Fast non-genomic effects of progesterone-derived neurosteroids on nociceptive thresholds and pain symptoms

Abstract Fast Inhibitory controls mediated by glycine (GlyRs) and GABAA receptors (GABAARs) play an important role to prevent the apparition of pathological pain symptoms of allodynia and hyperalgesia. The use of positive allosteric modulators of these receptors, specifically expressed in the spinal cord, may represent an interesting strategy to limit or block pain expression. In this study, we have used stereoisomers of progesterone metabolites, acting only via non-genomic effects, in order to evaluate the contribution of GlyRs and GABAARs for the reduction of mechanical and thermal heat hypernociception. We show that 3a neurosteroids were particularly efficient to elevate nociceptive thresholds in naive animal. It also reduced mechanical allodynia and thermal heat hyperalgesia in the carrageenan model of inflammatory pain. This effect is likely to be mediated by GABAA receptors since 3b isomer was inefficient. More interestingly, 3a5b neurosteroid was only efficient on mechanical allodynia while having no effect on thermal heat hyperalgesia. We characterized these paradoxical effects of 3a5b neurosteroid using the strychnine and bicuculline models of allodynia. We clearly show that 3a5b neurosteroid exerts an antinociceptive effect via a positive allosteric modulation of GABAARs but, at the same time, is pronociceptive by reducing GlyR function. This illustrates the importance of the inhibitory amino acid receptor channels and their allosteric modulators in spinal pain processing. Moreover, our results indicate that neurosteroids, which are synthesized in the dorsal horn of the spinal cord and have limited side effects, may be of significant interest in order to treat pathological pain symptoms.

Network epidemiology aimed at risk assessment in infection prevention and control

L’objectif de ma thèse est de proposer des solutions contre la propagation des maladies infectieuses dans des cas précis, en tenant compte de l'évolution des contacts entre les hôtes. Ce travail porte en particulier sur la détermination du seuil épidémique, un indicateur clé du risque épidémique. Il exploite et étend un formalisme mathématique issu de la théorie des réseaux, qui permet de déterminer le seuil épidémique dans des situations réelles, pour en dégager des mesures de santé publique. Un premier projet met en lumière des facteurs à l'origine de la persistance de la brucellose bovine en Italie en dépit des mesures d'éradication en place. L'approche théorique permet de calculer le seuil épidémique dans chaque région du pays à l'aide de données exhaustives sur les déplacements de bovins entre les exploitations italiennes sur plusieurs années, ainsi que des relevés datés de flambées épidémiques. Est ensuite présentée une extension du formalisme qui prend en compte différentes durées moyennes d’infection dans le calcul du seuil épidémique. Ce travail montre dans différents contextes épidémiologiques comment l’hypothèse classique selon laquelle la durée moyenne d’infection est homogène peut biaiser l’estimation du risque épidémique. Cette méthode permet également d'identifier les hôtes d'une population qui sont principalement responsables du risque épidémique global.My doctoral thesis aims to propose solutions against the spread of infectious diseases in specific contexts, taking into account how host contacts evolve in time using a temporal network representation. It focuses on the determination of the epidemic threshold, a key indicator of the epidemic risk. By leveraging and extending a mathematical formalism from network theory, this work enables the computation of the epidemic threshold in real situations in order to identify public health measures. A first project addresses the persistence of bovine brucellosis in Italy despite the existing eradication measures. Using comprehensive data on cattle movements between Italian farms over several years, as well as time-stamped outbreak records, the epidemic threshold computation in each region of the country provides information on regions vulnerability and proposes factors that may explain disease persistence. An extension of the formalism is then presented, including heterogeneous average infectious periods in the epidemic threshold computation. This work shows in different epidemiological contexts how the classical assumption that the average infectious period is the same for all hosts in a population may bias epidemic risk assessments. This method also identifies the hosts in a population that are primarily responsible for the global epidemic risk

