Autism Spectrum Disorder Subtype Research Articles

Embryonic origin of two ASD subtypes of social symptom severity: the larger the brain cortical organoid size, the more severe the social symptoms

BackgroundSocial affective and communication symptoms are central to autism spectrum disorder (ASD), yet their severity differs across toddlers: Some toddlers with ASD display improving abilities across early ages and develop good social and language skills, while others with “profound” autism have persistently low social, language and cognitive skills and require lifelong care. The biological origins of these opposite ASD social severity subtypes and developmental trajectories are not known.MethodsBecause ASD involves early brain overgrowth and excess neurons, we measured size and growth in 4910 embryonic-stage brain cortical organoids (BCOs) from a total of 10 toddlers with ASD and 6 controls (averaging 196 individual BCOs measured/subject). In a 2021 batch, we measured BCOs from 10 ASD and 5 controls. In a 2022 batch, we tested replicability of BCO size and growth effects by generating and measuring an independent batch of BCOs from 6 ASD and 4 control subjects. BCO size was analyzed within the context of our large, one-of-a-kind social symptom, social attention, social brain and social and language psychometric normative datasets ranging from N = 266 to N = 1902 toddlers. BCO growth rates were examined by measuring size changes between 1- and 2-months of organoid development. Neurogenesis markers at 2-months were examined at the cellular level. At the molecular level, we measured activity and expression of Ndel1; Ndel1 is a prime target for cell cycle-activated kinases; known to regulate cell cycle, proliferation, neurogenesis, and growth; and known to be involved in neuropsychiatric conditions.ResultsAt the BCO level, analyses showed BCO size was significantly enlarged by 39% and 41% in ASD in the 2021 and 2022 batches. The larger the embryonic BCO size, the more severe the ASD social symptoms. Correlations between BCO size and social symptoms were r = 0.719 in the 2021 batch and r = 0. 873 in the replication 2022 batch. ASD BCOs grew at an accelerated rate nearly 3 times faster than controls. At the cell level, the two largest ASD BCOs had accelerated neurogenesis. At the molecular level, Ndel1 activity was highly correlated with the growth rate and size of BCOs. Two BCO subtypes were found in ASD toddlers: Those in one subtype had very enlarged BCO size with accelerated rate of growth and neurogenesis; a profound autism clinical phenotype displaying severe social symptoms, reduced social attention, reduced cognitive, very low language and social IQ; and substantially altered growth in specific cortical social, language and sensory regions. Those in a second subtype had milder BCO enlargement and milder social, attention, cognitive, language and cortical differences.LimitationsLarger samples of ASD toddler-derived BCO and clinical phenotypes may reveal additional ASD embryonic subtypes.ConclusionsBy embryogenesis, the biological bases of two subtypes of ASD social and brain development—profound autism and mild autism—are already present and measurable and involve dysregulated cell proliferation and accelerated neurogenesis and growth. The larger the embryonic BCO size in ASD, the more severe the toddler’s social symptoms and the more reduced the social attention, language ability, and IQ, and the more atypical the growth of social and language brain regions.

Regressive autism with autoimmune encephalitis: case report and literature review of subtype of autism spectrum disorder

About one-third of individuals with autism spectrum disorder (ASD) show a sudden loss of acquired skills between the 2nd or 3rd years of life. The primary areas of loss were grouped into language skills, social interest, adaptive functioning, and motor skills. A 4-year and 2-month-old boy presented to the child and adolescent mental health and disease outpatient clinic with his parents with complaints of decreased eye contact, and regression in speech. His developmental milestones were within the typical range of three years and two months. During the investigation into the organic etiology of regression, serum anti-CASPR2 positivity was detected, and intravenous immunoglobulin administration was initiated. After 6 months of treatment and special education, there was a slight decrease in the CARS score along with improvements in adaptation to change, visual response, and non-verbal communication. This rare case of regressive autism accompanied by autoimmune encephalitis provides further evidence in the field that autoimmune encephalitis may influence the development of the regressive subtype of ASD. Differential diagnosis is crucial because additional immunotherapy may be beneficial in these cases. Further investigation is needed to identify the relationship.

EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning.

Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD). Existing studies have leveraged the functional connectivity (FC) of rs-fMRI, achieving notable classification performance. However, they have significant limitations, including the lack of adequate information while using linear low-order FC as inputs to the model, not considering individual characteristics (i.e., different symptoms or varying stages of severity) among patients with ASD, and the non-explainability of the decision process. To cover these limitations, we propose a novel explainability-guided region of interest (ROI) selection (EAG-RS) framework that identifies non-linear high-order functional associations among brain regions by leveraging an explainable artificial intelligence technique and selects class-discriminative regions for brain disease identification. The proposed framework includes three steps: (i) inter-regional relation learning to estimate non-linear relations through random seed-based network masking, (ii) explainable connection-wise relevance score estimation to explore high-order relations between functional connections, and (iii) non-linear high-order FC-based diagnosis-informative ROI selection and classifier learning to identify ASD. We validated the effectiveness of our proposed method by conducting experiments using the Autism Brain Imaging Database Exchange (ABIDE) dataset, demonstrating that the proposed method outperforms other comparative methods in terms of various evaluation metrics. Furthermore, we qualitatively analyzed the selected ROIs and identified ASD subtypes linked to previous neuroscientific studies.

Dysregulation of mTOR signaling mediates common neurite and migration defects in both idiopathic and 16p11.2 deletion autism neural precursor cells.

Autism spectrum disorder (ASD) is defined by common behavioral characteristics, raising the possibility of shared pathogenic mechanisms. Yet, vast clinical and etiological heterogeneity suggests personalized phenotypes. Surprisingly, our iPSC studies find that six individuals from two distinct ASD subtypes, idiopathic and 16p11.2 deletion, have common reductions in neural precursor cell (NPC) neurite outgrowth and migration even though whole genome sequencing demonstrates no genetic overlap between the datasets. To identify signaling differences that may contribute to these developmental defects, an unbiased phospho-(p)-proteome screen was performed. Surprisingly despite the genetic heterogeneity, hundreds of shared p-peptides were identified between autism subtypes including the mTOR pathway. mTOR signaling alterations were confirmed in all NPCs across both ASD subtypes, and mTOR modulation rescued ASD phenotypes and reproduced autism NPC-associated phenotypes in control NPCs. Thus, our studies demonstrate that genetically distinct ASD subtypes have common defects in neurite outgrowth and migration which are driven by the shared pathogenic mechanism of mTOR signaling dysregulation.

Identification of Immune Infiltration and Iron Metabolism-Related Subgroups in Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with a broad spectrum of symptoms and prognoses. Effective therapy requires understanding this variability. ASD children's cognitive and immunological development may depend on iron homoeostasis. This study employs a machine learning model that focuses on iron metabolism hub genes to identify ASD subgroups and describe immune infiltration patterns. A total of 97 control and 148 ASD samples were obtained from the GEO database. Differentially expressed genes (DEGs) and an iron metabolism gene collection achieved the intersection of 25 genes. Unsupervised cluster analysis determined molecular subgroups in individuals with ASD based on 25 genes related to iron metabolism. We assessed gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, gene set variation analysis (GSVA), and immune infiltration analysis to compare iron metabolism subtype effects. We employed machine learning to identify subtype-predicting hub genes and utilized both training and validation sets to assess gene subtype prediction accuracy. ASD can be classified into two iron-metabolizing molecular clusters. Metabolic enrichment pathways differed between clusters. Immune infiltration showed that clusters differed immunologically. Cluster 2 had better immunological scores and more immune cells, indicating a stronger immune response. Machine learning screening identified SELENBP1 and CAND1 as important genes in ASD's iron metabolism signaling pathway. These genes express in the brain and have AUC values over 0.8, implying significant predictive power. The present study introduces iron metabolism signaling pathway indicators to predict ASD subtypes. ASD is linked to immune cell infiltration and iron metabolism disorders.

Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder.

Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra-SN and inter-SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting-state functional magnetic resonance imaging data from 105 ASD and 102 typically-developing controls (TC), we calculated the time-varying FC of intra-SN and inter-SN (SN with DMN and CEN). Then, the joint recurrence features for the time-varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k-means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.

The Role of SNAP-25 in Autism Spectrum Disorders Onset Patterns.

