Autism Spectrum Disorder Model Mice Induced by Prenatal Exposure to Valproic Acid Exhibit Enhanced Empathy-Like Behavior via Oxytocinergic Signaling.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by core symptoms, including impairments in social behavior and repetitive interests. Recent studies have revealed that individuals with ASD also display decreased empathy, ultimately leading to difficulties in social relationships; however, another report indicated that individuals with ASD have enhanced emotional empathy. Nonetheless, the neurobiological mechanisms underlying altered empathy in individuals with ASD remain unclear. In this study, we assessed empathy-like behaviors in valproic acid (VPA)-treated mice-a mouse model of ASD with observational fear learning. We then investigated the brain regions and signaling systems responsible for the altered empathy-like behaviors in VPA-treated mice. As a result, mice prenatally exposed to VPA displayed increased empathy-like behaviors, which were not attributed to altered sensitivity to auditory stimuli or enhanced memory for pain-related contexts. Immunohistochemical analysis revealed that the number of c-Fos positive oxytocinergic neurons in the paraventricular nucleus of the hypothalamus (PVN) was significantly higher in VPA-treated mice after observational fear learning. Finally, we found that pretreatment with L-368899, an antagonist of the oxytocin receptor, repressed the empathetic behavior in VPA-treated mice. These results suggest that VPA-treated ASD model animals showed increased emotional empathy-like behaviors through the hyperactivation of PVN oxytocinergic neurons for the first time. Further investigation of this hyperactivity will help to identify extrinsic stimuli and the condition which are capable of activation of PVN oxytocinergic neurons and to identify novel approach to enhance oxytocin signaling, which ultimately pave the way to development of novel therapy for ASD.

The Role of Dorsal Raphe Serotonin Neurons in the Balance between Reward and Aversion.

BACKGROUND: Reward processing is fundamental for animals to survive and reproduce. Many studies have shown the importance of dorsal raphe nucleus (DRN) serotonin (5-HT) neurons in this process, but the strongly correlative link between the activity of DRN 5-HT neurons and rewarding/aversive potency is under debate. Our primary objective was to reveal this link using two different strategies to transduce DRN 5-HT neurons. METHODS: For transduction of 5-HT neurons in wildtype mice, adeno-associated virus (AAV) bearing the mouse tryptophan hydroxylase 2 (TPH2) gene promoter was used. For transduction in Tph2-tTA transgenic mice, AAVs bearing the tTA-dependent TetO enhancer were used. To manipulate the activity of 5-HT neurons, optogenetic actuators (CheRiff, eArchT) were expressed by AAVs. For measurement of rewarding/aversive potency, we performed a nose-poke self-stimulation test and conditioned place preference (CPP) test. RESULTS: We found that stimulation of DRN 5-HT neurons and their projections to the ventral tegmental area (VTA) increased the number of nose-pokes in self-stimulation test and CPP scores in both targeting methods. Concomitantly, CPP scores were decreased by inhibition of DRN 5-HT neurons and their projections to VTA. CONCLUSION: Our findings indicate that the activity of DRN 5-HT neurons projecting to the VTA is a key modulator of balance between reward and aversion.

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks.

Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies.