Explore the vast range of titles available.

Key Points Twin and familial studies reveal that autism spectrum disorder (ASD) traits are highly heritable. The genetic landscape of ASD is made of common and rare variants and can be different from one individual to another. Most of the ASD-risk genes are involved in chromatin remodelling, regulation of protein synthesis and degradation, or synaptic plasticity. In cellular and animal models, mutations in the ASD-risk genes lead to a distortion of typical neuronal connectivity by decreasing or increasing synapse strength or number. Compensatory mechanisms, such as genetic buffering and synaptic homeostasis, could modulate the severity of these mutations. Recent years have seen considerable interest in the genetics of autism spectrum disorder (ASD). In this Review, Thomas Bourgeron examines the genetic architecture of this disorder and how ASD-linked mutations might affect synaptic plasticity, before exploring the synaptic homeostasis hypothesis of ASD. Genetics studies of autism spectrum disorder (ASD) have identified several risk genes that are key regulators of synaptic plasticity. Indeed, many of the risk genes that have been linked to these disorders encode synaptic scaffolding proteins, receptors, cell adhesion molecules or proteins that are involved in chromatin remodelling, transcription, protein synthesis or degradation, or actin cytoskeleton dynamics. Changes in any of these proteins can increase or decrease synaptic strength or number and, ultimately, neuronal connectivity in the brain. In addition, when deleterious mutations occur, inefficient genetic buffering and impaired synaptic homeostasis may increase an individual's risk for ASD.

Abstract The bulk of social neuroscience takes a ‘stimulus-brain’ approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a ‘brain-to-brain’ approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, ‘hyperscanning’ setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such ‘inter-brain connectivity analysis’, resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses.

Autism spectrum disorders (ASDs) are a clinically heterogeneous group of neurodevelopmental disorders characterized by social and communication deficits and repetitive behaviors. In a subset of individuals with ASD, mutations in genes involved in synaptic function have been identified, and this Perspective discusses the evidence from mouse models of ASD that synaptic deficits can be ameliorated in the mature brain. The authors also suggest a strategy for designing more informative clinical trials for ASD therapies that stratify patients according to their specific synaptic mutations. Autism spectrum disorder (ASD) encompasses a range of disorders that are characterized by social and communication deficits and repetitive behaviors. For the majority of affected individuals, the cause of ASD remains unknown, but in at least 20% of the cases, a genetic cause can be identified. There is currently no cure for ASD; however, results from mouse models indicate that some forms of the disorder could be alleviated even at the adult stage. Genes involved in ASD seem to converge on common pathways altering synaptic homeostasis. We propose, given the clinical heterogeneity of ASD, that specific 'synaptic clinical trials' should be designed and launched with the aim of establishing whether phenotype 'reversals' could also occur in humans.

MRI has been extensively used to identify anatomical and functional differences in Autism Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices. We conducted an international challenge to predict ASD diagnosis from MRI data, where we provided preprocessed anatomical and functional MRI data from > 2,000 individuals. Evaluation of the predictions was rigorously blinded. 146 challengers submitted prediction algorithms, which were evaluated at the end of the challenge using unseen data and an additional acquisition site. On the best algorithms, we studied the importance of MRI modalities, brain regions, and sample size. We found evidence that MRI could predict ASD diagnosis: the 10 best algorithms reliably predicted diagnosis with AUC∼0.80 – far superior to what can be currently obtained using genotyping data in cohorts 20-times larger. We observed that functional MRI was more important for prediction than anatomical MRI, and that increasing sample size steadily increased prediction accuracy, providing an efficient strategy to improve biomarkers. We also observed that despite a strong incentive to generalise to unseen data, model development on a given dataset faces the risk of overfitting: performing well in cross-validation on the data at hand, but not generalising. Finally, we were able to predict ASD diagnosis on an external sample added after the end of the challenge (EU-AIMS), although with a lower prediction accuracy (AUC=0.72). This indicates that despite being based on a large multisite cohort, our challenge still produced biomarkers fragile in the face of dataset shifts.

Social interactions in mice are frequently analysed in genetically modified strains in order to get insight of disorders affecting social interactions such as autism spectrum disorders. Different types of social interactions have been described, mostly between females and pups, and between adult males and females. However, we recently showed that social interactions between adult males could also encompass cognitive and motivational features. During social interactions, rodents emit ultrasonic vocalizations (USVs), but it remains unknown if call types are differently used depending of the context and if they are correlated with motivational state. Here, we recorded the calls of adult C57BL/6J male mice in various behavioral conditions, such as social interaction, novelty exploration and restraint stress. We introduced a modulator for the motivational state by comparing males maintained in isolation and males maintained in groups before the experiments. Male mice uttered USVs in all social and non-social situations, and even in a stressful restraint context. They nevertheless emitted the most important number of calls with the largest diversity of call types in social interactions, particularly when showing a high motivation for social contact. For mice maintained in social isolation, the number of calls recorded was positively correlated with the duration of social contacts, and most calls were uttered during contacts between the two mice. This correlation was not observed in mice maintained in groups. These results open the way for a deeper understanding and characterization of acoustic signals associated with social interactions. They can also help evaluating the role of motivational states in the emission of acoustic signals.

