Fragile X mental retardation protein (FMRP) and metabotropic glutamate receptor subtype 5 (mGlu5) control stress granule formation in astrocytes

Fragile X syndrome (FXS) is a common form of intellectual disability and autism caused by the lack of Fragile X Mental Retardation Protein (FMRP), an RNA-binding protein involved in RNA transport and protein synthesis. Upon cellular stress, global protein synthesis is blocked and mRNAs are recruited into stress granules (SGs), together with RNA-binding proteins including FMRP. Activation of group-I metabotropic glutamate (mGlu) receptors stimulates FMRP-mediated mRNA transport and protein synthesis, but their role in SGs formation is unexplored. To this aim, we pre-treated wild type (WT) and Fmr1 knockout (KO) cultured astrocytes with the group-I-mGlu receptor agonist (S)-3,5-Dihydroxyphenylglycine (DHPG) and exposed them to sodium arsenite (NaAsO2), a widely used inducer of SGs formation. In WT cultures the activation of group-I mGlu receptors reduced SGs formation and recruitment of FMRP into SGs, and also attenuated phosphorylation of eIF2α, a key event crucially involved in SGs formation and inhibition of protein synthesis. In contrast, Fmr1 KO astrocytes, which exhibited a lower number of SGs than WT astrocytes, did not respond to agonist stimulation. Interestingly, the mGlu5 receptor negative allosteric modulator (NAM) 2-methyl-6-(phenylethynyl)pyridine (MPEP) antagonized DHPG-mediated SGs reduction in WT and reversed SGs formation in Fmr1 KO cultures. Our findings reveal a novel function of mGlu5 receptor as modulator of SGs formation and open new perspectives for understanding cellular response to stress in FXS pathophysiology

Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs

While the genetics of autism spectrum disorders (ASD) has been intensively studied, resulting in the identification of over 100 putative risk genes, the epigenetics of ASD has received less attention, and results have been inconsistent across studies. We aimed to investigate the contribution of DNA methylation (DNAm) to the risk of ASD and identify candidate biomarkers arising from the interaction of epigenetic mechanisms with genotype, gene expression, and cellular proportions. We performed DNAm differential analysis using whole blood samples from 75 discordant sibling pairs of the Italian Autism Network collection and estimated their cellular composition. We studied the correlation between DNAm and gene expression accounting for the potential effects of different genotypes on DNAm. We showed that the proportion of NK cells was significantly reduced in ASD siblings suggesting an imbalance in their immune system. We identified differentially methylated regions (DMRs) involved in neurogenesis and synaptic organization. Among candidate loci for ASD, we detected a DMR mapping to CLEC11A (neighboring SHANK1) where DNAm and gene expression were significantly and negatively correlated, independently from genotype effects. As reported in previous studies, we confirmed the involvement of immune functions in the pathophysiology of ASD. Notwithstanding the complexity of the disorder, suitable biomarkers such as CLEC11A and its neighbor SHANK1 can be discovered using integrative analyses even with peripheral tissues

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Nonlinear EEG analysis during sleep in premature and full-term newborns

Objective: Recently it has been shown that the adult sleep EEG is mostly determined by high-dimensional, linear dynamics with the exception of the A phase of the cyclic alternating pattern which displays more synchronized nonlinear dynamics. It is not known how these two different types of brain dynamics develop in early life; for this reason the aim of this study was that to extend the nonlinear analysis to the EEG during sleep recorded in premature and full-term newborns. Methods: EEG epochs were chosen from a total of 24 polygraphic recordings from 14 babies (9 males and 5 females) aged between 33 weeks 3 days and 4 months conceptional age. All subjects were neurologically normal and showed normal psychomotor development at follow-up. A total of 243 artifact-free epochs was chosen during active sleep (AS, 74 total epochs), quiet sleep (QS, 76 total epochs) and indeterminate sleep (IS, 93 total epochs). The dynamic properties of the EEG were assessed by means of the nonlinear cross prediction test which uses 3 different 'model' time series in order to predict nonlinearly the original data set (Pred, Ama, and Tir). Pred is a measure of the predictability of the time series, and Ama and Tir are measures of asymmetry, indicating nonlinear structure. Results: Our results show that the structure of sleep EEG in newborns is significantly different from that of adults, it cannot be distinguished from that of high-dimensional noise in the majority of epochs, and shows a tendency to become nonlinear in nature, mostly during QS, in a small percentage of the epochs analyzed. Conclusions: These findings can be interpreted as the effect of immature synaptic interconnections between neurons in the newborn brain