Synapses as therapeutic targets for autism spectrum disorders: an international symposium held in Pavia on july 4th, 2014

New progresses into the molecular and cellular mechanisms of autism spectrum disorders (ASDs) have been discussed in 1 day international symposium held in Pavia (Italy) on July 4th, 2014 entitled “synapses as therapeutic targets for autism spectrum disorders” (satellite of the FENS Forum for Neuroscience, Milan, 2014). In particular, world experts in the field have highlighted how animal models of ASDs have greatly advanced our understanding of the molecular pathways involved in synaptic dysfunction leading sometimes to “synaptic clinical trials” in children. © 2014 Curatolo, Ben-Ari, Bozzi, Catania, D’Angelo, Mapelli, Oberman, Rosenmund and Cherubini

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma ScaleResults: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study.Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations. (C) 2020 The Authors. Published by Elsevier Inc.</p

Galanin Receptor 1 Deletion Exacerbates Hippocampal Neuronal Loss after Systemic Kainate Administration in Mice

Galanin is a neuropeptide with a wide distribution in the central and peripheral nervous systems and whose physiological effects are mediated through three G protein-coupled receptor subtypes, GalR1, GalR2, and GalR3. Several lines of evidence indicate that galanin, as well as activation of the GalR1 receptor, is a potent and effective modulator of neuronal excitability in the hippocampus.In order to test more formally the potential influence of GalR1 on seizure-induced excitotoxic cell death, we conducted functional complementation tests in which transgenic mice that exhibit decreased expression of the GalR1 candidate mRNA underwent kainate-induced status epilepticus to determine if the quantitative trait of susceptibility to seizure-induced cell death is determined by the activity of GalR1. In the present study, we report that reduction of GalR1 mRNA via null mutation or injection of the GalR1 antagonist, galantide, prior to kainate-induced status epilepticus induces hippocampal damage in a mouse strain known to be highly resistant to kainate-induced neuronal injury. Wild-type and GalR1 knockout mice were subjected to systemic kainate administration. Seven days later, Nissl and NeuN immune- staining demonstrated that hippocampal cell death was significantly increased in GalR1 knockout strains and in animals injected with the GalR1 antagonist. Compared to GalR1-expressing mice, GalR1-deficient mice had significantly larger hippocampal lesions after status epilepticus.Our results suggest that a reduction of GalR1 expression in the C57BL/6J mouse strain renders them susceptible to excitotoxic injury following systemic kainate administration. From these results, GalR1 protein emerges as a new molecular target that may have a potential therapeutic value in modulating seizure-induced cell death

Temporal coding at the immature depolarizing gabaergic synapse

In the developing hippocampus, GABA exerts depolarizing and excitatory actions and contributes to the generation of neuronal network driven giant depolarizing potentials (GDPs). Here, we studied spike time coding at immature GABAergic synapses and its impact on synchronization of the neuronal network during GDPs in the neonatal (postnatal days P2-6) rat hippocampal slices. Using extracellular recordings, we found that the delays of action potentials (APs) evoked by synaptic activation of GABA(A) receptors are long (mean, 65 ms) and variable (within a time window of 10-200 ms). During patch-clamp recordings, depolarizing GABAergic responses were mainly subthreshold and their amplification by persistent sodium conductance was required to trigger APs. AP delays at GABAergic synapses shortened and their variability reduced with an increase in intracellular chloride concentration during whole-cell recordings. Negative shift of the GABA reversal potential (EGABA) with low concentrations of bumetanide, or potentiation of GABA(A) receptors with diazepam reduced GDPs amplitude, desynchronized neuronal firing during GDPs and slowed down GDPs propagation. Partial blockade of GABA(A) receptors with bicuculline increased neuronal synchronization and accelerated GDPs propagation. We propose that spike timing at depolarizing GABA synapses is determined by intracellular chloride concentration. At physiological levels of intracellular chloride GABAergic depolarization does not reach the action potential threshold and amplification of GABAergic responses by non-inactivating sodium conductance is required for postsynaptic AP initiation. Slow and variable excitation at GABAergic synapse determines the level of neuronal synchrony and the rate of GDPs propagation in the developing hippocampus. © 2010 Valeeva, Abdullin, Tyzio, Skorinkin, Nikolski, Ben-Ari and Khazipov