Autism spectrum disorders (ASD) can present with different onset and timing of symptom development; children may manifest symptoms early in their first year of life, i.e., early onset (EO-ASD), or may lose already achieved skills during their second year of life, thus showing a regressive-type onset (RO-ASD). It is still controversial whether regression represents a neurobiological subtype of ASD, resulting from distinct genetic and environmental causes. We focused this study on the 25 kD synaptosomal-associated protein (SNAP-25) gene involved in both post-synaptic formation and adhesion and considered a key player in the pathogenesis of ASD. To this end, four single nucleotide polymorphisms (SNPs) of the SNAP-25 gene, rs363050, rs363039, rs363043, and rs1051312, already known to be involved in neurodevelopmental and psychiatric disorders, were analyzed in a cohort of 69 children with EO-ASD and 58 children with RO-ASD. Both the rs363039 G allele and GG genotype were significantly more frequently carried by patients with EO-ASD than those with RO-ASD and healthy controls (HC). On the contrary, the rs1051312 T allele and TT genotype were more frequent in individuals with RO-ASD than those with EO-ASD and HC. Thus, two different SNAP-25 alleles/genotypes seem to discriminate between EO-ASD and RO-ASD. Notably, rs1051312 is located in the 3' untranslated region (UTR) of the gene and is the target of microRNA (miRNA) regulation, suggesting a possible epigenetic role in the onset of regressive autism. These SNPs, by discriminating two different onset patterns, may represent diagnostic biomarkers of ASD and may provide insight into the different biological mechanisms towards the development of better tailored therapeutic and rehabilitative approaches.

Corneal confocal microscopy demonstrates sensory nerve loss in children with autism spectrum disorder.

Autism spectrum disorder (ASD) is a developmental disorder characterized by difficulty in communication and interaction with others. Postmortem studies have shown cerebral neuronal loss and neuroimaging studies show neuronal loss in the amygdala, cerebellum and inter-hemispheric regions of the brain. Recent studies have shown altered tactile discrimination and allodynia on the face, mouth, hands and feet and intraepidermal nerve fiber loss in the legs of subjects with ASD. Fifteen children with ASD (age: 12.00 ± 3.55 years) and twenty age-matched healthy controls (age: 12.83 ± 1.91 years) underwent corneal confocal microscopy (CCM) and quantification of corneal nerve fiber morphology. Corneal nerve fibre density (fibers/mm2) (28.61 ± 5.74 vs. 40.42 ± 8.95, p = 0.000), corneal nerve fibre length (mm/mm2) (16.61 ± 3.26 vs. 21.44 ± 4.44, p = 0.001), corneal nerve branch density (branches/mm2) (43.68 ± 22.71 vs. 62.39 ± 21.58, p = 0.018) and corneal nerve fibre tortuosity (0.037 ± 0.023 vs. 0.074 ± 0.017, p = 0.000) were significantly lower and inferior whorl length (mm/mm2) (21.06 ± 6.12 vs. 23.43 ± 3.95, p = 0.255) was comparable in children with ASD compared to controls. CCM identifies central corneal nerve fiber loss in children with ASD. These findings, urge the need for larger longitudinal studies to determine the utility of CCM as an imaging biomarker for neuronal loss in different subtypes of ASD and in relation to disease progression.

Stratifying ASD and characterizing the functional connectivity of subtypes in resting-state fMRI

Although stratifying autism spectrum disorder (ASD) into different subtypes is a common effort in the research field, few papers have characterized the functional connectivity alterations of ASD subgroups classified by their clinical presentations. This is a case-control rs-fMRI study, based on large samples of open database (Autism Brain Imaging Data Exchange, ABIDE). The rs-MRI data from n=415 ASD patients (males n=357), and n=574 typical development (TD) controls (males n=410) were included. Clinical features of ASD were extracted and classified using data from each patient's Autism Diagnostic Interview-Revised (ADI-R) evaluation. Each subtype of ASD was characterized by local functional connectivity using regional homogeneity (ReHo) for assessment, remote functional connectivity using voxel-mirrored homotopic connectivity (VMHC) for assessment, the whole-brain functional connectivity, and graph theoretical features. These identified imaging properties from each subtype were integrated to create a machine learning model for classifying ASD patients into the subtypes based on their rs-fMRI data, and an independent dataset was used to validate the model. All ASD participants were classified into Cluster-1 (patients with more severe impairment) and Cluster-2 (patients with moderate impairment) according to the dimensional scores of ADI-R. When compared to the TD group, Cluster-1 demonstrated increased local connection and decreased remote connectivity, and widespread hyper- and hypo-connectivity variations in the whole-brain functional connectivity. Cluster-2 was quite similar to the TD group in both local and remote connectivity. But at the level of whole-brain functional connectivity, the MCC-related connections were specifically impaired in Cluster-2. These properties of functional connectivity were fused to build a machine learning model, which achieved ~75% for identifying ASD subtypes (Cluster-1 accuracy = 81.75%; Cluster-2 accuracy = 76.48%). The stratification of ASD by clinical presentations can help to minimize disease heterogeneity and highlight the distinguished properties of brain connectivity in ASD subtypes.

