1. Ahmad SF, Ansari MA, Nadeem A, Bakheet SA, Al-Ayadhi LY, Alotaibi MR, Alhoshani AR, Alshammari MA, Attia SM. {{Dysregulation of T cell immunoglobulin and mucin domain 3 (TIM-3) signaling in peripheral immune cells is associated with immune dysfunction in autistic children}}. {Mol Immunol};2018 (Dec 24);106:77-86.
Evidence suggests that immune dysregulation is associated with autism spectrum disorder (ASD). T cell immunoglobulin and mucin domain-3 (TIM-3) has a critical role in several inflammatory disorders; however, the role of TIM-3 signaling has not been demonstrated in ASD. In the present study, we assessed the role of TIM-3 signaling in children with ASD. We expected that increased numbers of TIM-3(+) cells could alter immune function in children with ASD. We revealed production of TIM-3 on CD3(+), CD4(+), CD8(+), CD11a(+),b(+), CD14(+), CD62P(+), and CXCR5(+) PBMCs in children with ASD and typically developing (TD) controls using immunofluorescent staining. We further demonstrated the production of IL-1beta, IFN-gamma, IL-17 A, and Foxp3 in TIM-3(+) PBMCs of TD controls and individuals with ASD. We also observed the mRNA expression levels of TIM-3, CD11a,b, CD14, IL-1beta and IFN-gamma using RT-PCR. We further assessed the protein levels of TIM-3, IL-1beta, CXCR5, and IFN-gamma using western blotting. The results showed that children with ASD had increased numbers of CD3(+)TIM-3(+), CD4(+)TIM-3(+), CD8(+)TIM-3(+), CD11a,b(+)TIM-3(+), CD14(+)TIM-3(+), CD62P(+)TIM-3(+) and CXCR5(+)TIM-3(+) cells compared with TD controls. Our results further showed that children with ASD had increased IL-1beta(+)TIM-3(+), IFN-gamma(+)TIM-3(+), and IL-17(+)TIM-3(+), and decreased Foxp3(+)TIM-3(+) production compared with that in TD controls. Our results indicated that children with ASD significantly induced TIM-3, CD11a,b, CD14, CXCR5, IL-1beta and IFN-gamma mRNA and protein expression levels compared with TD controls. The results suggested that detection of TIM-3 signaling could contribute to the early diagnoses of ASD.
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2. Alqhazo MT, Hatamleh LS, Bashtawi M. {{Phonological and lexical abilities of Jordanian children with autism}}. {Appl Neuropsychol Child};2018 (Dec 26):1-9.
This study examines phonological and lexical abilities of children with autism in Jordan. JISH Articulation Test (JAT) to measure phonological abilities and JISH School Readiness Screening Test to measure lexical abilities were used in the study. Children with ASD (n = 39) aged 4-8 years and 40 children with typical development were selected to participate in this study. The main finding of this study was that language impairment in both phonological and lexical abilities were noted among Jordanian children with ASD. The results also showed that phonological impairment was greater than lexical impairment. It is concluded that the findings of such study will be helpful to guide experts to provide children with ASD with appropriate educational programs and establish a comprehensive and effective treatment protocols that include phonological and lexical exercises to enhance their speech and language abilities.
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3. Bakroon A, Lakshminarayanan V. {{Do different experimental tasks affect psychophysical measurements of motion perception in autism-spectrum disorder? An analysis}}. {Clin Optom (Auckl)};2018;10:131-143.
There is a rapid increase in the number of individuals with high-functioning autism (HFA). Research on motion perception in HFA has shown deficits in processing motion information at the higher visual cortical areas (V5/middle temporal). Several hypotheses have been put forth to explain these deficits as being due to enhanced processing of small details at the expense of the global picture or as a global integration abnormality. However, there is a lot of variability in the results obtained from experiments designed to study motion in adults with autism. These could be due to the inherent diagnostic differences within even the same range of the autism spectrum and/or due to comparison of different experimental paradigms whose processing by the same visual neural areas could be different. In this review, we discuss the various results on motion processing in HFA, as well as the theories of motion perception in autism.
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4. Jenabi E, Karami M, Khazaei S, Bashirian S. {{The association between preeclampsia and autism spectrum disorders among children: a meta-analysis}}. {Korean J Pediatr};2018 (Dec 24)
Purpose: This meta-analysis pooled all of case-control and cohort studies to obtain the association between preeclampsia and the risk of ASD children. Methods: The search for relevant studies in major databases was performed including; Web of Science, PubMed and Scopus up to May 2018. The ORs or RRs with 95% confidence intervals (CI) extracted from eligible studies for measuring of association among studies. Results: The pooled estimates of OR and RR indicated a significant association between preeclampsia and ASD (OR = 1.36, 95% CI = 1.12 to 1.60) and (RR = 1.30, 95% CI = 1.20 to 1.41). Conclusions: Despite existing controversy, our findings indicated that preeclampsia is associated with increased risk of ASD among children.
