Potential Biomarker For Autism Spectrum Disorder Research Articles

Transcriptomic Analysis Reveals Sex-Based Differences in The Prefrontal Cortex of Autism Spectrum Disorder Patients.

The prevalence of neurological disorders often varies by sex, with conditions such as Alzheimer's disease and autism spectrum disorder (ASD) demonstrating notable differences in incidence. The aim of this study is to understand the molecular basis for these divergences in order to facilitate the creation of sex-specific therapeutic strategies. This study is a bioinformatic analysis of publicly available RNA sequencing datasets involving autism patients. The study utilized RNA sequencing data from postmortem human brains' prefrontal cortex, including 38 neurotypical controls and 34 individuals with ASD. The sequencing data was obtained from previously published papers, and we downloaded the raw data from SRA. We investigated the molecular basis of sex-biased presentation in ASD through comprehensive transcriptomic analysis. Comparative analysis of gene expression between male and female subjects, both autistic and unaffected, was conducted, using a significance level of ≤0.01. In autistic individuals, 136 genes demonstrated differential expression between sexes, predominantly upregulated in males, indicating a bias in male gene expression. Among these, 12 genes were identified as risk factors in the SFARI dataset. While most sex-biased genes were autosomal, expression differences on sex chromosomes were also observed in neurotypical subjects. Notable genes included TCF7L2, collagen family genes, and solute carrier family genes. In ASD males, extracellular matrix (ECM) organization emerged as a significant pathway, while immune-related processes were prominent in unaffected individuals. Our study highlights the impact of the ECM pathway in ASD, with notable differences between sexes, particularly in males. MIR424 shows promise as a potential biomarker for ASD in males. Recognizing the importance of sex differences in ASD transcriptomic research is crucial, as these variances provide insights into the disorder's pathophysiology and may guide the development of more personalized treatments for both sexes.

Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach.

Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present. The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene. We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis. In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.

Purine signaling pathway dysfunction in autism spectrum disorders: Evidence from multiple omics data.

Previous studies have suggested that the dysregulation of purine metabolism may be associated with autism spectrum disorder (ASD). Here, we adopted metabolomics and transcriptomics to verify and explore the underlying molecular mechanism of purine metabolism dysfunction in ASD and identify potential biomarkers within the purine metabolism pathway. Ultra-high-performance liquid chromatography-mass spectrometry was used to obtain the plasma metabolic profiles of 12 patients with ASD and 12 typically developing (TD) children. RNA sequencing was used to screen differentially expressed genes related to the purine metabolic pathway and purine receptor-coding genes in 24 children with ASD and 21 healthy controls. Finally, serum uric acid levels were compared in 80 patients with ASD and 174 TD children to validate the omics results. A total of 66 identified metabolites showed significant between-group differences. Network analysis showed that purine metabolism was the most strongly enriched. Uric acid was one of the most highlighted nodes within the network. The transcriptomic study revealed significant differential expression of three purine metabolism-related genes (adenosine deaminase, adenylosuccinate lyase, and bifunctional enzyme neoformans 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) transformylase/inosine monophosphate (IMP) cyclohydrolase) (p < 0.01) and five purinergic receptor genes (P2X7, P2Y2, P2Y6, P2Y8, and P2Y10) (p < 0.05). In the validation sample, there was a significant difference in serum uric acid levels between the two groups (p < 0.001), and the area under the curve for uric acid was 0.812 (sensitivity, 82.5%; specificity, 63.8%). Patients with ASD had dysfunctional purine metabolic pathways, and blood uric acid may be a potential biomarker for ASD.

An Integrative Bioinformatics Meta-Analysis of Microarray Data For Identifying Hub Genes As Biomarkers Of Autism Spectrum Disorder (ASD)