Data-driven dissection of the fever effect in autism spectrum disorder.

Some individuals with autism spectrum disorder (ASD) demonstrate marked behavioral improvements during febrile episodes, in what is perhaps the only present-day means of modulating the core ASD phenotype. Understanding the nature of this so-called fever effect is therefore essential for leveraging this natural temporary relief of symptoms to a sustained efficacious intervention. Toward this goal, we used machine learning to analyze the rich clinical data of the Simons Simplex Collection, in which one out of every six children with ASD was reported to improve during febrile episodes, across multiple ASD domains. Reported behavioral improvements during febrile episodes were associated with maternal infection in pregnancy (OR = 1.7, 95% CI = [1.42, 2.03], P = 4.24 × 10-4 ) and gastrointestinal (GI) dysfunction (OR = 1.46, 95% CI = [1.15, 1.81], P = 1.94 × 10-3 ). Family members of children reported to improve when febrile have an increased prevalence of autoimmune disorders (OR = 1.43, 95% CI = [1.23, 1.67], P = 3.0 × 10-6 ), language disorders (OR = 1.63, 95% CI = [1.29, 2.04], P = 2.5 × 10-5 ), and neuropsychiatric disorders (OR = 1.59, 95% CI = [1.34, 1.89], P < 1 × 10-6 ). Since both GI abnormalities and maternal immune activation have been linked to ASD via proinflammatory cytokines, these results might suggest a possible involvement of immune dysregulation in the fever effect, consistent with findings in mouse models. This work advances our understanding of the fever-responsive ASD subtype and motivates the future studies to directly test the link between proinflammatory cytokines and behavioral modifications in individuals with ASD.

The Neurogenetics of Functional Connectivity Alterations in Autism: Insights From Subtyping in 657 Individuals

BackgroundThere is little consensus and controversial evidence on anatomical alterations in the brains of people with autism spectrum disorder (ASD), due in part to the large heterogeneity present in ASD, which in turn is a major drawback for developing therapies. One strategy to characterize this heterogeneity in ASD is to cluster large-scale functional brain connectivity profiles. MethodsA subtyping approach based on consensus clustering of functional brain connectivity patterns was applied to a population of 657 autistic individuals with quality-assured neuroimaging data. We then used high-resolution gene transcriptomic data to characterize the molecular mechanism behind each subtype by performing enrichment analysis of the set of genes showing a high spatial similarity with the profiles of functional connectivity alterations between each subtype and a group of typically developing control participants. ResultsTwo major stable subtypes were found: subtype 1 exhibited hypoconnectivity (less average connectivity than typically developing control participants) and subtype 2, hyperconnectivity. The 2 subtypes did not differ in structural imaging metrics in any of the analyzed regions (68 cortical and 14 subcortical) or in any of the behavioral scores (including IQ, Autism Diagnostic Interview, and Autism Diagnostic Observation Schedule). Finally, only subtype 2, comprising about 43% of ASD participants, led to significant enrichments after multiple testing corrections. Notably, the dominant enrichment corresponded to excitation/inhibition imbalance, a leading well-known primary mechanism in the pathophysiology of ASD. ConclusionsOur results support a link between excitation/inhibition imbalance and functional connectivity alterations, but only in one ASD subtype, overall characterized by brain hyperconnectivity and major alterations in somatomotor and default mode networks.

Connectome-based prediction of the severity of autism spectrum disorder.

