, it causes a progressive failure of hypoglycaemia to trigger the secretion of glucagon and other counterregulatory hormones. Therefore, identification of the glucose-sensing cells that control the autonomic innervation of the endocrine pancreatic and insulin and glucagon secretion is an important goal of research. This is required for a better understanding of the physiological control of glucose homeostasis and its deregulation in diabetes. This review will discuss recent advances in this field of investigation.

Regulation of mass and function of pancreatic β-cells: identification of anti-apoptotic peptides and role of GLP-1

Résumé La masse de cellules β sécrétrices d'insuline est un tissu dynamique qui s'adapte aux variations de la demande métabolique pour assurer une normoglycémie. Cette adaptation se fait par un changement de sécrétion d'insuline et de la masse totale des cellules β. Une perte complète ou partielle des cellules β conduit respectivement à un diabète de type 1 et de type 2. Les mécanismes qui régulent la masse de cellules β et maintiennent leur phénotype differencié sont encore peu connus. Leur identification est nécessaire pour comprendre le développement du diabète et développer des stratégies de traitement. La greffe d'îlots est une approche thérapeutique prometteuse pour le diabète de type 1, mais est limitée par une perte précoce des cellules β due à une apoptose induite par des cytokines. Afin d'améliorer la survie des cellules β lors de la greffe d'îlots, le premier but était de trouver des peptides pouvant bloquer l'apoptose induite par FasL et TNF-α. Pour ce faire, deux librairies de phages ont été criblées pour sélectionner des peptides se liant au Fas DD ou au TNFRl DD. Nous avons identifié six peptides différents. Cependant, aucun d'entre eux n'était capable de protéger les cellules de l'apoptose induite par FasL ou TNF-α. Deuxièmement, le GLP-1 est une hormone qui stimule la sécrétion d'insuline, et est impliquée dans la prolifération des cellules β, la différentiation, et inhibe l'apoptose. Nous avons fait l'hypothèse que le GLP-1 joue un rôle crucial dans le contrôle de la masse et de la fonction des cellules β. Afin de l'évaluer, une analyse par puce à ADN a été réalisée en comparant des cellules βTC-Tet traitées avec du GLP-1 à des cellules non-traitées. 376 gènes régulés ont été identifiés, dont RGS2, CREM, ICERI et DUSP14, augmentés significativement par le GLP-1. Nous avons confirmé que le GLP-1 augmente l'expression de ces gènes, aussi bien au niveau des transcripts que des protéines. De plus, nous avons montré que le GLP-1 induit leur expression par activation de la voie cAMP/PKA, et nécessite l'entrée de calcium extracellulaire. D'après leur fonction biologique, nous avons ensuite supposé que ces gènes pourraient agir comme régulateurs négatifs de la signalisation du GLP-l, et donc freiner son effet proliférateur. Pour vérifier notre hypothèse, des siRNAs contre ces gènes ont été développés, et leurs effets sur la prolifération des cellules β seront évalués ultérieurement. Abstract The pancreatic β-cell mass is a dynamic tissue which adapts to variations in metabolic demand in order to ensure normoglycemia. This adaptation occurs through a change in both insulin secretion and the total mass of ,β-cells. An absolute or relative loss of β-cells leads to type 1 and type 2 diabetes, respectively. The mechanisms that regulate the pancreatic β-cell mass and maintain the fully differentiated phenotype of the insulin-secreting β-cells are only poorly defined. Their identification is required to understand the progression of diabetes, but also to design strategies for the treatment of diabetes. Islet transplantation is a promising therapeutic approach for type 1 diabetes, but it is still limited by an early graft loss due to cytokine-induced apoptosis. In order to improve β-cell survival during islet transplantation, our first goal was to find novel blockers of FasL- and TNF-α-mediated cell death in the form of peptides. To that end, we screened two phage display libraries to select Fas DD- or TNFR1 DD-binding peptides. We identified six different small peptides. However, none of these peptides was able to prevent cells from FasL- or TNF-α-mediated apoptosis. Secondly, GLP-1 is a hormone that has been shown to stimulate insulin secretion and to be involved in β-cell proliferation, differentiation and inhibition of apoptosis. We hypothesized that GLP-1 plays a crucial role to control mass and function of β-cells. To evaluate this hypothesis, we performed a cDNA microarray analysis with GLP-1-treated βTC-Tet cells compared to untreated cells. We found 376 regulated genes, among these, RGS2, CREM, ICERI and DUSP14, which were significantly upregulated by GLP-1. We confirmed that both their mRNA and protein levels were strongly and rapidly increased after GLP-1 treatment. Moreover, we found that GLP-1 activates their expression mainly through the activation of the cAMP/PKA signaling pathway, and requires extracellular calcium entry. According to their biological function, we then hypothesized that these genes might act as negative regulators of the GLP-1 signaling. In particular, they might brake the effects of GLP-1 on β-cell proliferation. To verify this hypothesis, siRNAs against these genes were developed. The effect of these siRNAs on GLP-1-induced β-cell proliferation will be evaluated later.