Molecular Autism最新文献

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by atypical patterns of social functioning and repetitive/restricted behaviors. ASD commonly co-occurs with ADHD and, despite their clinical distinctiveness, the two share considerable genetic overlap. Given their shared genetic liability, it is unclear which genetic pathways increase the likelihood of ASD independently of ADHD.

Methods: We applied Genomic Structural Equation Modeling (SEM) to GWAS summary statistics for ASD and childhood-diagnosed ADHD, decomposing the genetic variance for ASD into that which is unique to ASD (uASD) and that which is shared with ADHD. We computed genetic correlations between uASD and 83 external traits to estimate genetic overlap between uASD and other clinically relevant phenotypes. We went on to apply Stratified Genomic SEM to identify classes of genes enriched for uASD. Finally, we implemented Transcriptome-Wide SEM (T-SEM) to explore patterns of gene-expression associated with uASD.

Results: We observed positive genetic correlations between uASD and several external traits, most notably those relating to cognitive/educational outcomes and internalizing psychiatric traits. Stratified Genomic SEM showed that heritability for uASD was significantly enriched in genes involved in evolutionarily conserved processes, as well as for a histone mark in the germinal matrix. T-SEM revealed 83 unique genes with expression associated with uASD, 34 of which were novel with respect to univariate analyses. These genes were overrepresented in skin-related pathologies.

Limitations: Our study was limited by summary statistics derived exclusively from individuals of European ancestry. Additionally, using data based on a general ASD diagnosis limits our ability to understand genetic factors contributing to the pronounced clinical heterogeneity in ASD.

Conclusions: Our findings delineate the unique genetic underpinnings of ASD that are independent of ADHD at the genome-wide, functional, and gene expression level of analysis. In addition, we identify novel associations previously masked by their diametric effects on ADHD. Collectively, these results provide insight into the processes that make ASD biologically unique.

Background: Autistic-like traits (ALT) are prevalent across the general population and might be linked to some facets of a broader autism spectrum disorder (ASD) phenotype. Recent studies suggest an association of these traits with both genetic and brain structural markers in non-autistic individuals, showing similar spatial location of findings observed in ASD and thus suggesting a potential neurobiological continuum.

Methods: In this study, we first tested an association of ALTs (assessed with the AQ questionnaire) with cortical complexity, a cortical surface marker of early neurodevelopment, and then the association with disrupted functional connectivity. We analysed structural T1-weighted and resting-state functional MRI scans in 250 psychiatrically healthy individuals without a history of early developmental disorders, in a first step using the CAT12 toolbox for cortical complexity analysis and in a second step we used regional cortical complexity findings to apply the CONN toolbox for seed-based functional connectivity analysis.

Results: Our findings show a significant negative correlation of both AQ total and AQ attention switching subscores with left superior temporal sulcus (STS) cortical folding complexity, with the former being significantly correlated with STS to left lateral occipital cortex connectivity, while the latter showed significant positive correlation of STS to left inferior/middle frontal gyrus connectivity (n = 233; all p < 0.05, FWE cluster-level corrected). Additional analyses also revealed a significant correlation of AQ attention to detail subscores with STS to left lateral occipital cortex connectivity.

Limitations: Phenotyping might affect association results (e.g. choice of inventories); in addition, our study was limited to subclinical expressions of autistic-like traits.

Conclusions: Our findings provide further evidence for biological correlates of ALT even in the absence of clinical ASD, while establishing a link between structural variation of early developmental origin and functional connectivity.

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder associated with alterations in structural and functional coupling in gray matter. However, despite the detectability and modulation of brain signals in white matter, the structure-function coupling in white matter in autism remains less explored.

Methods: In this study, we investigated structural-functional coupling in white matter (WM) regions, by integrating diffusion tensor data that contain fiber orientation information from WM tracts, with functional connectivity tensor data that reflect local functional anisotropy information. Using functional and diffusion magnetic resonance images, we analyzed a cohort of 89 ASD and 63 typically developing (TD) individuals from the Autism Brain Imaging Data Exchange II (ABIDE-II). Subsequently, the associations between structural-functional coupling in WM regions and ASD severity symptoms assessed by Autism Diagnostic Observation Schedule-2 were examined via supervised machine learning in an independent test cohort of 29 ASD individuals. Furthermore, we also compared the performance of multi-model features (i.e. structural-functional coupling) with single-model features (i.e. functional or structural models alone).

