1. Bangash MA, Park JM, Melnikova T, Wang D, Jeon SK, Lee D, et al. {{Enhanced Polyubiquitination of Shank3 and NMDA Receptor in a Mouse Model of Autism}}. {Cell}. 2011 May 11.
We have created a mouse genetic model that mimics a human mutation of Shank3 that deletes the C terminus and is associated with autism. Expressed as a single copy [Shank3(+/DeltaC) mice], Shank3DeltaC protein interacts with the wild-type (WT) gene product and results in >90% reduction of Shank3 at synapses. This « gain-of-function » phenotype is linked to increased polyubiquitination of WT Shank3 and its redistribution into proteasomes. Similarly, the NR1 subunit of the NMDA receptor is reduced at synapses with increased polyubiquitination. Assays of postsynaptic density proteins, spine morphology, and synapse number are unchanged in Shank3(+/DeltaC) mice, but the amplitude of NMDAR responses is reduced together with reduced NMDAR-dependent LTP and LTD. Reciprocally, mGluR-dependent LTD is markedly enhanced. Shank3(+/DeltaC) mice show behavioral deficits suggestive of autism and reduced NMDA receptor function. These studies reveal a mechanism distinct from haploinsufficiency by which mutations of Shank3 can evoke an autism-like disorder.
2. Cicchetti DV. {{On Scales of Measurement in Autism Spectrum Disorders (ASD) and Beyond: Where Smitty Went Wrong}}. {J Autism Dev Disord}. 2011 May 10.
The author examined critically three beliefs of S.S. Stevens pertaining to his quadripartite system of scales of measurement: (1) There are four scales of measurement in common usage (2) These scales and the scientific disciplines that use them can be incrementally graded for levels of reliability and validity or accuracy such that: Nominal scale variables produce the lowest levels of reliability and accuracy; with successively improving levels for Ordinal, Equal-Interval, and Equal-Ratio scales; and (3) The scale upon which a variable is measured determines the specific type of statistical test that one is permitted to apply. It was shown that each of the three beliefs is fundamentally flawed.
3. Ghanizadeh A. {{Nuclear factor kappa B may increase insight into the management of neuroinflammation and excitotoxicity in autism}}. {Expert Opin Ther Targets}. 2011 May 10.
4. Hu VW, Addington A, Hyman A. {{Novel Autism Subtype-Dependent Genetic Variants Are Revealed by Quantitative Trait and Subphenotype Association Analyses of Published GWAS Data}}. {PLoS One}. 2011;6(4):e19067.
The heterogeneity of symptoms associated with autism spectrum disorders (ASDs) has presented a significant challenge to genetic analyses. Even when associations with genetic variants have been identified, it has been difficult to associate them with a specific trait or characteristic of autism. Here, we report that quantitative trait analyses of ASD symptoms combined with case-control association analyses using distinct ASD subphenotypes identified on the basis of symptomatic profiles result in the identification of highly significant associations with 18 novel single nucleotide polymorphisms (SNPs). The symptom categories included deficits in language usage, non-verbal communication, social development, and play skills, as well as insistence on sameness or ritualistic behaviors. Ten of the trait-associated SNPs, or quantitative trait loci (QTL), were associated with more than one subtype, providing partial replication of the identified QTL. Notably, none of the novel SNPs is located within an exonic region, suggesting that these hereditary components of ASDs are more likely related to gene regulatory processes (or gene expression) than to structural or functional changes in gene products. Seven of the QTL reside within intergenic chromosomal regions associated with rare copy number variants that have been previously reported in autistic samples. Pathway analyses of the genes associated with the QTL identified in this study implicate neurological functions and disorders associated with autism pathophysiology. This study underscores the advantage of incorporating both quantitative traits as well as subphenotypes into large-scale genome-wide analyses of complex disorders.
5. Mak-Fan KM, Taylor MJ, Roberts W, Lerch JP. {{Measures of Cortical Grey Matter Structure and Development in Children with Autism Spectrum Disorder}}. {J Autism Dev Disord}. 2011 May 10.
The current study examined group differences in cortical volume, surface area, and thickness with age, in a group of typically developing children and a group of children with ASD aged 6-15 years. Results showed evidence of age by group interactions, suggesting atypicalities in the relation between these measures and age in the ASD group. Additional vertex-based analyses of cortical thickness revealed that specific regions in the left inferior frontal gyrus (BA 44) and left precuneus showed thicker cortex for the ASD group at younger ages only. These data support the hypothesis of an abnormal developmental trajectory of the cortex in ASD, which could have profound effects on other aspects of neural development in children with ASD.
6. Rossignol DA, Frye RE. {{Substantial Problems with Measuring Brain Mitochondrial Dysfunction in Autism Spectrum Disorder Using Magnetic Resonance Spectroscopy}}. {J Autism Dev Disord}. 2011 May 10.
7. Silva LM, Schalock M. {{Autism Parenting Stress Index: Initial Psychometric Evidence}}. {J Autism Dev Disord}. 2011 May 10.
