Hyperexcitability of the local cortical circuit in mouse models of tuberous sclerosis complex

Tuberous sclerosis complex (TSC) is a neurogenetic disorder associated with epilepsy, intellectual disabilities, and autistic behaviors. These neurological symptoms result from synaptic dysregulations, which shift a balance between excitation and inhibition. To decipher the synaptic substrate of hyperexcitability, we examined pan-neuronal Tsc1 knockout mouse and found a reduction in surface expression of a GABA receptor (GABAR) subunit but not AMPA receptor (AMPAR) subunit. Using electrophysiological recordings, we found a significant reduction in the frequency of GABAR-mediated miniature inhibitory postsynaptic currents (GABAR-mIPSCs) but not AMPAR-mediated miniature excitatory postsynaptic currents (AMPAR-mEPSCs) in layer 2/3 pyramidal neurons. To determine a subpopulation of interneurons that are especially vulnerable to the absence of TSC1 function, we also analyzed two strains of conditional knockout mice targeting two of the prominent interneuron subtypes that express parvalbumin (PV) or somatostatin (SST). Unlike pan-neuronal knockout mice, both interneuron-specific Tsc-1 knockout mice did not develop spontaneous seizures and grew into adults. Further, the properties of AMPAR-mEPSCs and GABAR-mIPSCs were normal in both Pv-Cre and Sst-Cre x Tsc1fl/fl knockout mice. These results indicate that removal of TSC1 from all neurons in a local cortical circuit results in hyperexcitability while connections between pyramidal neurons and interneurons expressing PV and SST are preserved in the layer 2/3 visual cortex. Our study suggests that another inhibitory cell type or a combination of multiple subtypes may be accountable for hyperexcitability in TSC. Keywords: Tuberous sclerosis complex; E/I balance; AMPA receptor; GABA receptor; Autism; Epilepsy; mTOR pathwa

The Interaction between Early Life Epilepsy and Autistic-Like Behavioral Consequences: A Role for the Mammalian Target of Rapamycin (mTOR) Pathway

Early life seizures can result in chronic epilepsy, cognitive deficits and behavioral changes such as autism, and conversely epilepsy is common in autistic children. We hypothesized that during early brain development, seizures could alter regulators of synaptic development and underlie the interaction between epilepsy and autism. The mammalian Target of Rapamycin (mTOR) modulates protein translation and is dysregulated in Tuberous Sclerosis Complex, a disorder characterized by epilepsy and autism. We used a rodent model of acute hypoxia-induced neonatal seizures that results in long term increases in neuronal excitability, seizure susceptibility, and spontaneous seizures, to determine how seizures alter mTOR Complex 1 (mTORC1) signaling. We hypothesized that seizures occurring at a developmental stage coinciding with a critical period of synaptogenesis will activate mTORC1, contributing to epileptic networks and autistic-like behavior in later life. Here we show that in the rat, baseline mTORC1 activation peaks during the first three postnatal weeks, and induction of seizures at postnatal day 10 results in further transient activation of its downstream targets phospho-4E-BP1 (Thr37/46), phospho-p70S6K (Thr389) and phospho-S6 (Ser235/236), as well as rapid induction of activity-dependent upstream signaling molecules, including BDNF, phospho-Akt (Thr308) and phospho-ERK (Thr202/Tyr204). Furthermore, treatment with the mTORC1 inhibitor rapamycin immediately before and after seizures reversed early increases in glutamatergic neurotransmission and seizure susceptibility and attenuated later life epilepsy and autistic-like behavior. Together, these findings suggest that in the developing brain the mTORC1 signaling pathway is involved in epileptogenesis and altered social behavior, and that it may be a target for development of novel therapies that eliminate the progressive effects of neonatal seizures

Management of Low-Grade Glioma

The optimal management of patients with low-grade glioma (LGG) is controversial. The controversy largely stems from the lack of well-designed clinical trials with adequate follow-up to account for the relatively long progression-free survival and overall survival of patients with LGG. Nonetheless, the literature increasingly suggests that expectant management is no longer optimal. Rather, there is mounting evidence supporting active management including consideration of surgical resection, radiotherapy, chemotherapy, molecular and histopathologic characterization, and use of modern imaging techniques for monitoring and prognostication. In particular, there is growing evidence favoring extensive surgical resection and increasing interest in the role of chemotherapy (especially temozolomide) in the management of these tumors. In this review, we critically analyze emerging trends in the literature with respect to management of LGG, with particular emphasis on reports published during the past year