Intracellular chloride concentration influences the GABAA receptor subunit composition

GABAA receptors (GABAARs) exist as different subtype variants showing unique functional properties and defined spatio-temporal expression pattern. The molecular mechanisms underlying the developmental expression of different GABAAR are largely unknown. The intracellular concentration of chloride ([Cl−]i), the main ion permeating through GABAARs, also undergoes considerable changes during maturation, being higher at early neuronal stages with respect to adult neurons. Here we investigate the possibility that [Cl−]i could modulate the sequential expression of specific GABAARs subtypes in primary cerebellar neurons. We show that [Cl−]i regulates the expression of α3-1 and δ-containing GABAA receptors, responsible for phasic and tonic inhibition, respectively. Our findings highlight the role of [Cl−]i in tuning the strength of GABAergic responses by acting as an intracellular messenger

Insights into GABA receptor signalling in TM3 Leydig cells

gamma-Aminobutyric acid (GABA) is an emerging signalling molecule in endocrine organs, since it is produced by endocrine cells and acts via GABA(A) receptors in a paracrine/autocrine fashion. Testicular Leydig cells are producers and targets for GABA. These cells express GABA(A) receptor subunits and in the murine Leydig cell line TM3 pharmacological activation leads to increased proliferation. The signalling pathway of GABA in these cells is not known in this study. We therefore attempted to elucidate details of GABA(A) signalling in TM3 and adult mouse Leydig cells using several experimental approaches. TM3 cells not only express GABA(A) receptor subunits, but also bind the GABA agonist {[}H-3] muscimol with a binding affinity in the range reported for other endocrine cells (K-d = 2.740 +/- 0.721 nM). However, they exhibit a low B-max value of 28.08 fmol/mg protein. Typical GABA(A) receptor-associated events, including Cl- currents, changes in resting membrane potential, intracellular Ca2+ or cAMP, were not measurable with the methods employed in TM3 cells, or, as studied in part, in primary mouse Leydig cells. GABA or GABA(A) agonist isoguvacine treatment resulted in increased or decreased levels of several mRNAs, including transcription factors (c-fos, hsf-1, egr-1) and cell cycle-associated genes (Cdk2, cyclin D1). In an attempt to verify the cDNA array results and because egr-1 was recently implied in Leydig cell development, we further studied this factor. RT-PCR and Western blotting confirmed a time-dependent regulation of egr-1 in TM3. In the postnatal testis egr-1 was seen in cytoplasmic and nuclear locations of developing Leydig cells, which bear GABA(A) receptors and correspond well to TM3 cells. Thus, GABA acts via an untypical novel signalling pathway in TM3 cells. Further details of this pathway remain to be elucidated. Copyright (c) 2005 S. Karger AG, Base

Bumetanide for autism: more eye contact, less amygdala activation.

We recently showed that constraining eye contact leads to exaggerated increase of amygdala activation in autism. Here, in a proof of concept pilot study, we demonstrate that administration of bumetanide (a NKCC1 chloride importer antagonist that restores GABAergic inhibition) normalizes the level of amygdala activation during constrained eye contact with dynamic emotional face stimuli in autism. In addition, eye-tracking data reveal that bumetanide administration increases the time spent in spontaneous eye gaze during in a free-viewing mode of the same face stimuli. In keeping with clinical trials, our data support the Excitatory/Inhibitory dysfunction hypothesis in autism, and indicate that bumetanide may improve specific aspects of social processing in autism. Future double-blind placebo controlled studies with larger cohorts of participants will help clarify the mechanisms of bumetanide action in autism

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

ObjectiveWe aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.Study Design and SettingWe performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified.ResultsIn the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study.ConclusionML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations.</p