A novel blood-based biomarker for detection of autism spectrum disorders

A Systematic Investigation of Complement and Coagulation-Related Protein in Autism Spectrum Disorder Using Multiple Reaction Monitoring Technology

Autism spectrum disorder (ASD) is one of the common neurodevelopmental disorders in children. Its etiology and pathogenesis are poorly understood. Previous studies have suggested potential changes in the complement and coagulation pathways in individuals with ASD. In this study, using multiple reactions monitoring proteomic technology, 16 of the 33 proteins involved in this pathway were identified as differentially-expressed proteins in plasma between children with ASD and controls. Among them, CFHR3, C4BPB, C4BPA, CFH, C9, SERPIND1, C8A, F9, and F11 were found to be altered in the plasma of children with ASD for the first time. SERPIND1 expression was positively correlated with the CARS score. Using the machine learning method, we obtained a panel composed of 12 differentially-expressed proteins with diagnostic potential for ASD. We also reviewed the proteins changed in this pathway in the brain and blood of patients with ASD. The complement and coagulation pathways may be activated in the peripheral blood of children with ASD and play a key role in the pathogenesis of ASD.

A Proteomics Investigation of Salivary Profiles as Potential Biomarkers for Autism Spectrum Disorder (ASD)

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects approximately 1/68 children, with a more recent study suggesting numbers as high as 1/36. According to Diagnostic and Statistical Manual of Mental Disorders, the etiology of ASD is unknown and diagnosis of this disorder is behavioral. There is currently no biomarker signature for ASD, however, identifying a biomarker signature is crucial as it would aid in diagnosis, identifying treatment targets, monitoring treatments, and identifying the etiology of the disorder. Here we used nanoliquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) to investigate the saliva from individuals with ASD and matched controls in a 14 vs 14 study. We found numerous proteins to have statistically significant dysregulations, including lactotransferrin, transferrin, polymeric immunoglobulin receptor, Ig A L, Ig J chain, mucin 5 AC, and lipocalin 1 isoform X1. These findings are consistent with previous studies by our lab, and others, and point to dysregulations in the immune system, lipid metabolism and/or transport, and gastrointestinal disturbances, which are common and reoccurring topics in ASD research.

Dysregulation of complement system in neuropsychiatric disorders: A mini review

Complement system is one of the most important defense mechanisms of the innate immune system. In addition to their roles in immune regulation, complement proteins are also involved in neurodevelopment and adult brain plasticity. Complement dysregulation has been shown in neurodevelopmental disorders including schizophrenia and autism spectrum disorder as well as in mood disorders. A number of clinical as well as genetic studies suggest the role of complement proteins in the cortical thinning and excessive synaptic pruning frequently associated with schizophrenia. The changes in complement proteins are also associated with the pathophysiology of autism spectrum disorder, major depressive disorder and bipolar disorder, but warrant further research. In addition, rodent models suggest a strong case for complement system in anxiety-like behavior. In this article, we review the recent findings on the role of complement system in neuropsychiatric disorders. The possible uses for future complement targeted therapies are also discussed.

A Combined Proteomics and Metabolomics Profiling to Investigate the Genetic Heterogeneity of Autistic Children

Autism spectrum disorder (ASD) has become one of the most common neurological developmental disorders in children. However, the study of ASD diagnostic markers faces significant challenges due to the existence of heterogeneity. In this study, genetic testing was performed on children who were clinically diagnosed with ASD. Children with ASD susceptibility genes and healthy controls were studied. The proteomics of plasma and peripheral blood mononuclear cells (PBMCs) as well as plasma metabolomics were carried out. The results showed that although there was genetic heterogeneity in children with ASD, the differentially expressed proteins (DEPs) in plasma, peripheral blood mononuclear cells, and differential metabolites in plasma could still effectively distinguish autistic children from controls. The mechanism associated with them focuses on several common and previously reported mechanisms of ASD. The biomarkers for ASD diagnosis could be found by taking differentially expressed proteins and differential metabolites into consideration. Integrating omics data, glycerophospholipid metabolism and N-glycan biosynthesis might play a critical role in the pathogenesis of ASD.

