Modélisation de l'impact de la géométrie sur la signalisation électrique et calcique dans les épines dendritiques avec la méthode des éléments finis

La résolution des équations de Poisson-Nernst-Planck (PNP) dans les structures neurales gagne de la reconnaissance comme un outil important pour modéliser le champ électrique et l'évolution des concentrations ioniques dans les compartiments sous microscopiques. Cette approche a l'avantage de ne pas compter sur la simplification des hypothèses généralement faites dans le formalisme de la théorie du câble, comme la localisation du gradient électrique à la membrane ou l'homogénéité des concentrations ioniques dans les sous-domaines. Employant la méthode des éléments finis (FEM), nous appliquons les équations de PNP pour déchiffrer la relation, demeurée insaisissable jusqu'à maintenant, deinsaisissable entre la forme et la fonction des épines dendritiques. Nous montrons que la géométrie des épines (le volume de la tête des épines, la longueur et le rayon du cou des épines) est un déterminant important de la dynamique calcique dans l'épine dendritique, tout en ayant un impact limité sur le signal électrique

Genetic determinants of fungi-induced ROS production are associated with the risk of invasive pulmonary aspergillosis

© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Reactive oxygen species (ROS) are an essential component of the host defense against fungal infections. However, little is known about how common genetic variation affects ROS-mediated antifungal host defense. In the present study, we investigated the genetic factors that regulate ROS production capacity in response to the two human fungal pathogens: Candida albicans and Aspergillus fumigatus. We investigated fungal-stimulated ROS production by immune cells isolated from a population-based cohort of approximately 200 healthy individuals (200FG cohort), and mapped ROS-quantitative trait loci (QTLs). We identified several genetic loci that regulate ROS levels (P < 9.99 × 10-6), with some of these loci being pathogen-specific, and others shared between the two fungi. These ROS-QTLs were investigated for their influence on the risk of invasive pulmonary aspergillosis (IPA) in a disease relevant context. We stratified hematopoietic stem-cell transplant (HSCT) recipients based on the donor's SNP genotype and tested their impact on the risk of IPA. We identified rs4685368 as a ROS-QTL locus that was significantly associated with an increased risk of IPA after controlling for patient age and sex, hematological malignancy, type of transplantation, conditioning regimen, acute graft-versus-host-disease grades III-IV, and antifungal prophylaxis. Collectively, this data provides evidence that common genetic variation can influence ROS production capacity, and, importantly, the risk of developing IPA among HSCT recipients. This evidence warrants further research for patient stratification based on the genetic profiling that would allow the identifications of patients at high-risk for an invasive fungal infection, and who would benefit the most from a preventive strategy.This study was supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 847507 (HDM-FUN). MGN was supported by an ERC Advanced grant (833247) and a Spinoza grant of the Netherlands Association for Scientific Research. VK was supported by a Research Grant [2017] of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) and Hypatia tenure track grant. AC was supported by the Fundação para a Ciência e a Tecnologia (FCT) (UIDB/50026/2020 and UIDP/50026/2020), the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) (NORTE-01-0145-FEDER-000039), and the “la Caixa” Foundation (ID 100010434) and FCT under the agreement LCF/PR/HR17/52190003. CC was supported by FCT (CEECIND/04058/2018 and PTDC/SAU-SER/29,635/2017) and the Gilead Research Scholars Program – Antifungals. SMG was the recipient of a PhD fellowship funded by FCT (SFRH/BD/136,814/2018). MSG was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft - DFG) Emmy Noether Program (project no. 434385622/GR 5617/1-1).info:eu-repo/semantics/publishedVersio

Risk of candidiasis associated with interleukin-17 inhibitors:A real-world observational study of multiple independent sources

BACKGROUND: Biologics directed against the T-helper (Th)-17 pathway have been approved for several inflammatory diseases. Interleukin (IL)-17 is involved in anti-Candida host defense, and clinical trials suggested increased candidiasis incidence during IL-17 inhibitor therapy. We describe the worldwide epidemiology of candidiasis during Th17 inhibitor therapy, and immunological mechanisms involved in candidiasis susceptibility. METHODS: A comprehensive analysis of multiple independent sources reporting Candida adverse events during biologics inhibiting the Th17 pathway was performed. Association between Th17 inhibitors and candidiasis was assessed using safety reports of (1) WHO and (2) EMA, (3) a population-based prescriptions registry, and (4) a psoriasis cohort. In a cohort of psoriasis patients experiencing candidiasis during Th17 inhibitors, Candida killing by immune cells and serum inflammatory proteome were analyzed. FINDINGS: A strong association between IL-17 inhibitors and candidiasis (ROR 10·20) was found in the WHO database, particularly for cutaneous (ROR 12·28), oropharyngeal (ROR 19·18), and esophageal candidiasis (ROR 21·20). Risk was higher relative to TNF-α inhibitors (4–10-fold, depending on candidiasis type), confirmed by EMA reports (16–33-fold), prescriptions registry (2–42-fold), and a psoriasis cohort (3–25-fold). After start of IL-17 inhibitors, patients’ risk of candidiasis requiring antifungals increased 2–16 fold. In the psoriasis cohort, 58% of IL-17 treatment episodes were associated with candidiasis. In Th17 inhibitor recipients, proteins involved in anti-Candida immunity and Candida killing by mononuclear leukocytes were impaired. INTERPRETATION: IL-17 inhibitors are associated with an increased risk of oropharyngeal, esophageal, and cutaneous candidiasis, posing a significant disease burden for IL-17 inhibitor recipients. FUNDING: RadboudUMC

