De novo TBR1 variants cause a neurocognitive phenotype with ID and autistic traits:report of 25 new individuals and review of the literature

TBR1, a T-box transcription factor expressed in the cerebral cortex, regulates the expression of several candidate genes for autism spectrum disorders (ASD). Although TBR1 has been reported as a high-confidence risk gene for ASD and intellectual disability (ID) in functional and clinical reports since 2011, TBR1 has only recently been recorded as a human disease gene in the OMIM database. Currently, the neurodevelopmental disorders and structural brain anomalies associated with TBR1 variants are not well characterized. Through international data sharing, we collected data from 25 unreported individuals and compared them with data from the literature. We evaluated structural brain anomalies in seven individuals by analysis of MRI images, and compared these with anomalies observed in TBR1 mutant mice. The phenotype included ID in all individuals, associated to autistic traits in 76% of them. No recognizable facial phenotype could be identified. MRI analysis revealed a reduction of the anterior commissure and suggested new features including dysplastic hippocampus and subtle neocortical dysgenesis. This report supports the role of TBR1 in ID associated with autistic traits and suggests new structural brain malformations in humans. We hope this work will help geneticists to interpret TBR1 variants and diagnose ASD probands

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Identification et analyse fonctionnelle dans la levure de gènes potentiellement impliqués dans les instabilités chromosomiques

L'instabilité génétique est une caractéristique essentielle des cellules cancéreuses. Si les bases moléculaires de l'instabilité des microsatellites observées dans les tumeurs sont aujourd'hui connues, en revanche, celles de l'instabilité chromosomique à l'origine de l'aneuploïdie ne le sont pas. Les données récentes de la génomique fonctionnelle obtenues dans la levure Saccharomyces cerevisiae, et en particulier la caractérisation du transcriptome en présence de drogues dépolymérisant le fuseau mitotique, permettent d'envisager la caractérisation extensive des gènes intervenant dans le point de contrôle du fuseau mitotique. Nous avons étudié l'implication dans la ségrégation chromosomique du gène CBK1, homologue du gène suppresseur de tumeur LATS et des gènes YDR372c et YMR131 c dont l'expression est induite lorsque le point de contrôle de fuseau mitotique est activé. L'invalidation dans la levure de ces gènes et l'analyse fonctionnelle des souches déficientes a démontré que le gène YMR131 c intervenait dans le point de contrôle du fuseau mitotique et que son inactivation entraînait une instabilité chromosomique. Si le gène CBK1 ne semble pas intervenir dans le point de contrôle du fuseau mitotique, en revanche les résultats obtenus suggèrent son implication dans la cytokinèse. La construction des souches de levure déficientes pour ces gènes permet d'envisager le développement d'essais fonctionnels pour rechercher des altérations des homologues humains dans les tumeurs. Nos résultats démontrent l'intérêt de la génomique fonctionnelle de la levure pour identifier et caractériser les gènes impliqués dans la ségrégation chromosomique.ROUEN-BU Médecine-Pharmacie (765402102) / SudocSudocFranceF

Modélisation du Signal TGF-b : De lObtention des données à la Simulation

Le TGF-b est une cytokine qui possède de nombreuses fonctions biologiques ; croissance, différenciation cellulaire, régulation de la réponse inflammatoire, sécrétion de la matrice extracellulaire et apoptose. La fixation du TGF-b sur des récepteurs membranaires de type thréonine kinases, déclenche une cascade de phosphorylation des protéines SMAD dans le cytoplasme, celles-ci migrent jusqu'au noyau où elles régulent la transcription de gènes cibles. Dans le contexte du cancer le TGF-b présente 2 rôles antagonistes à la fois suppresseur et promoteur de tumeur. C'est en effet un inhibiteur de la prolifération cellulaire mais il peut également être un puissant mitogène au cours de la tumorigénèse. La voie de signalisation dépendante des SMADs s'inscrit au sein d'un réseau complexe impliquant de nombreuses protéines et la compréhension de la dynamique de la signalisation du TGF-b nécessite des approches de modélisation. Dans ce projet, nous avons d'une part travaillé sur un modèle continu différentiel afin d'analyser l'importance du transport nucléocytoplasmique des SMADs sur le signal induit par le TGF-b et d'étudier plus particulièrement le rôle de l'ubiquitine ligase TIF1g dans la dynamique de la protéine SMAD4. D'autre part afin de développer un modèle global de la voie de signalisation du TGF-b, nous avons exploré une nouvelle approche de modélisation discrète des mécanismes biologiques à l'aide du formalisme issu des statecharts. Ce dernier est implémenté dans un logiciel de modélisation en temps discret qui permet de réaliser des simulations pour obtenir l'évolution des entités au cours du temps sous forme de chronogramme. L'intérêt de cette notion de temps sera discuté face à d'autres méthodes emmenant de la littérature

Modeling the influence of EGF and TGF-b pathways in tumor progression of hepatocellular carcinoma.