Discriminant analysis and binary logistic regression enable more accurate prediction of autism spectrum disorder than principal component analysis

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impaired social interaction and restricted, repetitive behavior. Multiple studies have suggested mitochondrial dysfunction, glutamate excitotoxicity, and impaired detoxification mechanism as accepted etiological mechanisms of ASD that can be targeted for therapeutic intervention. In the current study, blood samples were collected from 40 people with autism and 40 control participants after informed consent and full approval from the Institutional Review Board of King Saud University. Sodium (Na⁺), Potassium (K⁺), lactate dehydrogenase (LDH), glutathione-s-transferase (GST), and mitochondrial respiratory chain complex I (MRC1) were measured in plasma of both groups. Predictive models were established to discriminate individuals with ASD from controls. The predictive power of these five variables, individually and in combination, was compared using the area under a ROC curve (AUC). We compared the performance of principal component analysis (PCA), discriminant analysis (DA), and binary logistic regression (BLR) as ways to combine single variables and create the predictive models. K⁺ had the highest AUC (0.801) of any single variable, followed by GST, LDH, Na⁺, and MRC1, respectively. Combining the five variables resulted in higher AUCs than those obtained using single variables across all models. Both DA and BLR were superior to PCA and comparable to each other. In our study, the combination of Na⁺, K⁺, LDH, GST, and MRC1 showed the highest promise in discriminating individuals with autism from controls. These results provide a platform that can potentially be used to verify the efficacy of our models with a larger sample size or evaluate other biomarkers.

The Complement System in the Central Nervous System: From Neurodevelopment to Neurodegeneration

The functions of the complement system to both innate and adaptive immunity through opsonization, cell lysis, and inflammatory activities are well known. In contrast, the role of complement in the central nervous system (CNS) which extends beyond immunity, is only beginning to be recognized as important to neurodevelopment and neurodegeneration. In addition to protecting the brain against invasive pathogens, appropriate activation of the complement system is pivotal to the maintenance of normal brain function. Moreover, overactivation or dysregulation may cause synaptic dysfunction and promote excessive pro-inflammatory responses. Recent studies have provided insights into the various responses of complement components in different neurological diseases and the regulatory mechanisms involved in their pathophysiology, as well as a glimpse into targeting complement factors as a potential therapeutic modality. However, there remain significant knowledge gaps in the relationship between the complement system and different brain disorders. This review summarizes recent key findings regarding the role of different components of the complement system in health and pathology of the CNS and discusses the therapeutic potential of anti-complement strategies for the treatment of neurodegenerative conditions.

Brain exosomes as minuscule information hub for Autism Spectrum Disorder

INTRODUCTION

Autism spectrum disorder (ASD) is a neurodevelopmental disorder initiating in the first three years of life. Early initiation of management therapies can significantly improve the health and quality of life of ASD subjects. Thus, indicating the need for suitable biomarkers for the early identification of ASD. Various biological domains were investigated in the quest for reliable biomarkers. However, most biomarkers are in the preliminary stage, and clinical validation is yet to be defined. Exosome based research gained momentum in various Central Nervous System disorders for biomarker identification. However, the utility and prospect of exosomes in ASD is still underexplored.

AREAS COVERED

In the present review, we summarized the biomarker discovery current status and the future of brain-specific exosomes in understanding pathophysiology and its potential as a biomarker. The studies reviewed herein were identified via systematic search (dated: June 2021) of PubMed using variations related to autism (ASD OR autism OR Autism spectrum disorder) AND exosomes AND/OR biomarkers.