Dysregulated innate and adaptive immune responses discriminate disease severity in COVID-19

The clinical spectrum of COVID-19 varies and the differences in host response characterizing this variation have not been fully elucidated. COVID-19 disease severity correlates with an excessive pro-inflammatory immune response and profound lymphopenia. Inflammatory responses according to disease severity were explored by plasma cytokine measurements and proteomics analysis in 147 COVID-19 patients. Furthermore, peripheral blood mononuclear cell cytokine production assays and whole blood flow cytometry were performed. Results confirm a hyperinflammatory innate immune state, while highlighting hepatocyte growth factor and stem cell factor as potential biomarkers for disease severity. Clustering analysis reveals no specific inflammatory endotypes in COVID-19 patients. Functional assays reveal abrogated adaptive cytokine production (interferon-gamma, interleukin-17 and interleukin-22) and prominent T cell exhaustion in critically ill patients, whereas innate immune responses were intact or hyperresponsive. Collectively, this extensive analysis provides a comprehensive insight into the pathobiology of severe to critical COVID-19 and highlight potential biomarkers of disease severity

Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications

A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in “Appendix”

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Analysis and application of Poisson-Nernst Planck equations in neural structures

Titre de l'écran-titre (visionné le 22 mai 2023)Les modèles mathématiques sont souvent employés en neurosciences pour mieux comprendre le comportement des neurones et des réseaux neuronaux. De nombreux outils mathématiques sont utilisés pour décrire les différents aspects de l'activité et des structures neuronales sur des échelles temporelles et temporelles s'étendant sur plusieurs ordres de grandeur. Par exemple, les systèmes d'équations différentielles ordinaires tels que le modèle de Hodgkin-Huxley sont utilisés depuis plusieurs décennies pour décrire les mécanismes de génération de potentiels d'action dans les neurones. À une échelle spatiale plus grande, les équations aux dérivées partielles (EDP) telles que les équations de Maxwell sont utilisées pour comprendre la distribution du champ électrique sur l'ensemble du cerveau. Un nombre moins important de recherches ont été consacrées à l'étude de la distribution des concentrations ioniques et du champ électrique dans les petites structures neuronales (∼ 1μm) telles que les nœuds de Ranvier, les épines dendritiques ou les vésicules présynaptiques. Une manière de modéliser ces structures est de résoudre le système EDP des équations de Poisson Nernst Planck. Ce système d'équations peut être utilisé pour calculer la distribution des concentrations ioniques en résolvant les équations de Nernst-Planck et résoudre la distribution des champs électriques par l'équation de Poisson. L'avantage d'une telle approche est qu'elle permet d'étudier des structures aux géométries arbitrairement complexes. L'objectif principal de cette thèse est d'utiliser le système d'équations de Poisson Nernst-Planck pour modéliser l'activité des épines dendritiques et des nœuds de Ranvier afin de mieux comprendre les les fluctuations des concentrations ioniques dans ces structures. Une contribution importante du projet projet est l'implémentation d'une méthode numériquement efficace pour résoudre ces équations. En effet, la résolution de l'EDP sur des géométries non triviales peut rapidement devenir coûteuse en termes de calcul ce qui rend important le choix d'une approche numérique efficace. Nous avons utilisé la méthode des éléments finis avec des éléments de second ordre. Notre code est implémenté sur le logiciel MEF++, un code développé par le groupe de recherche GIREF de l'Université Laval. Les deux structures d'intérêt, les épines dendritiques et les nœuds de Ranvier, ont été choisies parce qu'elles jouent des rôles importants dans la signalisation neuronale et parce que leurs fonctions sont susceptibles d'être modulées par des altérations de leurs géométries. Les épines dendritiques sont des structures en forme de champignon qui recouvrent les branches dendritiques. Une grande partie des synapses excitatrices sont situées sur les épines dendritiques et l'on pense donc que ces structures jouent un rôle dans la façon dont le signal électrique est transmis au corps cellulaire du neurone. Nous avons simulé des événements synaptiques se produisant sur des épines de géométries différentes afin de déchiffrer la relation entre leur forme et leur fonction. Les événements survenant au niveau des synapses excitatrices déclenchent deux types de réponses, une dépolarisation électrique et une augmentation de la concentration en calcium. Notre modèle décrit ces deux réponses. Nos simulations suggèrent que la forme des épines dendritiques est un déterminant important de la dynamique du calcium alors que son impact