Adaptation de l'algorithmique aux architectures parallèles

Dans cette thèse, nous nous intéressons à l'adaptation de l'algorithmique aux architectures parallèles. Les plateformes hautes performances actuelles disposent de plusieurs niveaux de parallélisme et requièrent un travail considérable pour en tirer parti. Les superordinateurs possèdent de plus en plus d'unités de calcul et sont de plus en plus hétérogènes et hiérarchiques, ce qui complexifie d'autant plus leur utilisation.Nous nous sommes intéressés ici à plusieurs aspects permettant de tirer parti des architectures parallèles modernes. Tout au long de cette thèse, plusieurs problèmes de natures différentes sont abordés, de manière plus théorique ou plus pratique selon le cadre et l'échelle des plateformes parallèles envisagées.Nous avons travaillé sur la modélisation de problèmes dans le but d'adapter leur formulation à des solveurs existants ou des méthodes de résolution existantes, en particulier dans le cadre du problème de la factorisation en nombres premiers modélisé et résolu à l'aide d'outils de programmation linéaire en nombres entiers.La contribution la plus importante de cette thèse correspond à la conception d'algorithmes pensés dès le départ pour être performants sur les architectures modernes (processeurs multi-coeurs, Cell, GPU). Deux algorithmes pour résoudre le problème du compressive sensing ont été conçus dans ce cadre : le premier repose sur la programmation linéaire et permet d'obtenir une solution exacte, alors que le second utilise des méthodes de programmation convexe et permet d'obtenir une solution approchée.Nous avons aussi utilisé une bibliothèque de parallélisation de haut niveau utilisant le modèle BSP dans le cadre de la vérification de modèles pour implémenter de manière parallèle un algorithme existant. A partir d'une unique implémentation, cet outil rend possible l'utilisation de l'algorithme sur des plateformes disposant de différents niveaux de parallélisme, tout en ayant des performances de premier ordre sur chacune d'entre elles. En l'occurrence, la plateforme de plus grande échelle considérée ici est le cluster de machines multiprocesseurs multi-coeurs. De plus, dans le cadre très particulier du processeur Cell, une implémentation a été réécrite à partir de zéro pour tirer parti de celle-ci.In this thesis, we are interested in adapting algorithms to parallel architectures. Current high performance platforms have several levels of parallelism and require a significant amount of work to make the most of them. Supercomputers possess more and more computational units and are more and more heterogeneous and hierarchical, which make their use very difficult.We take an interest in several aspects which enable to benefit from modern parallel architectures. Throughout this thesis, several problems with different natures are tackled, more theoretically or more practically according to the context and the scale of the considered parallel platforms.We have worked on modeling problems in order to adapt their formulation to existing solvers or resolution methods, in particular in the context of integer factorization problem modeled and solved with integer programming tools.The main contribution of this thesis corresponds to the design of algorithms thought from the beginning to be efficient when running on modern architectures (multi-core processors, Cell, GPU). Two algorithms which solve the compressive sensing problem have been designed in this context: the first one uses linear programming and enables to find an exact solution, whereas the second one uses convex programming and enables to find an approximate solution.We have also used a high-level parallelization library which uses the BSP model in the context of model checking to implement in parallel an existing algorithm. From a unique implementation, this tool enables the use of the algorithm on platforms with different levels of parallelism, while obtaining cutting edge performance for each of them. In our case, the largest-scale platform that we considered is the cluster of multi-core multiprocessors. More, in the context of the very particular Cell processor, an implementation has been written from scratch to take benefit from it.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

A Simple Compressive Sensing Algorithm for Parallel Many-Core Architectures

International audienceIn this paper we consider the l 1-compressive sensing problem. We propose an algorithm specifically designed to take advantage of shared memory, vectorized, parallel and many-core microprocessors such as the Cell processor, new generation Graphics Processing Units (GPUs) and standard vectorized multi-core processors (e.g. quad-core CPUs). Besides its implementation is easy. We also give evidence of the efficiency of our approach and compare the algorithm on the three platforms, thus exhibiting pros and cons for each of them

Favouring inhibitory synaptic drive mediated by GABA A receptors in the basolateral nucleus of the amygdala efficiently reduces pain symptoms in neuropathic mice

International audienc

Disease persistence on temporal contact networks accounting for heterogeneous infectious periods