Differential modulatory effects of GSK-3β and HDM2 on sorafenib-induced AIF nuclear translocation (programmed necrosis) in melanoma

<p>Abstract</p> <p>Background</p> <p>GSK-3β phosphorylates numerous substrates that govern cell survival. It phosphorylates p53, for example, and induces its nuclear export, HDM2-dependent ubiquitination, and proteasomal degradation. GSK-3β can either enhance or inhibit programmed cell death, depending on the nature of the pro-apoptotic stimulus. We previously showed that the multikinase inhibitor sorafenib activated GSK-3β and that this activation attenuated the cytotoxic effects of the drug in various BRAF-mutant melanoma cell lines. In this report, we describe the results of studies exploring the effects of GSK-3β on the cytotoxicity and antitumor activity of sorafenib combined with the HDM2 antagonist MI-319.</p> <p>Results</p> <p>MI-319 alone increased p53 levels and p53-dependent gene expression in melanoma cells but did not induce programmed cell death. Its cytotoxicity, however, was augmented in some melanoma cell lines by the addition of sorafenib. In responsive cell lines, the MI-319/sorafenib combination induced the disappearance of p53 from the nucleus, the down modulation of Bcl-2 and Bcl-x_L, the translocation of p53 to the mitochondria and that of AIF to the nuclei. These events were all GSK-3β-dependent in that they were blocked with a GSK-3β shRNA and facilitated in otherwise unresponsive melanoma cell lines by the introduction of a constitutively active form of the kinase (GSK-3β-S9A). These modulatory effects of GSK-3β on the activities of the sorafenib/MI-319 combination were the exact reverse of its effects on the activities of sorafenib alone, which induced the down modulation of Bcl-2 and Bcl-x_Land the nuclear translocation of AIF only in cells in which GSK-3β activity was either down modulated or constitutively low. In A375 xenografts, the antitumor effects of sorafenib and MI-319 were additive and associated with the down modulation of Bcl-2 and Bcl-x_L, the nuclear translocation of AIF, and increased suppression of tumor angiogenesis.</p> <p>Conclusions</p> <p>Our data demonstrate a complex partnership between GSK-3β and HDM2 in the regulation of p53 function in the nucleus and mitochondria. The data suggest that the ability of sorafenib to activate GSK-3β and alter the intracellular distribution of p53 may be exploitable as an adjunct to agents that prevent the HDM2-dependent degradation of p53 in the treatment of melanoma.</p

Predictive JET current ramp-up modelling using QuaLiKiz-neural-network

This work applies the coupled JINTRAC and QuaLiKiz-neural-network (QLKNN) model on the ohmic current ramp-up phase of a JET D discharge. The chosen scenario exhibits a hollow T-e profile attributed to core impurity accumulation, which is observed to worsen with the increasing fuel ion mass from D to T. A dynamic D simulation was validated, evolving j, n(e), T-e, T-i, n(Be), n(Ni), and n(W) for 7.25 s along with self-consistent equilibrium calculations, and was consequently extended to simulate a pure T plasma in a predict-first exercise. The light impurity (Be) accounted for Z(eff) while the heavy impurities (Ni, W) accounted for Prad. This study reveals the role of transport on the Te hollowing, which originates from the isotope effect on the electron-ion energy exchange affecting T-i. This exercise successfully affirmed isotopic trends from previous H experiments and provided engineering targets used to recreate the D q-profile in T experiments, demonstrating the potential of neural network surrogates for fast routine analysis and discharge design. However, discrepancies were found between the impurity transport behaviour of QuaLiKiz and QLKNN, which lead to notable T-e hollowing differences. Further investigation into the turbulent component of heavy impurity transport is recommended

The Biochemistry, Ultrastructure, and Subunit Assembly Mechanism of AMPA Receptors

The AMPA-type ionotropic glutamate receptors (AMPA-Rs) are tetrameric ligand-gated ion channels that play crucial roles in synaptic transmission and plasticity. Our knowledge about the ultrastructure and subunit assembly mechanisms of intact AMPA-Rs was very limited. However, the new studies using single particle EM and X-ray crystallography are revealing important insights. For example, the tetrameric crystal structure of the GluA2cryst construct provided the atomic view of the intact receptor. In addition, the single particle EM structures of the subunit assembly intermediates revealed the conformational requirement for the dimer-to-tetramer transition during the maturation of AMPA-Rs. These new data in the field provide new models and interpretations. In the brain, the native AMPA-R complexes contain auxiliary subunits that influence subunit assembly, gating, and trafficking of the AMPA-Rs. Understanding the mechanisms of the auxiliary subunits will become increasingly important to precisely describe the function of AMPA-Rs in the brain. The AMPA-R proteomics studies continuously reveal a previously unexpected degree of molecular heterogeneity of the complex. Because the AMPA-Rs are important drug targets for treating various neurological and psychiatric diseases, it is likely that these new native complexes will require detailed mechanistic analysis in the future. The current ultrastructural data on the receptors and the receptor-expressing stable cell lines that were developed during the course of these studies are useful resources for high throughput drug screening and further drug designing. Moreover, we are getting closer to understanding the precise mechanisms of AMPA-R-mediated synaptic plasticity