Melatonin is a powerful antioxidant and a synchronizer of many physiological processes. Alteration of the melatonin pathway has been reported in circadian disorders, diabetes and autism spectrum disorders (ASD). However, very little is known about the genetic variability of melatonin receptors in humans. Here, we sequenced the melatonin receptor MTNR1A and MTNR1B, genes coding for MT1 and MT2 receptors, respectively, in a large panel of 941 individuals including 295 patients with ASD, 362 controls and 284 individuals from different ethnic backgrounds. We also sequenced GPR50, coding for the orphan melatonin-related receptor GPR50 in patients and controls. We identified six non-synonymous mutations for MTNR1A and ten for MTNR1B. The majority of these variations altered receptor function. Particularly interesting mutants are MT1-I49N, which is devoid of any melatonin binding and cell surface expression, and MT1-G166E and MT1-I212T, which showed severely impaired cell surface expression. Of note, several mutants possessed pathway-selective signaling properties, some preferentially inhibiting the adenylyl cyclase pathway, others preferentially activating the MAPK pathway. The prevalence of these deleterious mutations in cases and controls indicates that they do not represent major risk factor for ASD (MTNR1A case 3.6% vs controls 4.4%; MTNR1B case 4.7% vs 3% controls). Concerning GPR50, we detected a significant association between ASD and two variations, Delta502-505 and T532A, in affected males, but it did not hold up after Bonferonni correction for multiple testing. Our results represent the first functional ascertainment of melatonin receptors in humans and constitute a basis for future structure-function studies and for interpreting genetic data on the melatonin pathway in patients.

Autism, an entirely behavioral diagnosis with no largely understood etiologies and no population-wide biomarkers, contrasts with fragile X syndrome (FXS), a single-gene disorder with definite alterations of gene expression and neuronal morphology. Nevertheless, the behavioral overlap between autism and FXS suggests some overlapping mechanisms. Understanding how the single-gene alteration in FXS plays out within complex genetic and neural network processes may suggest targets for autism research and illustrate strategies for relating autism to more singular genetic syndromes.

Ultrasonic vocalizations (USVs) are used as a phenotypic marker in mouse models of neuropsychiatric disorders. Nevertheless, current methodologies still require time-consuming manual input or sound recordings clean of any background noise. We developed a method to overcome these two restraints to boost knowledge on mouse USVs. The methods are freely available and the USV analysis runs online at https://usv.pasteur.cloud . As little is currently known about usage and structure of ultrasonic vocalizations during social interactions over the long-term and in unconstrained context, we investigated mouse spontaneous communication by coupling the analysis of USVs with automatic labeling of behaviors. We continuously recorded during 3 days undisturbed interactions of same-sex pairs of C57BL/6J sexually naive males and females at 5 weeks and 3 and 7 months of age. In same-sex interactions, we observed robust differences between males and females in the amount of USVs produced, in the acoustic structure and in the contexts of emission. The context-specific acoustic variations emerged with increasing age. The emission of USVs also reflected a high level of excitement during social interactions. We finally highlighted the importance of studying long-term spontaneous communication by investigating female mice lacking Shank3 , a synaptic protein associated with autism. While the previous short-time constrained investigations could not detect USV emission abnormalities, our analysis revealed robust differences in the usage and structure of the USVs emitted by mutant mice compared to wild-type female pairs.

Autism spectrum disorders (ASD) are characterized by a wide genetic and clinical heterogeneity. However, some biochemical impairments, including decreased melatonin (crucial for circadian regulation) and elevated platelet N-acetylserotonin (the precursor of melatonin) have been reported as very frequent features in individuals with ASD. To address the mechanisms of these dysfunctions, we investigated melatonin synthesis in post-mortem pineal glands - the main source of melatonin (9 patients and 22 controls) - and gut samples - the main source of serotonin (11 patients and 13 controls), and in blood platelets from 239 individuals with ASD, their first-degree relatives and 278 controls. Our results elucidate the enzymatic mechanism for melatonin deficit in ASD, involving a reduction of both enzyme activities contributing to melatonin synthesis (AANAT and ASMT), observed in the pineal gland as well as in gut and platelets of patients. Further investigations suggest new, post-translational (reduced levels of 14-3-3 proteins which regulate AANAT and ASMT activities) and post-transcriptional (increased levels of miR-451, targeting 14-3-3ζ) mechanisms to these impairments. This study thus gives insights into the pathophysiological pathways involved in ASD.

Phelan-McDermid syndrome (PMS) was initially called the 22q13 deletion syndrome based on its etiology as a deletion of the distal long arm of chromosome 22. These included terminal and interstitial deletions, as well as other structural rearrangements. Later, pathogenetic variants and deletions of the SHANK3 gene were found to result in a phenotype consistent with PMS. The association between SHANK3 and PMS led investigators to consider disruption/deletion of SHANK3 to be a prerequisite for diagnosing PMS. This narrow definition of PMS based on the involvement of SHANK3 has the adverse effect of causing patients with interstitial deletions of chromosome 22 to “lose” their diagnosis. It also results in underreporting of individuals with interstitial deletions of 22q13 that preserve SHANK3. To reduce the confusion for families, clinicians, researchers, and pharma, a simple classification for PMS has been devised. PMS and will be further classified as PMS- SHANK3 related or PMS- SHANK3 unrelated. PMS can still be used as a general term, but this classification system is inclusive. It allows researchers, regulatory agencies, and other stakeholders to define SHANK3 alterations or interstitial deletions not affecting the SHANK3 coding region.