Autism spectrum disorder (ASD) is characterized by social and behavioural deficits. Current diagnosis relies on behavioural criteria, but machine learning, particularly connectome-based predictive modelling (CPM), offers the potential to uncover neural biomarkers for ASD. This study aims to predict the severity of ASD traits using CPM and explores differences among ASD subtypes, seeking to enhance diagnosis and understanding of ASD. Resting-state functional magnetic resonance imaging data from 151 ASD patients were used in the model. CPM with leave-one-out cross-validation was conducted to identify intrinsic neural networks that predict Autism Diagnostic Observation Schedule (ADOS) scores. After the model was constructed, it was applied to independent samples to test its replicability (172 ASD patients) and specificity (36 healthy control participants). Furthermore, we examined the predictive model across different aspects of ASD and in subtypes of ASD to understand the potential mechanisms underlying the results. The CPM successfully identified negative networks that significantly predicted ADOS total scores [r (df=150) = 0.19, P=0.008 in all patients; r (df=104) = 0.20, P=0.040 in classic autism] and communication scores [r (df=150) = 0.22, P=0.010 in all patients; r (df=104) = 0.21, P=0.020 in classic autism]. These results were reproducible across independent databases. The networks were characterized by enhanced inter- and intranetwork connectivity associated with the occipital network (OCC), and the sensorimotor network (SMN) also played important roles. A CPM based on whole-brain resting-state functional connectivity can predicted the severity of ASD. Large-scale networks, including the OCC and SMN, played important roles in the predictive model. These findings may provide new directions for the diagnosis and intervention of ASD, and maybe could be the targets in novel interventions.

Immunotherapy In Autism Spectrum Disorder; A Case Series

IntroductionResearches has shown that a subset of the autism spectrum disorder (ASD) population presents with immune dysregulation. Based upon the immunological abnormalities, various treatment modalities have been applied to children with ASD. One immunomodulatory treatment that has been studied in ASD is intravenous immunoglobulins (IVIG).ObjectivesThis report is based upon two hypotheses: autism etiology may be closely related to neuroinflammation; and, an effective treatment should restore the individual’s comminication skills. In this report, we present two cases who were diagnosed with ASD and received IVIG treatment.MethodsCase A is a 5-year-old female patient who was diagnosed with ASD at the age of 3. The second case, B is a 9 -year-old male patient who is a 7th grade student and was also diagnosed with ASD at the age of 3. The first patient’s autism symptoms was noticed by her family at the age of 2. Although she had the ability to coordinate eye contact, understood simple commands, she was observed to lose all her acquired developmental skills one by one at around 18-24 months. Towards the age of 3, stereotypical movements such as turning around and flapping wings, started. Similarly, the second patient’s acquired skills were lost around at the age of 2-3, after having recurrent infections. Based on deficits in social‐emotional reciprocity, deficits in nonverbal communicative behaviors used for social interaction and restricted/repetitive patterns of behavior, both patients were diagnosed with ASD. Considering that autoimmune mechanisms may have been affected in ASD, IVIG treatment was initiated at 1 g/kg/month. Case A received IVIG once in a month, total 5 infusions had been applied. Case B received total 12 infusions with the same protocol. Treatment response was assessed with the Childhood Autism Rating Scale (CARS).ResultsIn these two ASD cases which were treated with IVIG ,a subjective partial reduction in autism symptoms and an objective decrease in CARS scores were detected. Current evidence suggests that there are various factors contributing to the development of autism and different combinations of these aspects give rise to different variations of some ASD subtypes. Recent studies in this field indicate a possible connection between the immune system impairments and ASD. This report, therefore, provide support for the notion that at least a subset of children with ASD might have immune abnormalities and may respond to the immune modulating effect of IVIG therapy.ConclusionsAlthough, autoimmune or inflammatory etiologies may not explain the majority of cases of autism, it might at least be useful to understand some. Further studies with larger number of patients should be conducted about the use of IVIG in selected patients with ASD.Disclosure of InterestNone Declared

Exploring the heterogeneity of brain structure in autism spectrum disorder based on individual structural covariance network.