Peripheral temperature gradient screening of high-Z impurities in optimised 'hybrid' scenario H-mode plasmas in JET-ILW

Screening of high-Z (W) impurities from the confined plasma by the temperature gradient at the plasma periphery of fusion-grade H-mode plasmas has been demonstrated in the JET-ILW (ITER-like wall) tokamak. Through careful optimisation of the hybrid-scenario, deuterium plasmas with sufficient heating power (greater than or similar to 32 MW), high enough ion temperature gradients at the H-mode pedestal top can be achieved for the collisional, neo-classical convection of the W impurities to be directed outwards, expelling them from the confined plasma. Measurements of the W impurity fluxes between and during edge-localised modes (ELMs) based on fast bolometry measurements show that in such plasmas there is a net efflux (loss) between ELMs but that ELMs often allow some W back into the confined plasma. Provided steady, high-power heating is maintained, this mechanism allows such plasmas to sustain high performance, with an average D-D neutron rate of similar to 3.2 x 10(16) s(-1) over a period of similar to 3 s, after an initial overshoot (equivalent to a D-T fusion power of similar to 9.4 MW), without an uncontrolled rise in W impurity radiation, giving added confidence that impurity screening by the pedestal may also occur in ITER, as has previously been predicted (Dux et al 2017 Nucl. Mater. Energy 12 28-35)

First-Principles Density Limit Scaling in Tokamaks Based on Edge Turbulent Transport and Implications for ITER

A first-principles scaling law, based on turbulent transport considerations, and a multimachine database of density limit discharges from the ASDEX Upgrade, JET, and TCV tokamaks, show that the increase of the boundary turbulent transport with the plasma collisionality sets the maximum density achievable in tokamaks. This scaling law shows a strong dependence on the heating power, therefore predicting for ITER a significantly larger safety margin than the Greenwald empirical scaling [Greenwald et al., Nucl. Fusion, 28, 2199 (1988)] in case of unintentional high-to-low confinement transition

Testing a prediction model for the H-mode density pedestal against JET-ILW pedestals

The neutral ionisation model proposed by Groebner et al (2002 Phys. Plasmas 9 2134) to determine the plasma density profile in the H-mode pedestal, is extended to include charge exchange processes in the pedestal stimulated by the ideas of Mahdavi et al (2003 Phys. Plasmas 10 3984). The model is then tested against JET H-mode pedestal data, both in a 'standalone' version using experimental temperature profiles and also by incorporating it in the Europed version of EPED. The model is able to predict the density pedestal over a wide range of conditions with good accuracy. It is also able to predict the experimentally observed isotope effect on the density pedestal that eludes simpler neutral ionization models

Performance Comparison of Machine Learning Disruption Predictors at JET

Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (DEMO) and China Fusion Engineering Test Reactor (CFETR). In the last two decades, a great number of DP systems have been developed using data-driven methods. The performance of the DP models has been improved over the years both for a more appropriate choice of diagnostics and input features and for the availability of increasingly powerful data-driven modelling techniques. However, a direct comparison among the proposals has not yet been conducted. Such a comparison is mandatory, at least for the same device, to learn lessons from all these efforts and finally choose the best set of diagnostic signals and the best modelling approach. A first effort towards this goal is made in this paper, where different DP models will be compared using the same performance indices and the same device. In particular, the performance of a conventional Multilayer Perceptron Neural Network (MLP-NN) model is compared with those of two more sophisticated models, based on Generative Topographic Mapping (GTM) and Convolutional Neural Networks (CNN), on the same real time diagnostic signals from several experiments at the JET tokamak. The most common performance indices have been used to compare the different DP models and the results are deeply discussed. The comparison confirms the soundness of all the investigated machine learning approaches and the chosen diagnostics, enables us to highlight the pros and cons of each model, and helps to consciously choose the approach that best matches with the plasma protection needs