International audienceThe infectious period of a transmissible disease is a key factor for disease spread and persistence. Epidemic models on networks typically assume an identical average infectious period for all individuals, thus allowing an analytical treatment. This simplifying assumption is, however, often unrealistic, as hosts may have different infectious periods, due, for instance, to individual host-pathogen interactions or inhomogeneous access to treatment. While previous work accounted for this heterogeneity in static networks, a full theoretical understanding of the interplay of varying infectious periods and time-evolving contacts is still missing. Here, we consider a susceptible-infectious-susceptible epidemic on a temporal network with host-specific average infectious periods, and develop an analytical framework to estimate the epidemic threshold, i.e. the critical transmissibility for disease spread in the host population. Integrating contact data for transmission with outbreak data and epidemiological estimates, we apply our framework to three real-world case studies exploring different epidemic contexts-the persistence of bovine tuberculosis in southern Italy, the spread of nosocomial infections in a hospital, and the diffusion of pandemic influenza in a school. We find that the homogeneous parametrization may cause important biases in the assessment of the epidemic risk of the host population. Our approach is also able to identify groups of hosts mostly responsible for disease diffusion who may be targeted for prevention and control, aiding public health interventions

The Role of Oxytocin in Abnormal Brain Development: Effect on Glial Cells and Neuroinflammation

The neonatal period is critical for brain development and determinant for long-term brain trajectory. Yet, this time concurs with a sensitivity and risk for numerous brain injuries following perinatal complications such as preterm birth. Brain injury in premature infants leads to a complex amalgam of primary destructive diseases and secondary maturational and trophic disturbances and, as a consequence, to long-term neurocognitive and behavioral problems. Neuroinflammation is an important common factor in these complications, which contributes to the adverse effects on brain development. Mediating this inflammatory response forms a key therapeutic target in protecting the vulnerable developing brain when complications arise. The neuropeptide oxytocin (OT) plays an important role in the perinatal period, and its importance for lactation and social bonding in early life are well-recognized. Yet, novel functions of OT for the developing brain are increasingly emerging. In particular, OT seems able to modulate glial activity in neuroinflammatory states, but the exact mechanisms underlying this connection are largely unknown. The current review provides an overview of the oxytocinergic system and its early life development across rodent and human. Moreover, we cover the most up-to-date understanding of the role of OT in neonatal brain development and the potential neuroprotective effects it holds when adverse neural events arise in association with neuroinflammation. A detailed assessment of the underlying mechanisms between OT treatment and astrocyte and microglia reactivity is given, as well as a focus on the amygdala, a brain region of crucial importance for socio-emotional behavior, particularly in infants born preterm

Calcium imaging and BAPTA loading of amygdala astrocytes in mouse brain slices

International audienceAstrocytes are glial cells that exhibit calcium signaling-mediated activity. Here, we present a protocol to monitor and manipulate astrocyte calcium activity from mouse amygdala slices. In the first part of this protocol, we describe the procedure of astrocyte calcium imaging. In the second part, we detail how to disrupt astrocyte calcium activity by patch-clamp-mediated loading of BAPTA. These two approaches are presented separately but they can also be used simultaneously to monitor the effects of disruption on an astrocyte network

Network-based assessment of the vulnerability of Italian regions to bovine brucellosis

International audienceThe endemic circulation of bovine brucellosis in cattle herds has a markedly negative impact on economy, due to decreased fertility, increased abortion rates, reduced milk and meat production. It also poses a direct threat to human health. In Italy, despite the long lasting efforts and the considerable economic investment, complete eradication of this disease still eludes the southern regions, as opposed to the northern regions that are disease-free. Here we introduced a novel quantitative network-based approach able to fully exploit the highly resolved databases of cattle trade movements and outbreak reports to yield estimates of the vulnerability of a cattle market to brucellosis. Tested on the affected regions, the introduced vulnerability indicator was shown to be accurate in predicting the number of bovine brucellosis outbreaks, thus confirming the suitability of our tool for epidemic risk assessment. We evaluated the dependence of regional vulnerability to brucellosis on a set of factors including premises spatial distribution, trading patterns, farming practices, herd market value, compliance to outbreak regulations, and exploring different epidemiological conditions. Animal trade movements were identified as a major route for brucellosis spread between farms, with an additional potential risk attributed to the use of shared pastures. By comparing the vulnerability of disease-free regions in the north to affected regions in the south, we found that more intense trade and higher market value of the cattle sector in the north, likely inducing more efficient biosafety measures, together with poor compliance to trade restrictions following outbreaks in the south were key factors explaining the diverse success in eradicating brucellosis. Our modeling scheme is both synthetic and effective in gauging regional vulnerability to brucellosis persistence. Its general formulation makes it adaptable to other diseases and host species, providing a useful tool for veterinary epidemiology and policy assessment