EXPERT OPINION

As exosomess are highly relevant in brain disorders like ASD, direct access to brain tissue for molecular assessment is ethically impossible. Thus investigating the brain-derived exosomes would undoubtedly answer many unsolved aspects of the pathogenesis and provide reliable biomarkers.

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. SIGNIFICANCE: This 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.

Impaired Thiol/Disulfide Homeostasis in Children Diagnosed with Autism: A Case-Control Study

Although genetic factors occupy an important place in the development of autism spectrum disorder (ASD), oxidative stress and exposure to environmental toxicants have also been linked to the condition. The aim of this study was to examine dynamic thiol/disulfide homeostasis in children diagnosed with ASD. Forty-eight children aged 3-12 years diagnosed with ASD and 40 age- and sex-matched healthy children were included in the study. A sociodemographic data form was completed for all the cases, and the Childhood Autism Rating Scale (CARS) was applied to the patients. Thiol/disulfide parameters in serum were measured in all cases and compared between the two groups. Mean native thiol, total thiol concentrations (μmol/L), and median reduced thiol ratios were significantly lower in the ASD group than in the control group (p = 0.001 for all). Median disulfide concentrations (μmol/L), redox potential, and median oxidized thiol ratios were significantly higher in the ASD group than in the control group (p = 0.001, p = 0.001, and p = 0.001, respectively). ROC analysis revealed that area under the curve (AUC) values with "excellent discriminatory potential," for native thiol, total thiol, the reduced thiol ration, the oxidized thiol ratio, and redox potential and with "acceptable discriminatory potential" for disulfide were significantly capable of differentiating individuals with ASD from healthy individuals. No correlation was determined between the severity of autism and laboratory parameters. Impaired dynamic thiol/disulfide homeostasis was observed in children with ASD, suggesting that dynamic thiol/disulfide homeostasis in serum may be of diagnostic value in autism.

Protein Biomarkers of Autism Spectrum Disorder Identified by Computational and Experimental Methods

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects millions of people worldwide. However, there are currently no reliable biomarkers for ASD diagnosis. Materials and Methods: The strategy of computational prediction combined with experimental verification was used to identify blood protein biomarkers for ASD. First, brain tissue-based transcriptome data of ASD were collected from Gene Expression Omnibus database and analyzed to find ASD-related genes by bioinformatics method of significance analysis of microarrays. Then, a prediction program of blood-secretory proteins was applied on these genes to predict ASD-related proteins in blood. Furthermore, ELISA was used to verify these proteins in plasma samples of ASD patients. Results: A total of 364 genes were identified differentially expressed in brain tissue of ASD, among which 59 genes were predicted to encode ASD-related blood-secretory proteins. After functional analysis and literature survey, six proteins were chosen for experimental verification and five were successfully validated. Receiver operating characteristic curve analyses showed that the area under the curve of SLC25A12, LIMK1, and RARS was larger than 0.85, indicating that they are more powerful in discriminating ASD cases from controls. Conclusion: SLC25A12, LIMK1, and RARS might serve as new potential blood protein biomarkers for ASD. Our findings provide new insights into the pathogenesis and diagnosis of ASD.

Early screening of autism spectrum disorder using cry features

The increase in the number of children with autism and the importance of early autism intervention has prompted researchers to perform automatic and early autism screening. Consequently, in the present paper, a cry-based screening approach for children with Autism Spectrum Disorder (ASD) is introduced which would provide both early and automatic screening. During the study, we realized that ASD specific features are not necessarily observable in all children with ASD and in all instances collected from each child. Therefore, we proposed a new classification approach to be able to determine such features and their corresponding instances. To test the proposed approach a set of data relating to children between 18 to 53 months which had been recorded using high-quality voice recording devices and typical smartphones at various locations such as homes and daycares was studied. Then, after preprocessing, the approach was used to train a classifier, using data for 10 boys with ASD and 10 Typically Developed (TD) boys. The trained classifier was tested on the data of 14 boys and 7 girls with ASD and 14 TD boys and 7 TD girls. The sensitivity, specificity, and precision of the proposed approach for boys were 85.71%, 100%, and 92.85%, respectively. These measures were 71.42%, 100%, and 85.71% for girls, respectively. It was shown that the proposed approach outperforms the common classification methods. Furthermore, it demonstrated better results than the studies which used voice features for screening ASD. To pilot the practicality of the proposed approach for early autism screening, the trained classifier was tested on 57 participants between 10 to 18 months. These 57 participants consisted of 28 boys and 29 girls and the results were very encouraging for the use of the approach in early ASD screening.

Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements

An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.

Nuclear Factor Kappa B in Autism Spectrum Disorder: A Systematic Review

BACKGROUND

The nuclear factor kappa B (NF-κB) is composed of a series of transcription factors, which are involved in the expression of a plethora of target genes, many of these genes contributing to the regulation of inflammatory responses. Consistent with its central role in inflammatory responses, existing studies of the neurobiological basis for ASD propose the involvement of NF-κB in the etiology of this disorder.

OBJECTIVES

The present review aimed to systematically characterize extant literatures regarding the role of NF-κB in the etiology of ASD through data derived from both human studies and animal models.

METHODS

A systematic electronic search was conducted for records indexed within Pubmed, EMBASE, or Web of Science to identify potentially eligible studies. Study inclusion and data extraction was agreed by two independent authors after reviewing the abstract and full text.

RESULTS

Among the 371 articles identified in the initial screening, 18 articles met the eligibility criteria for this review, including 14 human case-control studies compared the expression or activation of NF-κB between ASD cases and controls as well as 4 animal studies used mouse model of ASD to examine the level of NF-κB and further evaluate its changes after different drug treatments. These included 18 studies, although relatively small in quantity, appear to support the role of NF-κB in the etiology of ASD.

CONCLUSIONS

Evidence generated from both human studies and animal models supported the involvement of NF-κB in the neurobiological basis of ASD, despite some concern about whether it functions as a primary contributor causes ASD onset or rather an ancillary factor regulates ASD pathogenesis. The increased understanding of NF-κB in the neurobiological basis of ASD could aid the emergence of clinically relevant diagnostic biomarkers and novel therapeutic strategies acting on the underlying disease pathogenesis. These results suggested that potential methodological differences between studies need to be accounted for and keep open the discussion over the existence of aberrantly NF-κB signaling in ASD subjects.

Genomics, transcriptomics, proteomics and big data analysis in the discovery of new diagnostic markers and targets for therapy development

Highly complex endophenotypes and underlying molecular mechanisms have prevented effective diagnosis and treatment of autism spectrum disorder. Despite extensive studies to identify relevant biosignatures, no biomarker and therapeutic targets are available in the current clinical practice. While our current knowledge is still largely incomplete, -omics technology and machine learning-based big data analysis have provided novel insights on the etiology of autism spectrum disorders, elucidating systemic impairments that can be translated into biomarker and therapy target candidates. However, more integrated and sophisticated approaches are vital to realize molecular stratification and individualized treatment strategy. Ultimately, systemic approaches based on -omics and big data analysis will significantly contribute to more effective biomarker and therapy development for autism spectrum disorder.