Autism spectrum disorder (ASD) is characterized by highly structural heterogeneity. However, most previous studies analyzed between-group differences through a structural covariance network constructed based on the ASD group level, ignoring the effect of between-individual differences. We constructed the gray matter volume-based individual differential structural covariance network (IDSCN) using T1-weighted images of 207 children (ASD/healthy controls: 105/102). We analyzed structural heterogeneity of ASD and differences among ASD subtypes obtained by a K-means clustering analysis based on evidently different covariance edges relative to healthy controls. The relationship between the distortion coefficients (DCs) calculated at the whole-brain, intra- and interhemispheric levels and the clinical symptoms of ASD subtypes was then examined. Compared with the control group, ASD showed significantly altered structural covariance edges mainly involved in the frontal and subcortical regions. Given the IDSCN of ASD, we obtained 2 subtypes, and the positive DCs of the 2 ASD subtypes were significantly different. Intra- and interhemispheric positive and negative DCs can predict the severity of repetitive stereotyped behaviors in ASD subtypes 1 and 2, respectively. These findings highlight the crucial role of frontal and subcortical regions in the heterogeneity of ASD and the necessity of studying ASD from the perspective of individual differences.

Systematic Review on EEG Analysis to Diagnose and Treat Autism by Evaluating Functional Connectivity and Spectral Power.

An abnormality in neural connectivity is linked to autism spectrum disorder (ASD). There is no way to test the concept of neural connectivity empirically. According to recent network theory and time series analysis findings, electroencephalography (EEG) can assess neural network architecture, a sign of activity in the brain. This systematic review aims to evaluate functional connectivity and spectral power using EEG signals. EEG records the brain activity of an individual by displaying wavy lines that depict brain cells' communication through electrical impulses. EEG can diagnose various brain disorders, including epilepsy and related seizure illness, brain dysfunction, tumors, and damage. We found 21 studies using two of the most common EEG analysis methods: functional connectivity and spectral power. ASD and non-ASD individuals were found to differ significantly in all selected papers. Due to high heterogeneity in the outcomes, generalizations cannot be drawn, and no single method is currently beneficial as a diagnostic tool. For ASD subtype delineation, the lack of research prevented the evaluation of these techniques as diagnostic tools. These findings confirm the presence of abnormalities in the EEG in ASD, but they are insufficient to diagnose. Our study suggests that EEG is useful in diagnosing ASD by evaluating entropy in the brain. Researchers may be able to develop new diagnostic methods for ASD which focuses on particular stimuli and brainwaves if they conduct more extensive studies with higher numbers and more rigorous study designs.

Integrated analysis of endoplasmic reticulum stress regulators' expression identifies distinct subtypes of autism spectrum disorder.

Endoplasmic reticulum (ER) stress has been demonstrated to play important roles in a variety of human diseases. However, their relevance to autism spectrum disorder (ASD) remains largely unknown. Herein, we aimed to investigate the expression patterns and potential roles of the ER stress regulators in ASD. The ASD expression profiles GSE111176 and GSE77103 were compiled from the Gene Expression Omnibus (GEO) database. ER stress score determined by the single sample gene set enrichment analysis (ssGSEA) was significantly higher in ASD patients. Differential analysis revealed that there were 37 ER stress regulators dysregulated in ASD. Based on their expression profile, the random forest and artificial neuron network techniques were applied to build a classifier that can effectively distinguish ASD from control samples among independent datasets. Weighted gene co-expression network analysis (WGCNA) screened out the turquoise module with 774 genes was closely related to the ER stress score. Through the overlapping results of the turquoise module and differential expression ER stress genes, hub regulators were gathered. The TF/miRNA-hub gene interaction networks were created. Furthermore, the consensus clustering algorithm was performed to cluster the ASD patients, and there were two ASD subclusters. Each subcluster has unique expression profiles, biological functions, and immunological characteristics. In ASD subcluster 1, the FAS pathway was more enriched, while subcluster 2 had a higher level of plasma cell infiltration as well as the BCR signaling pathway and interleukin receptor reaction reactivity. Finally, the Connectivity map (CMap) database was used to find prospective compounds that target various ASD subclusters. A total of 136 compounds were significantly enriched. In addition to some specific drugs which can effectively reverse the differential gene expression of each subcluster, we found that the PKC inhibitor BRD-K09991945 that targets Glycogen synthase kinase 3β (GSK3B) might have a therapeutic effect on both ASD subtypes that worth of the experimental validation. Our finding proved that ER stress plays a crucial role in the diversity and complexity of ASD, which may inform both mechanistic and therapeutic assessments of the disorder.