sur la signalisation électrique reste limité sur une large gamme de géométries. Les axones sont des structures filiformes qui transmettent des signaux électriques d'un neurone à d'autres. Les axones sont isolés électriquement par des gaines de myéline qui accélèrent la propagation des signaux. Les nœuds de Ranvier sont de petites sections non myélinisées de l'axone, espacées à des intervalles à peu près réguliers. Ces structures sont caractérisées par une forte densité de canaux commandés par le voltage qui maintiennent l'amplitude du potentiel d'action pendant sa propagation. Nous étudions numériquement l'effet de la longueur du nœud, de l'épaisseur de la myéline et de l'angle que fait la myéline avec le nœud de Ranvier sur la propagation du potentiel électrique dans la membrane de l'axone. Nous montrons que la perte de myéline dans le nœud de Ranvier pourrait avoir un impact important sur les potentiels extracellulaires. La méthodologie développée dans cette thèse pourrait être appliquée à de nombreuses autres structures telles que la fente synaptique ou les vésicules présynaptiques.Mathematical models are often employed in neuroscience to better understand the behaviour of neurons and neural networks. Many mathematical tools are used to describe the different aspects of neural activity and structures over temporal and time scales spanning over several order of magnitudes. For example, systems of ordinary differential equations (ODE's) such as the Hodgkin-Huxley model have been used for several decades to describe the spike generating mechanisms in neurons. On a larger spatial scale, partial differential equations (PDE's) such as Maxwell equations are used to understand the distribution of the electrical field over the whole brain. A lesser amount of research has been devoted to the investigation of the distribution of ionic concentrations and electrical field in small neural structures (∼ 1 μm) such as nodes of Ranvier, dendritic spines or presynaptic vesicles. One way to perform such investigations is to solve the PDE system of Poisson Nernst Planck equations. This system of equations can be used to compute the distribution of ionic concentrations by solving the Nernst-Planck equations and resolve the distribution of electric fields through the Poisson equation. The advantage of such an approach is that it allows the investigation of structures with arbitrarily complex geometries. The main aim of this thesis is to use the Poisson Nernst-Planck system of equations to model the electrical activity of dendritic spines and nodes of Ranvier and to better understand the fluctuations of ionic concentrations in these structures. A significant contribution of the project is the implementation of a numerically efficient way to solve these equations. Indeed, the resolution of PDE on non trivial geometries can rapidly become computationally expensive making the choice of an efficient numerical approach important. We used the finite element method with second order elements. Our code is implemented on the MEF++ software, a code developed by the GIREF research group at Laval University. The two structures of interest, dendritic spines and nodes of Ranvier were chosen because they play important roles in signaling in neural signaling and because their functions is likely to be modulated by alterations in their geometries. Dendritic spines are mushroom like structures covering dendritic branches. A large proportion of excitatory synapses are located on dendritic spines and it is thus believed that these structures play a role in how the electric signal is transmitted to the neuron's cell body. We simulated synaptic events occurring on spines with many different geometries to decipher the elusive relationship between their shape and function. Events at excitatory synapses trigger two types of responses: an electrical depolarization and an increase in calcium concentration. Our model describes these two responses. Our simulations suggest that the shape of the spine is an important determinant of calcium dynamics while its impact on electric signaling remains limited over a wide range of geometries. Axons are wire like structures transmitting electric signals from one neuron to others. Axons are electrically insulated by myelin sheaths which accelerates signal propagation. Nodes of Ranvier are small unmyelinated sections of the axon spaced at roughly regular intervals. Theses structures are characterized by a high density of voltage gated channels which maintain the amplitude of the action potential during its propagation. Numerically, we investigate the effect of the node length, myelin thickness and the angle which the myelin makes with the node of Ranvier on the propagation of electric potential in the membrane of the axon. We show that loss of myelin in the node of Ranvier might have an important impact on extracellular potentials. The methodology developed in this thesis could be applied to many other structures such as the synaptic cleft or presynaptic vesicles