Autism spectrum disorder (ASD) is a neurodevelopmental disorder primarily affecting young children. ASD is a complex disease involving genetic and environmental factors. Environmental risk factors identified include gestational exposure to pollution, pesticides, maternal infections, and inflammation. Genetic mutations account for about 10 – 20% of ASD cases. Based on the Centre for Disease Control (CDC) in the United States, 1 in 68 children are affected with ASD. Recent advancements in genetic technologies have enabled the detection of biomarkers for the early detection of diseases and risk identification. Aim: This meta-analysis aims to determine the gene signatures involved in ASD. We conducted a meta-analysis to identify the differentially expressed genes (DEGs) in ASD microarray datasets comprising 122 ASD and 89 control peripheral blood mononuclear cell (PBMC) and whole blood samples from two microarray studies. We performed gene ontology, pathway enrichment, and protein-protein interaction (PPI) network analysis to identify associations between autism and altered gene expression levels. At a false discovery rate (FDR) &lt; 0.05, we identified 1862 DEGs; 1056 genes were upregulated, and 806 genes were downregulated. DEGs revealed that dysregulated genes were significantly enriched in the “Primary immunodeficiency pathway”, “Influenzae A”, “Epstein-Barr virus infection pathway”, and other signalling pathways from the analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Subsequently, protein-protein interactions (PPI) analysis identified SUMO1, SP1, EGR1, EP300, and VHL as hub genes to be the potential biomarkers for ASD. In total, eighteen differentially expressed hub genes could potentially be used as potential biomarkers for the diagnosis of ASD.

A proteomic analysis of urine biomarkers in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by early-onset social-communication challenges, restricted and repetitive behaviors, or unusual sensory-motor behaviors. A lack of specific biomarkers hinders the early diagnosis and treatment of this disease in many children. This study analyzes and validates potential urinary biomarkers using mass spectrometry proteomics. Global proteomics profiles of urine from 19 ASD patients and 19 healthy control subjects were compared to identify significantly changed proteins. These proteins were validated with targeted proteomics using parallel reaction monitoring (PRM) in an independent validation set consisting of samples from 40 ASD patients and 38 healthy controls. A total of 34 significantly changed proteins were found in the discovery set, among which seven proteins were identified as potential biomarkers for ASD through PRM assays in the validation set. Of these seven proteins, immunoglobulin kappa variable 4–1, immunoglobulin kappa variable 3–20, and immunoglobulin lambda variable 1–51 were up-regulated, while ATP synthase F1 subunit alpha, 10 kDa heat shock protein, apolipoprotein C-III, and arylsulfatase F were down-regulated. Six of these seven proteins support previous findings that ASD is accompanied by altered immune response and lipid metabolism, as well as mitochondrial dysfunction. This study lays the groundwork for additional research using biomarkers to clinically diagnose ASD. The proteomics and PRM raw data of this study have been deposited under the accession number IPX0002592000 at iProX. SignificanceThis study identified 34 proteins in urine of ASD patients that were significantly changed from the urinary proteins of healthy subjects using LC-MS/MS-based proteomics in a discovery set. Seven of these proteins were validated by PRM analysis in an independent validation set. This report represents the first description of combined label-free quantitative proteomics and PRM analysis of targeted proteins for discovery of ASD urinary biomarkers. The results will be helpful for early diagnosis and can provide additional insight into the molecular mechanisms of ASD.

Comorbidity Matters: Social Visual Attention in a Comparative Study of Autism Spectrum Disorder, Attention-Deficit/Hyperactivity Disorder and Their Comorbidity.

Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) represent two common neurodevelopmental disorders with considerable co-occurrence. Their comorbidity (ASD + ADHD) has been included in the latest diagnostic guidelines (DSM-V, 2013). The present study focuses on social visual attention that i) is a main aspect of social attention reflecting social cognition and ii) its atypicalities have been suggested as a potential biomarker for ASD. Considering the possible shared background of both disorders and their comorbidity, it is important to compare such traits directly. Here, 73 children and adolescents paired for age and IQ diagnosed with ASD (N = 12), ADHD (N = 21), comorbid ASD + ADHD (N = 15), and “typically developing” (TD) controls (N = 25), were shown static real-life social scenes while their gaze movements were recorded with eye-tracking. Scenes with two levels of social complexity were presented: low complexity (one person depicted) and high (four interacting individuals). Gaze fixation variables were investigated. Fixation duration on faces was significantly reduced only in ASD + ADHD which also required longer time to fixate all faces at least once. Fixation duration on faces in ASD was reduced, compared to TD, only when looking at scenes with high versus low social complexity. ADHD individuals did not differ from TD. Concluding, the observed alterations of social visual attention support the existence of possible dysfunctional particularities differentiating ASD, ADHD, and ASD + ADHD, which can be revealed with the new method of eye-tracking technique. The objective gaze measurements provided contribute to the development of biomarkers enabling early diagnosis, amelioration of care and further interventions specified for each group.