Data validating the Autism Parenting Stress Index (APSI) is presented for 274 children under age six. Cronbach’s alpha was .827. As a measure of parenting stress specific to core and co-morbid symptoms of autism, the APSI is unique. It is intended for use by clinicians to identify areas where parents need support with parenting skills, and to assess the effect of intervention on parenting stress. Mean parenting stress in the autism group was four times that of the typical group and double that of the other developmental delay group [F(2,272) = 153; p < 001]. An exploratory factor analysis suggested three factors impacting parenting stress: one relating to core deficits, one to co-morbid behavioral symptoms, and one to co-morbid physical symptoms.
8. Skokauskas N, Gallagher L. {{Mental health aspects of autistic spectrum disorders in children}}. {J Intellect Disabil Res}. 2011 May 10.
Background Previous studies have reported variable and at times opposite findings on comorbid psychiatric problems in children with autistic spectrum disorders (ASD). Aims This study aimed to examine patterns of comorbid psychiatric problems in children with ASD and their parents compared with IQ matched controls and their parents. Methods Behavioural/emotional problems were evaluated in a sample of children with ASD [a diagnosis of ASD was given if they met criteria for ASD on both of the ADI-R (Autism Diagnostic Interview-Revised) and ADOS (Autism Diagnostic Observational Schedule)] and an age and IQ matched control group using the Child Behavior Checklist (CBCL/6-18). Parental psychological distress for both groups was evaluated with the Brief Symptom Inventory (BSI). Results There were 59 (88%) boys and 8 (12%) girls in the ASD group. Similarly, 57 (85%) of the control group were male and 10 (15%) were female. The groups did not differ significantly on mean age, mean IQ scores, gender and parents mean age. Results of the CBCL/6-18 revealed that the majority of parents reported their child with ASD as having either internalising (clinical range: 47.8%; borderline range: 16.4%) or externalising problems (clinical range: 10.4%; borderline range: 20.9%). In the control group more parents reported their children having externalising (clinical range: 46.3%; borderline range: 16.4%) than internalising problems (clinical range: 35.8%; borderline range: 11.9%). Almost a half of the ASD group met CBCL DSM criteria for clinically significant attention deficit hyperactivity disorder (44.78%) and anxiety (46.2%) problems. Based on the Brief Symptom Inventory Global Severity Index 22.4% of fathers and 23.8% of mothers of ASD children produced scores that were indicative of possible psychopathology. Conclusions High rates of clinically significant psychiatric problems were detected in ASD children, with anxiety and attention deficit hyperactivity disorder being the most frequently detected syndromes.
9. Wang LW, Tancredi DJ, Thomas DW. {{The Prevalence of Gastrointestinal Problems in Children Across the United States with Autism Spectrum Disorders from Families with Multiple Affected Members}}. {J Dev Behav Pediatr}. 2011 May 6.
OBJECTIVE:: To perform a large registry-based study to determine the relative prevalence of gastrointestinal (GI) problems in children with an autism spectrum disorder (ASD) from families with multiple affected members compared with their unaffected sibling(s). METHODS:: In-home structured retrospective medical history interviews by parent recall were conducted by a pediatric neurologist. Our analysis sample included information about GI health of 589 subjects with idiopathic, familial ASD and 163 of their unaffected sibling controls registered with Autism Genetic Resource Exchange. Individuals with ASD were subgrouped into 3 autism severity groups (Full Autism, Almost Autism, and Spectrum) based on their Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Scale scores. RESULTS:: Parents reported significantly more GI problems in children with ASD (249/589; 42%) compared with their unaffected siblings (20/163; 12%) (p < .001). The 2 most common Gl problems in children with ASD were constipation (116/589; 20%) and chronic diarrhea (111/589; 19%). Conditional logistic regression analysis showed that having Full Autism (adjusted odds ratio [AOR] = 14.28, 95% confidence interval [CI]: 6.22-32.77) or Almost Autism (AOR = 5.16, 95% CI 2.02-13.21) was most highly associated with experiencing GI problems. Increased autism symptom severity was associated with higher odds of GI problems (AOR for trend = 2.63, 95% CI: 1.56-4.45). CONCLUSIONS:: Parents report significantly more GI problems in children with familial ASD, especially those with Full Autism, than in their unaffected children. Increased autism symptom severity is associated with increased odds of having GI problems.
10. Zimmer MH, Hart LC, Manning-Courtney P, Murray DS, Bing NM, Summer S. {{Food Variety as a Predictor of Nutritional Status Among Children with Autism}}. {J Autism Dev Disord}. 2011 May 10.
The frequency of selective eating and nutritional deficiency was studied among 22 children with autism and an age matched typically developing control group. Children with autism ate fewer foods on average than typically developing children. (33.5 vs. 54.5 foods, P < .001) As compared to typical controls, children with autism had a higher average intake of magnesium, and lower average intake of protein, calcium, vitamin B12, and vitamin D. Selective eaters were significantly more likely than typical controls to be at risk for at least one serious nutrient deficiency (P < .001).