Paving the Way &amp; Translating PPH Resources towards Autismrelated Care

The link that might exert reliable control over morbidity, mortality and disabling rates as well as significantly optimize the efficacy of autism spectrum disorders (ASDs) treatment for those who had fallen ill and for persons-at-risk is Personalized &amp; Precision Medicine (PPM). Since there are significant molecular and neurobiological overlaps among ASD-related clinical phenotypes, tar-geted and/or smart (intelligent) treatments developed for a specific ASD subtype, may be help-ful in ASD of unknown etiology. Recent changes in diagnostics criteria have contributed to the broadening of the ASDs and left clinicians ill-equipped to treat the highly heterogeneous spec-trum that now includes toddlers and children with sensory and motor issues. And the biomarker discovery and thus the biomarker-driven drug design have the potential to tailor therapeutic inter-ventions to fit individualized ASD-related conditions in order to receive maximum benefits. This review summarizes the current advances in ASD biomarkers and discusses the potential imple-mentation of biomarkers in PPM-affiliated areas in the context of ASDs, whilst stressing the im-pact of PPM as the Science and ART and illustrating application of the different tools of the next step generation at the population, community and individualized levels.

Disrupted network integration and segregation involving the default mode network in autism spectrum disorder

Changes in the brain's default mode network (DMN) in the resting state are closely related to autism spectrum disorder (ASD). Module segmentation can effectively elucidate the neural mechanism of ASD and explore intra- and inter-network connections by means of the participation coefficient (PC). We used that resting-state fMRI data from 269 ASD patients and 340 healthy controls (HCs) in the current study. From the results, ASD subjects showed a significantly higher PC of the DMN than HC subjects. This difference was related to lower intra-module connections within the DMN and higher inter-network connections between the DMN and other networks. When the subjects were split into age groups, the results were verified in the 7–12- and 12–18-year-old age groups but not in the young adult group (18–25 years). When the subjects were divided according to different subtypes of ASD, the results were also observed in the classic autism and pervasive developmental disorder groups, but not in the Asperger disorder group. In conclusions, less developed network segregation in the DMN could be a valid biomarker for ASD. This provides network scientists with new insights into the intermodular connectivity configurations of complex networks from different dimensions in a systematic and comprehensive manner.

Brief postnatal exposure to bisphenol A affects apoptosis and gene expression in the medial prefrontal cortex and social behavior in rats with sex specificity

Bisphenol A (BPA) is an endocrine disruptor found in polycarbonate plastics and exposure in humans is nearly ubiquitous and it has widespread effects on cognitive, emotional, and reproductive behaviors in both humans and animal models. In our laboratory we previously found that perinatal BPA exposure results in a higher number of neurons in the adult male rat prefrontal cortex (PFC) and less play in adolescents of both sexes. Here we examine changes in the rate of postnatal apoptosis in the rat prefrontal cortex and its timing with brief BPA exposure. Because an increased number of neurons in the PFC is a characteristic of a subtype of autism spectrum disorder, we tested social preference following brief BPA exposure and also expression of a small group of genes. Males and females were exposed to BPA from postnatal days (P) 6 through 8 or from P10 through 12. Both exposures significantly decreased indicators of cell death in the developing medial prefrontal cortex in male subjects only. Additionally, males exposed to BPA from P6 – 8 showed decreased social preference and decreased cortical expression of Shank3 and Homer1, two synaptic scaffolding genes that have been implicated in social deficits. There were no significant effects of BPA in the female subjects. These results draw attention to the negative consequences following brief exposure to BPA during early development.

Genome-wide rare variant score associates with morphological subtypes of autism spectrum disorder

Defining different genetic subtypes of autism spectrum disorder (ASD) can enable the prediction of developmental outcomes. Based on minor physical and major congenital anomalies, we categorize 325 Canadian children with ASD into dysmorphic and nondysmorphic subgroups. We develop a method for calculating a patient-level, genome-wide rare variant score (GRVS) from whole-genome sequencing (WGS) data. GRVS is a sum of the number of variants in morphology-associated coding and non-coding regions, weighted by their effect sizes. Probands with dysmorphic ASD have a significantly higher GRVS compared to those with nondysmorphic ASD (P = 0.03). Using the polygenic transmission disequilibrium test, we observe an over-transmission of ASD-associated common variants in nondysmorphic ASD probands (P = 2.9 × 10−3). These findings replicate using WGS data from 442 ASD probands with accompanying morphology data from the Simons Simplex Collection. Our results provide support for an alternative genomic classification of ASD subgroups using morphology data, which may inform intervention protocols.