Autism spectrum disorder and severe social impairment associated with elevated plasma interleukin-8.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with an unclear etiology and pathophysiology. Previous studies have indicated that the dysregulation of cytokines may be involved in the pathogenesis of ASD and that the levels of cytokines may serve as potential biomarkers of this disorder. The current study employed a family triad-based case-control design to study the levels of plasma cytokines in families with ASD (n = 45 triads) and controls (n = 38 triads) with a Human Cytokine Twenty-Five-Plex Kit. The Social Responsiveness Scale (SRS) was used to measure social impairment of ASD children. After controlling for the levels of parental cytokines, we identified that interferon-α (IFN-α), interleukin-7 (IL-7), IL-8, IFN-γ-inducible protein-10, and macrophage inflammatory protein-1β were associated with ASD, and IL-8 was the only cytokine also associated with the levels of both parental cytokines in the offspring-parents regression analysis and three subdomains of SRS (social awareness, cognition, and motivations) in the children with ASD. The receiver operating characteristic curve showed that the log-transformed IL-8 level discriminated children with autism from controls with an area under the curve of 0.858 (95% confidence interval: 0.777-0.939). Our study suggests that IL-8 is a potential biomarker for ASD and may be involved in the pathogenesis of ASD. The study suggests that IL-8 is a promising biomarker for ASD and may be involved in the pathogenesis of ASD. Only a very few studies have reported the parental cytokine levels. The significant strength of this article is that we applied the family triad-based approach to explore cytokine levels in families with autism and controls. There are no objective biomarkers, making the accurate diagnosis, prognostic prediction and effective treatment difficult, and our study provides promising results.

Decreased interhemispheric functional connectivity rather than corpus callosum volume as a potential biomarker for autism spectrum disorder

Previous studies have implicated both structural and interhemispheric functional connectivity alterations in autism spectrum disorder (ASD) although findings are inconsistent. There is evidence that connectivity between corresponding regions in each hemisphere (homotopic) may be of particular importance and therefore the present study used the Autism Brain Imaging Data Exchange data to investigate ASD-related resting-state and structural alterations as well as associations with symptom severity (Autism Diagnostic Observation Schedule – ADOS). We employed a voxel-mirrored homotopic connectivity analysis to compare interhemispheric functional connectivity in 409 ASD and 455 typically developing subjects. Additionally, voxel-based morphology was used to investigate volumetric differences in the corpus callosum, the major commissure for interhemispheric communication. ASD subjects demonstrated significant reductions in interhemispheric functional connections between regions in the default mode network (medial prefrontal, posterior cingulate and precuneus), salience network (anterior cingulate and insula), mirror neuron/motor systems (inferior frontal gyrus, inferior parietal lobule, precentral gyrus, supplementary motor area), thalamus and auditory (superior temporal gyrus) and visual systems (lingual, fusiform and inferior occipital gyri). A support vector machine analysis based on interhemispheric connectivity (but not all homotopic) revealed an average classification accuracy of 88.70% for distinguishing ASD from controls across different sites. In ASD subjects symptom severity as measured by ADOS was negatively associated with posterior cingulate, insula and superior temporal gyrus homotopic functional connectivity. While ASD subjects displayed reduced anterior and posterior callosal volumes they were not associated with either ADOS scores or functional connectivity changes. Our findings suggest that reduced interhemispheric connectivity involving homotopic regions may be a potential biomarker for ASD with bilateral posterior cingulate, insula and superior temporal gyrus connections being associated with symptom severity.

Urine Organic Acids as Potential Biomarkers for Autism-Spectrum Disorder in Chinese Children.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that lacks clear biological biomarkers. Existing diagnostic methods focus on behavioral and performance characteristics, which complicates the diagnosis of patients younger than 3 years-old. The purpose of this study is to characterize metabolic features of ASD that could be used to identify potential biomarkers for diagnosis and exploration of ASD etiology. We used gas chromatography-mass spectrometry (GC/MS) to evaluate major metabolic fluctuations in 76 organic acids present in urine from 156 children with ASD and from 64 non-autistic children. Three algorithms, Partial Least Squares-Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were used to develop models to distinguish ASD from typically developing (TD) children and to detect potential biomarkers. In an independent testing set, full model of XGBoost with all 76 acids achieved an AUR of 0.94, while reduced model with top 20 acids discovered by voting from these three algorithms achieved 0.93 and represent a good collection of potential ASD biomarkers. In summary, urine organic acids detection with GC/MS combined with XGBoost algorithm could represent a novel and accurate strategy for diagnosis of autism and the discovered potential biomarkers could be valuable for future research on the pathogenesis of autism and possible interventions.

