Molecular Autism最新文献

Background: Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology.

Methods: Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24-68 months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work.

Results: Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms.

Limitations: Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures.

Conclusions: This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance.

Background: Defective mitochondria and aberrant brain mitochondrial bioenergetics are consistent features in syndromic intellectual disability disorders, such as Rett syndrome (RTT), a rare neurologic disorder that severely affects mainly females carrying mutations in the X-linked MECP2 gene. A pool of CB1 cannabinoid receptors (CB1R), the primary receptor subtype of the endocannabinoid system in the brain, is located on brain mitochondrial membranes (mtCB1R), where it can locally regulate energy production, synaptic transmission and memory abilities through the inhibition of the intra-mitochondrial protein kinase A (mtPKA). In the present study, we asked whether an overactive mtCB1R-mtPKA signaling might underlie the brain mitochondrial alterations in RTT and whether its modulation by systemic administration of the CB1R inverse agonist rimonabant might improve bioenergetics and cognitive defects in mice modeling RTT.

Methods: Rimonabant (0.3 mg/kg/day, intraperitoneal injections) was administered daily to symptomatic female mice carrying a truncating mutation of the Mecp2 gene and its effects on brain mitochondria functionality, systemic oxidative status, and memory function were assessed.

Results: mtCB1R is overexpressed in the RTT mouse brain. Subchronic treatment with rimonabant normalizes mtCB1R expression in RTT mouse brains, boosts mtPKA signaling, and restores the defective brain mitochondrial bioenergetics, abnormal peripheral redox homeostasis, and impaired cognitive abilities in RTT mice.

Limitations: The lack of selectivity of the rimonabant treatment towards mtCB1R does not allow us to exclude that the beneficial effects exerted by the treatment in the RTT mouse model may be ascribed more broadly to the modulation of CB1R activity and distribution among intracellular compartments, rather than to a selective effect on mtCB1R-mediated signaling. The low sample size of few experiments is a further limitation that has been addressed replicating the main findings under different experimental conditions.

Conclusions: The present data identify mtCB1R overexpression as a novel molecular alteration in the RTT mouse brain that may underlie defective brain mitochondrial bioenergetics and cognitive dysfunction.

Background: Motor difficulties are common in many, but not all, autistic individuals. These difficulties can co-occur with other problems, such as delays in language, intellectual, and adaptive functioning. Biological mechanisms underpinning such difficulties are less well understood. Poor motor skills tend to be more common in individuals carrying highly penetrant rare genetic mutations. Such mechanisms may have downstream consequences of altering neurophysiological excitation-inhibition balance and lead to enhanced behavioral motor noise.

Methods: This study combined publicly available and in-house datasets of autistic (n = 156), typically-developing (TD, n = 149), and developmental coordination disorder (DCD, n = 23) children (age 3-16 years). Autism motor subtypes were identified based on patterns of motor abilities measured from the Movement Assessment Battery for Children 2nd edition. Stability-based relative clustering validation was used to identify autism motor subtypes and evaluate generalization accuracy in held-out data. Autism motor subtypes were tested for differences in motor noise, operationalized as the degree of dissimilarity between repeated motor kinematic trajectories recorded during a simple reach-to-drop task.

Results: Relatively 'high' (n = 87) versus 'low' (n = 69) autism motor subtypes could be detected and which generalize with 89% accuracy in held-out data. The relatively 'low' subtype was lower in general intellectual ability and older at age of independent walking, but did not differ in age at first words or autistic traits or symptomatology. Motor noise was considerably higher in the 'low' subtype compared to 'high' (Cohen's d = 0.77) or TD children (Cohen's d = 0.85), but similar between autism 'high' and TD children (Cohen's d = 0.08). Enhanced motor noise in the 'low' subtype was also most pronounced during the feedforward phase of reaching actions.

Limitations: The sample size of this work is limited. Future work in larger samples along with independent replication is important. Motor noise was measured only on one specific motor task. Thus, a more comprehensive assessment of motor noise on many other motor tasks is needed.

Conclusions: Autism can be split into at least two discrete motor subtypes that are characterized by differing levels of motor noise. This suggests that autism motor subtypes may be underpinned by different biological mechanisms.