International audienceBackground: Based on two major signaling pathways, EGF (Epidermic Growth Factor) and TGF-b (Transforming Growth Factor), altered during tumor progression of hepatocellular carcinoma, we propose to combine biological and computational analysis to decipher complexity of the tumor progression and identify new targets aimed to inhibit, diminish or reverse the tumoral phenotype. This project lies within the scope of Systems biology to shift towards a more global picture of the cellular mechanisms than with the gene-based approaches for instance and this in order to understand how cellular components work together as a connected system. Methods and Results: By using experimental data and online public databases, we develop an integrative model for cell cycle and signaling pathways. We propose discrete multi-clock models to characterize the influence of the EGF and TGF-b pathways in controlling cell proliferation and consequently tumor progression in the liver. Discrete models are especially suitable for qualitative biological data, which constitute most of the regulatory interaction data available. As each signal within a pathway follows its own clock, we now introduce multi-clock technique to model the dynamic of the biological interactions. We validated our approach with published cell cycle models and we evaluate the robustness of hepatocellular carcinoma cell line by using data from RNA interference experiments to constraint our model. This might help identify pathway checkpoints and buffering effects between different paths of the EGF and TGF-b pathways network, allowing to design news markers and new therapeutically targets for hepatocellular carcinomas

Multiclock discrete models of the eukaryotic cell cycle.

Journée satellite "Modélisation dynamique et simulation des réseaux biologiques "Among the various formalisms proposed to model biological system, the discrete models are especially appealing because the modeled events can easily reflect biological observations. It also benefits from a wealth of work done in circuit and program verification with well-established concepts and languages such as temporal logic, and efficient data structures and decision procedures like Binary Decision Diagrams (BDD) and satisfiability solvers (SAT solvers). In such models each entity (gene, protein, ...) is represented by a finite-state variable and the values reflect their different biological properties. For instance, in discrete boolean models, the value indicates whether a molecule is active or not. The evolution equation between two states of a molecule is then conditioned by the values of the other variables of the system. At each state of the system, some entities of the model can evolve. However this model does not contain any specification on the order of the transitions. This is deferred to the interpretation of the model in a simulator. The commonly used approaches are the synchronous and asynchronous interpretations. In synchronous mode, all the possible changes occur in one evolution step whereas in asynchronous mode, only one change is allowed at each step. The asynchronous interpretation is considered more interesting since it explores all the potential evolutions of the model. Nevertheless, it is often unsatisfactory, i.e. giving too many solutions not always biologically relevant or with questionable trajectories. In an attempt to get finer description of time, some authors introduced the notion of priorities on transitions [1]. However, in all cases, these assumptions are still not part of the model but directives to the simulator. When using model checking, specification of time outside the model becomes a source of problems. The different interpretations of the models must be implemented in the model-checker. With more complex directives on the sequencing of transitions, it is much more difficult to use formal verification methods. Moreover, it is rather difficult, even impossible, to perform computations for control purposes or model fitting. Here, we propose a new formalism to include timing specifications in the models and use a unique interpretation for all models. This formalism is inspired by the formal models underlying real time programming languages such as Esterel, Lustre and Signal [2,3]. Time is the logical time used in computer science: it does not correspond to the duration of events but to their relative sequencing. This allows the description of several biological signals with different clocks, i.e., multiclock systems. To illustrate the power of the formalism, we modeled the core network controlling the mammalian cell cycle. We showed that the standard asynchronous and synchronous interpretations of discrete models are equivalent to particular timing specifications in our formalism. One main improvement of our formalism is its capacity to support model-checking technique for properties involving biological entities and reaction time. This computational power can also be used for enforcing properties paving the way to model fitting and experiment planning. Finally, our example shows that pure timing constraints may not be sufficient to correctly model some biological phenomena. Priorities on reactions (transitions) have been used to finely tune timing conditions. In a forthcoming work we will investigate and implement model-checking approaches to explore our models. [1] A. Faure et al. Bioinformatics, 14:e124-131, 2006. [2] A. Benveniste et al. 40th IEEE Conference on Decision and Control, 2001. [3] A. Benveniste et al. Proceedings of the IEEE, Special issue on Modeling and Design of Embedded Systems, 91:64-83, 2003