Autistic feature as a presentation of Inborn Errors of Metabolism

Autism spectrum disorder (ASD) is a category of neurodevelopmental disorders characterized by social and communication impairment and restricted or repetitive behaviors. The pathogenesis of ASD is not well understood and it's proved that genetic is strongly associated with ASD in 5 to 25% of cases. Inborn errors of metabolism(IEMs), defined by a vast array of disorders that are caused by specific enzyme deficiencies or transport protein defects, is as frequent as in 1 in 800 births. IEMs can manifest several psychiatric or behavioral manifestations such as self-injuriesincreased activity and aggression, personality changes, paranoia, depression, catatonia, and psychosis. IEMs underlie autistic symptoms in less than 5% of cases. The literature on the association between ASD and respiratory chain abnormalities is growing, including complex III/IV deficiency and MELAS (mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes) syndrome, as well as glucose-6-phosphate dehydrogenase deficiency. Google Scholar, Pubmed, and SCOPUS databases were searched using a combination of the following keywords: "autism spectrum disorder", "autism spectrum", "autistic feature" and "inborn error of metabolism", " IEM", "congenital error of metabolism". Initially, 655 articles were found and our expert and methodologist altogether selected 187 articles based on the titles, relevance, and text language. After reading full texts, 37 studies were selected for review. We think it's best to consider IEMs in children with syndromic ASD and/or if there is a strong familial history of autism or parental consanguineous marriage.

Emerging biomarkers in autism spectrum disorder: a systematic review

Autism spectrum disorder (ASD) affects approximately 2% of children in the United States (US) yet its etiology is unclear and effective treatments are lacking. Therapeutic interventions are most effective if started early in life, yet diagnosis often remains delayed, partly because the diagnosis of ASD is based on identifying abnormal behaviors that may not emerge until the disorder is well established. Biomarkers that identify children at risk during the pre-symptomatic period, assist with early diagnosis, confirm behavioral observations, stratify patients into subgroups, and predict therapeutic response would be a great advance. Here we underwent a systematic review of the literature on ASD to identify promising biomarkers and rated the biomarkers in regards to a Level of Evidence and Grade of Recommendation using the Oxford Centre for Evidence-Based Medicine scale. Biomarkers identified by our review included physiological biomarkers that identify neuroimmune and metabolic abnormalities, neurological biomarkers including abnormalities in brain structure, function and neurophysiology, subtle behavioral biomarkers including atypical development of visual attention, genetic biomarkers and gastrointestinal biomarkers. Biomarkers of ASD may be found prior to birth and after diagnosis and some may predict response to specific treatments. Many promising biomarkers have been developed for ASD. However, many biomarkers are preliminary and need to be validated and their role in the diagnosis and treatment of ASD needs to be defined. It is likely that biomarkers will need to be combined to be effective to identify ASD early and guide treatment.

Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy

Current diagnosis of autism spectrum disorder (ASD) is based on assessment of behavioral symptoms, although there is strong evidence that ASD affects multiple organ systems including the gastrointestinal (GI) tract. This study used Fisher discriminant analysis (FDA) to evaluate plasma metabolites from 18 children with ASD and chronic GI problems (ASD + GI cohort) and 20 typically developing (TD) children without GI problems (TD − GI cohort). Using three plasma metabolites that may represent three general groups of metabolic abnormalities, it was possible to distinguish the ASD + GI cohort from the TD − GI cohort with 94% sensitivity and 100% specificity after leave-one-out cross-validation. After the ASD + GI participants underwent Microbiota Transfer Therapy with significant improvement in GI and ASD-related symptoms, their metabolic profiles shifted significantly to become more similar to the TD − GI group, indicating potential utility of this combination of plasma metabolites as a biomarker for treatment efficacy. Two of the metabolites, sarcosine and inosine 5′-monophosphate, improved greatly after treatment. The third metabolite, tyramine O-sulfate, showed no change in median value, suggesting it and correlated metabolites to be a possible target for future therapies. Since it is unclear whether the observed differences are due to metabolic abnormalities associated with ASD or with GI symptoms (or contributions from both), future studies aiming to classify ASD should feature TD participants with GI symptoms and have larger sample sizes to improve confidence in the results.

Biomarkers for Autism Spectrum Disorders (ASD): A Meta-analysis

OBJECTIVE

To compare the reported accuracy and sensitivity of the various modalities used to diagnose autism spectrum disorders (ASD) in efforts to help focus further biomarker research on the most promising methods for early diagnosis.