Background: Autism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits.

Methods: We used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models.

Results: We found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits.

Limitations: Optical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism.

Conclusions: This study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.

Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.

Background: Individuals with Autism Spectrum Condition (ASC) are characterized by atypicalities in social interactions, compared to Typically Developing individuals (TD). The social motivation theory posits that these difficulties stem from diminished anticipation, reception, and/or learning from social rewards. Although learning from socioemotional outcomes is core to the theory, studies to date have been sparse and inconsistent. This possibly arises from a combination of theoretical, methodological and sample-related issues. Here, we assessed participants' ability to develop a spontaneous preference for actions that lead to desirable socioemotional outcomes (approaching/avoiding of happy/angry individuals, respectively), in an ecologically valid social scenario. We expected that learning abilities would be impaired in ASC individuals, particularly in response to affiliative social feedback.

Method: We ran an online social reinforcement learning task, on two large online cohorts with (n = 274) and without (n = 290) ASC, matched for gender, age and education. Participants had to indicate where they would sit in a waiting room. Each seat was associated with different probabilities of approaching/avoiding emotional individuals. Importantly, the task was implicit, as participants were not instructed to learn, and emotional expressions were never mentioned. We applied both categorical analyses contrasting the ASC and TD groups and dimensional factor analysis on affective questionnaires.

Results: Contrary to our hypothesis, participants showed spontaneous learning from socioemotional outcomes, regardless of their diagnostic group. Yet, when accounting for dimensional variations in autistic traits, as well as depression and anxiety, two main findings emerged among females who failed to develop explicit learning strategies: (1) autism severity in ASC correlated with reduced learning to approach happy individuals; (2) anxiety-depression severity across both ASC and TD participants correlated with reduced learning to approach/avoid happy/angry individuals, respectively.

Conclusions: Implicit spontaneous learning from socioemotional outcomes is not generally impaired in autism but may be specifically associated with autism severity in females with ASC, when they do not have an explicit strategy for adapting to their social environment. Clinical diagnosis and intervention ought to take into account individual differences in their full complexity, including the presence of co-morbid anxiety and depression, when dealing with social atypicalities in autism.

Background: Angelman syndrome (AS) is a rare neurodevelopmental genetic disorder caused by the loss of function of the ubiquitin ligase E3A (UBE3A) gene, affecting approximately 1:15,000 live births. We have recently shown that mitochondrial function in AS is altered during mid to late embryonic brain development leading to increased oxidative stress and enhanced apoptosis of neural precursor cells. However, the overall alterations of metabolic processes are still unknown. Hence, as a follow-up, we aim to investigate the metabolic profiles of wild-type (WT) and AS littermates and to identify which metabolic processes are aberrant in the brain of AS model mice during embryonic development.

Methods: We collected brain tissue samples from mice embryos at E16.5 and performed metabolomic analyses using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. Multivariate and Univariate analyses were performed to determine the significantly altered metabolites in AS mice. Pathways associated with the altered metabolites were identified using metabolite set enrichment analysis.

Results: Our analysis showed that overall, the metabolomic fingerprint of AS embryonic brains differed from those of their WT littermates. Moreover, we revealed a significant elevation of distinct metabolites, such as acetate, lactate, and succinate in the AS samples compared to the WT samples. The elevated metabolites were significantly associated with the pyruvate metabolism and glycolytic pathways.

Limitations: Only 14 metabolites were successfully identified and investigated in the present study. The effect of unidentified metabolites and their unresolved peaks was not determined. Additionally, we conducted the metabolomic study on whole brain tissue samples. Employing high-resolution NMR studies on different brain regions could further expand our knowledge regarding metabolic alterations in the AS brain. Furthermore, increasing the sample size could reveal the involvement of more significantly altered metabolites in the pathophysiology of the AS brain.

Conclusions: Ube3a loss of function alters bioenergy-related metabolism in the AS brain during embryonic development. Furthermore, these neurochemical changes could be linked to the mitochondrial reactive oxygen species and oxidative stress that occurs during the AS embryonic development.