Modélisation du Signal TGF-b : De lObtention des données à la Simulation

Le TGF-b est une cytokine qui possède de nombreuses fonctions biologiques ; croissance, différenciation cellulaire, régulation de la réponse inflammatoire, sécrétion de la matrice extracellulaire et apoptose. La fixation du TGF-b sur des récepteurs membranaires de type thréonine kinases, déclenche une cascade de phosphorylation des protéines SMAD dans le cytoplasme, celles-ci migrent jusqu'au noyau où elles régulent la transcription de gènes cibles. Dans le contexte du cancer le TGF-b présente 2 rôles antagonistes à la fois suppresseur et promoteur de tumeur. C'est en effet un inhibiteur de la prolifération cellulaire mais il peut également être un puissant mitogène au cours de la tumorigénèse. La voie de signalisation dépendante des SMADs s'inscrit au sein d'un réseau complexe impliquant de nombreuses protéines et la compréhension de la dynamique de la signalisation du TGF-b nécessite des approches de modélisation. Dans ce projet, nous avons d'une part travaillé sur un modèle continu différentiel afin d'analyser l'importance du transport nucléocytoplasmique des SMADs sur le signal induit par le TGF-b et d'étudier plus particulièrement le rôle de l'ubiquitine ligase TIF1g dans la dynamique de la protéine SMAD4. D'autre part afin de développer un modèle global de la voie de signalisation du TGF-b, nous avons exploré une nouvelle approche de modélisation discrète des mécanismes biologiques à l'aide du formalisme issu des statecharts. Ce dernier est implémenté dans un logiciel de modélisation en temps discret qui permet de réaliser des simulations pour obtenir l'évolution des entités au cours du temps sous forme de chronogramme. L'intérêt de cette notion de temps sera discuté face à d'autres méthodes emmenant de la littérature

Multiclock discrete models of the eukaryotic cell cycle.

Journée satellite "Modélisation dynamique et simulation des réseaux biologiques "Among the various formalisms proposed to model biological system, the discrete models are especially appealing because the modeled events can easily reflect biological observations. It also benefits from a wealth of work done in circuit and program verification with well-established concepts and languages such as temporal logic, and efficient data structures and decision procedures like Binary Decision Diagrams (BDD) and satisfiability solvers (SAT solvers). In such models each entity (gene, protein, ...) is represented by a finite-state variable and the values reflect their different biological properties. For instance, in discrete boolean models, the value indicates whether a molecule is active or not. The evolution equation between two states of a molecule is then conditioned by the values of the other variables of the system. At each state of the system, some entities of the model can evolve. However this model does not contain any specification on the order of the transitions. This is deferred to the interpretation of the model in a simulator. The commonly used approaches are the synchronous and asynchronous interpretations. In synchronous mode, all the possible changes occur in one evolution step whereas in asynchronous mode, only one change is allowed at each step. The asynchronous interpretation is considered more interesting since it explores all the potential evolutions of the model. Nevertheless, it is often unsatisfactory, i.e. giving too many solutions not always biologically relevant or with questionable trajectories. In an attempt to get finer description of time, some authors introduced the notion of priorities on transitions [1]. However, in all cases, these assumptions are still not part of the model but directives to the simulator. When using model checking, specification of time outside the model becomes a source of problems. The different interpretations of the models must be implemented in the model-checker. With more complex directives on the sequencing of transitions, it is much more difficult to use formal verification methods. Moreover, it is rather difficult, even impossible, to perform computations for control purposes or model fitting. Here, we propose a new formalism to include timing specifications in the models and use a unique interpretation for all models. This formalism is inspired by the formal models underlying real time programming languages such as Esterel, Lustre and Signal [2,3]. Time is the logical time used in computer science: it does not correspond to the duration of events but to their relative sequencing. This allows the description of several biological signals with different clocks, i.e., multiclock systems. To illustrate the power of the formalism, we modeled the core network controlling the mammalian cell cycle. We showed that the standard asynchronous and synchronous interpretations of discrete models are equivalent to particular timing specifications in our formalism. One main improvement of our formalism is its capacity to support model-checking technique for properties involving biological entities and reaction time. This computational power can also be used for enforcing properties paving the way to model fitting and experiment planning. Finally, our example shows that pure timing constraints may not be sufficient to correctly model some biological phenomena. Priorities on reactions (transitions) have been used to finely tune timing conditions. In a forthcoming work we will investigate and implement model-checking approaches to explore our models. [1] A. Faure et al. Bioinformatics, 14:e124-131, 2006. [2] A. Benveniste et al. 40th IEEE Conference on Decision and Control, 2001. [3] A. Benveniste et al. Proceedings of the IEEE, Special issue on Modeling and Design of Embedded Systems, 91:64-83, 2003