METHODS

The Medline scientific literature database was searched to identify publications assessing potential clinical ASD biomarkers. Reports were categorized by the modality used to assess the putative markers, including protein, genetic, metabolic, or objective imaging methods. The reported sensitivity, specificity, area under the curve, and overall agreement were summarized and analyzed to determine weighted averages for each diagnostic modality. Heterogeneity was measured using the I² test.

RESULTS

Of the 71 papers included in this analysis, each belonging to one of five modalities, protein-based followed by metabolite-based markers provided the highest diagnostic accuracy, each with a pooled overall agreement of 83.3% and respective weighted area under the curve (AUC) of 89.5% and 88.3%. Sensitivity provided by protein markers was highest (85.5%), while metabolic (85.9%) and protein markers (84.7%) had the highest specificity. Other modalities showed degrees of sensitivity, specificity, and overall agreements in the range of 73%-80%.

CONCLUSIONS

Each modality provided for diagnostic accuracy and specificity similar or slightly higher than those reported for the gold-standard Autism Diagnostic Observation Schedule (ADOS) instrument. Further studies are required to identify the most predictive markers within each modality and to evaluate biological pathways or clustering with possible etiological relevance. Analyses will also be necessary to determine the potential of these novel biomarkers in diagnosing pediatric patients, thereby enabling early intervention.

Urinary Markers of Oxidative Stress in Children with Autism Spectrum Disorder (ASD)

Background: Autism spectrum disorder (ASD) is a developmental disorder characterized by deficits in social interaction, restricted interest and repetitive behavior. Oxidative stress in response to environmental exposure plays a role in virtually every human disease and represents a significant avenue of research into the etiology of ASD. The aim of this study was to explore the diagnostic utility of four urinary biomarkers of oxidative stress. Methods: One hundred and thirty-nine (139) children and adolescents with ASD (89% male, average age = 10.0 years, age range = 2.1 to 18.1 years) and 47 healthy children and adolescents (49% male, average age 9.2, age range = 2.5 to 20.8 years) were recruited for this study. Their urinary 8-OH-dG, 8-isoprostane, dityrosine and hexanoil-lisine were determined by using the ELISA method. Urinary creatinine was determined with the kinetic Jaffee reaction and was used to normalize all biochemical measurements. Non-parametric tests and support vector machines (SVM) with three different kernel functions (linear, radial, polynomial) were used to explore and optimize the multivariate prediction of an ASD diagnosis based on the collected biochemical measurements. The SVM models were first trained using data from a random subset of children and adolescents from the ASD group (n = 70, 90% male, average age = 9.7 years, age range = 2.1 to 17.8 years) and the control group (n = 24, 45.8% male, average age = 9.4 years, age range = 2.5 to 20.8 years) using bootstrapping, with additional synthetic minority over-sampling (SMOTE), which was utilized because of unbalanced data. The computed SVM models were then validated using the remaining data from children and adolescents from the ASD (n = 69, 88% male, average age = 10.2 years, age range = 4.3 to 18.1 years) and the control group (n = 23, 52.2% male, average age = 8.9 years, age range = 2.6 to 16.7 years). Results: Using a non-parametric test, we found a trend showing that the urinary 8-OH-dG concentration was lower in children with ASD compared to the control group (unadjusted p = 0.085). When all four biochemical measurements were combined using SVMs with a radial kernel function, we could predict an ASD diagnosis with a balanced accuracy of 73.4%, thereby accounting for an estimated 20.8% of variance (p < 0.001). The predictive accuracy expressed as the area under the curve (AUC) was solid (95% CI = 0.691-0.908). Using the validation data, we achieved significantly lower rates of classification accuracy as expressed by the balanced accuracy (60.1%), the AUC (95% CI = 0.502-0.781) and the percentage of explained variance (R² = 3.8%). Although the radial SVMs showed less predictive power using the validation data, they do, together with ratings of standardized SVM variable importance, provide some indication that urinary levels of 8-OH-dG and 8-isoprostane are predictive of an ASD diagnosis. Conclusions: Our results indicate that the examined urinary biomarkers in combination may differentiate children with ASD from healthy peers to a significant extent. However, the etiological importance of these findings is difficult to assesses, due to the high-dimensional nature of SVMs and a radial kernel function. Nonetheless, our results show that machine learning methods may provide significant insight into ASD and other disorders that could be related to oxidative stress.