An Observational Study With the Janssen Autism Knowledge Engine (JAKE®) in Individuals With Autism Spectrum Disorder.

Objective: The Janssen Autism Knowledge Engine (JAKE®) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components.Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures.Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was “easy” (69%, 74/108) or “very easy” (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93–100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported.Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies.Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT02668991

Profiling of Circulating Serum MicroRNAs in Children with Autism Spectrum Disorder using Stem-loop qRT-PCR Assay.

Development of biomarkers for autism spectrum disorder (ASD) has still remained a challenge to date. Recently, alterations of the expression of microRNAs (miRNAs) in peripheral blood, serum and post-mortem brain tissue have been linked to ASD. miRNAs are known to be secreted by various cell types and can mediate transmission of information into recipient cells and to modulate their physiological functions. On this basis it is assumed that circulating miRNAs could be useful biomarkers for the diagnosis or prognosis of pathological conditions. The aim of this study was to test whether circulating miRNAs display differential expression profile in serum of ASD patients. The relative expression levels of 42 miRNAs were analyzed by stem-loop qRT-PCR assay in the serum of ASD patients compared to healthy controls. The results indicated that 11 miRNAs in ASD patients were substantially higher expressed than these in control subjects, and 29 miRNAs were lower expressed, respectively. In addition, target gene analysis displayed that the altered serum miRNAs targeted some important genes like alpha 1C subunit of voltage-dependent calcium channel, L type, (CACNA1C), beta 1 subunit of voltage-dependent calcium channel (CACNB1) and other genes involved in epigenetic processes like dicer 1, coding ribonuclease type III (DICER). Our results suggested that differentially expressed miRNAs in serum might be involved in ASD molecular pathways, and serum miR-424-5p, miR-197- 5p, miR-328-3p, miR-500a-5p, miR-619-5p, miR-3135a, miR-664a-3p, and miR- 365a-3p might be able to serve as potential biomarkers for ASD because they displayed significant alterations in the expression profile in children diagnosed with ASD.

Altered plasma levels of chemokines in autism and their association with social behaviors

Autism Spectrum Disorder (ASD) is a group of neurodevelopment disorders with an unclear etiology. Chemokines have been implicated in the etiology and pathogenesis of ASD. The current study investigated the plasma levels of seven chemokines (RANTES, Eotaxin, MIP-1 α, MIP-1 β, MCP-1, IP-10, and MIG) in 42 young autistic patients and 35 age-matched typically developing (TD) children. The study also tested the association between these chemokine levels and social behaviors, as measured by the Social Responsiveness Scale (SRS). Compared to the TD children, RANTES, MIP-1α, and MIP-1β were higher, while IP-10 and MIG were lower in the autistic patients, after correcting for multiple comparisons. Among these seven chemokines, MIP-1α, MIP-1β and IP-10 levels were found to be associated with social behaviors in all the participants. Moreover, MIP-1α and IP-10 were found to be independent predictors of social behaviors. The results of our study support the hypothesis that altered chemokine levels are involved in the pathophysiology of ASD and they indicate that chemokines plasma levels could be potential biomarkers for ASD.

Altered tactile processing in children with autism spectrum disorder.

Although tactile reactivity issues are commonly reported in children with autism spectrum disorder (ASD), the underlying mechanisms are poorly understood. Less feed-forward inhibition has been proposed as a potential mechanism for some symptoms of ASD. We tested static and dynamic tactile thresholds as a behavioral proxy of feed-forward inhibition in 42 children (21 children with ASD and 21 typically developing [TD] children). Subthreshold conditioning typically raises the dynamic detection threshold, thus comparison of the dynamic to the static threshold generates a metric that predicts gamma-aminobutyric acid (GABA) mediated feed-forward inhibition. Children with ASD had marginally higher static thresholds and a significantly lower ratio between thresholds as compared with TD children. The lower ratio, only seen in children with ASD, might be indicative of less inhibition. Static thresholds were correlated with autism spectrum quotient scores, indicating the higher the tactile threshold, the more ASD traits. The amount of feed-forward inhibition (ratio between dynamic/static) was negatively correlated with autism diagnostic observation schedule repetitive behavior scores, meaning the less inhibition the more ASD symptoms. In summary, children with ASD showed altered tactile processing compared with TD children; thus measuring static and dynamic thresholds could be a potential biomarker for ASD and might be useful for prediction of treatment response with therapeutics, including those that target the GABAergic system. Autism Res 2016, 9: 616-620. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.