Results: In the discovery cohort (ABIDE-II), individuals with ASD exhibited widespread reductions in structural-functional coupling in WM regions compared to TD individuals, particularly in commissural tracts (e.g. corpus callosum), association tracts (sagittal stratum), and projection tracts (e.g. internal capsule). Notably, supervised machine learning analysis in the independent test cohort revealed a significant correlation between these alterations in structural-functional coupling and ASD severity scores. Furthermore, compared to single-model features, the integration of multi-model features (i.e., structural-functional coupling) performed best in predicting ASD severity scores.

Conclusion: This work provides novel evidence for atypical structural-functional coupling in ASD in white matter regions, further refining our understanding of the critical role of WM networks in the pathophysiology of ASD.

Background: Phelan-McDermid syndrome (PMS) is a rare neurodevelopmental disorder caused by 22q13 deletions that include the SHANK3 gene or pathogenic sequence variants in SHANK3. It is characterized by global developmental delay, intellectual disability, speech impairment, autism spectrum disorder, and hypotonia; other variable features include epilepsy, brain and renal malformations, and mild dysmorphic features. Here, we conducted genotype-phenotype correlation analyses using the PMS International Registry, a family-driven registry that compiles clinical data in the form of family-reported outcomes and family-sourced genetic test results.

Methods: Data from the registry were harmonized and integrated into the i2b2/tranSMART clinical and genomics data warehouse. We gathered information from 401 individuals with 22q13 deletions including SHANK3 (n = 350, ranging in size from 10 kb to 9.1 Mb) or pathogenic or likely pathogenic SHANK3 sequence variants (n = 51), and used regression models with deletion size as a potential predictor of clinical outcomes for 328 phenotypes.

Results: Our results showed that increased deletion size was significantly associated with delay in gross and fine motor acquisitions, a spectrum of conditions related to poor muscle tone, renal malformations, mild dysmorphic features (e.g., large fleshy hands, sacral dimple, dysplastic toenails, supernumerary teeth), lymphedema, congenital heart defects, and more frequent neuroimaging abnormalities and infections. These findings indicate that genes upstream of SHANK3 also contribute to some of the manifestations of PMS in individuals with larger deletions. We also showed that self-help skills, verbal ability and a range of psychiatric diagnoses (e.g., autism, ADHD, anxiety disorder) were more common among individuals with smaller deletions and SHANK3 variants.

Limitations: Some participants were tested with targeted 22q microarrays rather than genome-wide arrays, and karyotypes were unavailable in many cases, thus precluding the analysis of the effect of other copy number variants or chromosomal rearrangements on the phenotype.

Conclusions: This is the largest reported case series of individuals with PMS. Overall, we demonstrate the feasibility of using data from a family-sourced registry to conduct genotype-phenotype analyses in rare genetic disorders. We replicate and strengthen previous findings, and reveal novel associations between larger 22q13 deletions and congenital heart defects, neuroimaging abnormalities and recurrent infections.

Background: SETBP1 Haploinsufficiency Disorder (SETBP1-HD) is characterised by mild to moderate intellectual disability, speech and language impairment, mild motor developmental delay, behavioural issues, hypotonia, mild facial dysmorphisms, and vision impairment. Despite a clear link between SETBP1 mutations and neurodevelopmental disorders the precise role of SETBP1 in neural development remains elusive. We investigate the functional effects of three SETBP1 genetic variants including two pathogenic mutations p.Glu545Ter and SETBP1 p.Tyr1066Ter, resulting in removal of SKI and/or SET domains, and a point mutation p.Thr1387Met in the SET domain.

Methods: Genetic variants were introduced into induced pluripotent stem cells (iPSCs) and subsequently differentiated into neurons to model the disease. We measured changes in cellular differentiation, SETBP1 protein localisation, and gene expression changes.

Results: The data indicated a change in the WNT pathway, RNA polymerase II pathway and identified GATA2 as a central transcription factor in disease perturbation. In addition, the genetic variants altered the expression of gene sets related to neural forebrain development matching characteristics typical of the SETBP1-HD phenotype.

Limitations: The study investigates changes in cellular function in differentiation of iPSC to neural progenitor cells as a human model of SETBP1 HD disorder. Future studies may provide additional information relevant to disease on further neural cell specification, to derive mature neurons, neural forebrain cells, or brain organoids.

Conclusions: We developed a human SETBP1-HD model and identified perturbations to the WNT and POL2RA pathway, genes regulated by GATA2. Strikingly neural cells for both the SETBP1 truncation mutations and the single nucleotide variant displayed a SETBP1-HD-like phenotype.

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.