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5. Liu B, Molinaro G, Shu H, Stackpole EE, Huber KM, Richter JD. {{Optimization of ribosome profiling using low-input brain tissue from fragile X syndrome model mice}}. {Nucleic Acids Res};2018 (Dec 24)
Dysregulated protein synthesis is a major underlying cause of many neurodevelopmental diseases including fragile X syndrome. In order to capture subtle but biologically significant differences in translation in these disorders, a robust technique is required. One powerful tool to study translational control is ribosome profiling, which is based on deep sequencing of mRNA fragments protected from ribonuclease (RNase) digestion by ribosomes. However, this approach has been mainly applied to rapidly dividing cells where translation is active and large amounts of starting material are readily available. The application of ribosome profiling to low-input brain tissue where translation is modest and gene expression changes between genotypes are expected to be small has not been carefully evaluated. Using hippocampal tissue from wide type and fragile X mental retardation 1 (Fmr1) knockout mice, we show that variable RNase digestion can lead to significant sample batch effects. We also establish GC content and ribosome footprint length as quality control metrics for RNase digestion. We performed RNase titration experiments for low-input samples to identify optimal conditions for this critical step that is often improperly conducted. Our data reveal that optimal RNase digestion is essential to ensure high quality and reproducibility of ribosome profiling for low-input brain tissue.
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6. Sacrey LR, Zwaigenbaum L, Bryson S, Brian J, Smith IM. {{The reach-to-grasp movement in infants later diagnosed with autism spectrum disorder: a high-risk sibling cohort study}}. {J Neurodev Disord};2018 (Dec 27);10(1):41.
BACKGROUND: Although autism spectrum disorder (ASD) is characterized by impairments in social communication and the presence of repetitive behavior and/or restricted interests, there is evidence that motor impairments may be a contributing factor to the ASD phenotype. The purpose of this study was to examine the motor act of reaching-to-grasp in children at high risk (HR; with an older sibling diagnosed with ASD) and low-risk (LR; no family history of ASD) for ASD. METHODS: Children were compared for differences in reaching-to-grasp based on sibling status and diagnostic outcome. Children were enrolled between 6 and 12 months of age and the reach-to-grasp movement was scored at 6, 9, (where available) 12, 15, 18, 24, and 36 months of age using the qualitative Skilled Reaching Rating Scale to determine the presence of any group-, age-, or sex-related differences in the mechanics of the reach-to-grasp movement using a Mixed Models analysis. At 36 months, all children underwent a gold-standard diagnostic assessment, which resulted in three outcome groups: HR children diagnosed with ASD (HR-ASD; n = 10), HR children not diagnosed with ASD (HR-N; n = 10), and low-risk children not diagnosed with ASD (LR; n = 10). RESULTS: The group of children who were later diagnosed with ASD (HR-ASD group) showed higher (worse) total scores on the reach-to-grasp movement, as well as higher scores on the components of Orient, Lift, and Pronate compared to children in the LR and HR-N groups. CONCLUSIONS: Our results support the growing literature indicating that children who are later diagnosed with ASD show impaired early motor performance. These results highlight the importance of early surveillance of children who are at elevated risk for ASD, and early initiatives should focus on early signs of the phenotype, including both movement and sensory differences (prodromal signs) prior to the emergence of diagnostic characteristics.
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7. Won DC, Feldman HM, Huffman LC. {{Sleep Problem Detection and Documentation in Children With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder by Developmental-Behavioral Pediatricians: A DBPNet Study}}. {J Dev Behav Pediatr};2019 (Jan);40(1):20-31.
OBJECTIVE: To determine the percentage of children with autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and combined ASD + ADHD who had sleep problems documented by developmental-behavioral pediatricians at diagnostic and follow-up visits at 12 US academic medical centers comprising the Developmental-Behavioral Pediatrics Research Network (DBPNet) and to identify the predictors of sleep problem documentation. METHODS: Developmental-behavioral pediatricians completed encounter forms that covered sociodemographic, medical, clinician, and visit factors. There was 1 dependent variable, sleep problem documentation, for which 4 definitions were developed (Model 1 = Sleep Disorder coded; Model 2 = Sleep Disorder or polysomnogram coded; Model 3 = Sleep Disorder, polysomnogram, or sleep medication coded; and Model 4 = Sleep Disorder, polysomnogram, sleep medication, or clonidine coded). RESULTS: Sleep problem documentation was 14.1% for Model 1, 15.2% for Model 2, 17.3% for Model 3, and 19.7% for Model 4. All values were lower (p < 0.001) than the reported prevalence of sleep problems in these conditions. For Model 4, predictors of sleep problem documentation were age group, ethnicity, medical insurance type, and DBPNet site. CONCLUSION: Developmental-behavioral pediatricians in DBPNet under-reported sleep problems in children with ASD and ADHD. Variation among sites was substantial. Care plans for children with ASD and ADHD should specify which treating clinician(s) monitors sleep issues. Lien vers le texte intégral (Open Access ou abonnement)