Advances in Biomarker Studies in Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a neurological and developmental condition that begins early in childhood and lasts throughout life. The epidemiology of ASD is continuously increasing all over the world with huge social and economical burdens. As the etiology of autism is not completely understood, there is still no medication available for the treatment of this disorder. However, some behavioral interventions are available to improve the core and associated symptoms of autism, particularly when initiated at an early stage. Thus, there is an increasing demand for finding biomarkers for ASD. Although diagnostic biomarkers have not yet been established, research efforts have been carried out in neuroimaging and biological analyses including genomics and gene testing, proteomics, metabolomics, transcriptomics, and studies of the immune system, inflammation, and microRNAs. Here, we will review the current progress in these fields and focus on new methods, developments, research strategies, and studies of blood-based biomarkers.

Serum proteomic profiling for autism using magnetic bead-assisted matrix-assisted laser desorption ionization time-of-flight mass spectrometry: a pilot study

BACKGROUND

The pathogenesis of autism spectrum disorders remains elusive and currently there are no diagnostic or predictive biomarkers in autism available. Proteomic profiling has been used in a wide range of neurodevelopmental disorder studies, which could produce deeper perceptions of the molecular bases behind certain disease and potentially becomes useful in discovering biomarkers in autism spectrum disorders.

METHODS

Serum samples were collected from autistic children about 3 years old in age (n = 32) and healthy controls (n = 20) in similar age and gender. The samples were identified specific proteins that are differentially expressed by magnetic bead-based pre-fractionation and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-ToF-MS).

RESULTS

Eight protein peaks were significantly different in autistic children from the healthy controls (P < 0.0001). The two peaks with the most significant differences were 6428 and 7758 Da in size.

CONCLUSION

According to differences in serum protein profiles between the autistic children and healthy controls, this study identified a set of differentially expressed proteins those are significant for further evaluation and might function as biomarkers in autism.

Advanced glycation endproducts, dityrosine and arginine transporter dysfunction in autism - a source of biomarkers for clinical diagnosis

Background

Clinical chemistry tests for autism spectrum disorder (ASD) are currently unavailable. The aim of this study was to explore the diagnostic utility of proteotoxic biomarkers in plasma and urine, plasma protein glycation, oxidation, and nitration adducts, and related glycated, oxidized, and nitrated amino acids (free adducts), for the clinical diagnosis of ASD.

Methods

Thirty-eight children with ASD (29 male, 9 female; age 7.6 ± 2.0 years) and 31 age-matched healthy controls (23 males, 8 females; 8.6 ± 2.0 years) were recruited for this study. Plasma protein glycation, oxidation, and nitration adducts and amino acid metabolome in plasma and urine were determined by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry. Machine learning methods were then employed to explore and optimize combinations of analyte data for ASD diagnosis.

Results

We found that children with ASD had increased advanced glycation endproducts (AGEs), N_ε-carboxymethyl-lysine (CML) and N_ω-carboxymethylarginine (CMA), and increased oxidation damage marker, dityrosine (DT), in plasma protein, with respect to healthy controls. We also found that children with ASD had increased CMA free adduct in plasma ultrafiltrate and increased urinary excretion of oxidation free adducts, alpha-aminoadipic semialdehyde and glutamic semialdehyde. From study of renal handling of amino acids, we found that children with ASD had decreased renal clearance of arginine and CMA with respect to healthy controls. Algorithms to discriminate between ASD and healthy controls gave strong diagnostic performance with features: plasma protein AGEs—CML, CMA—and 3-deoxyglucosone-derived hydroimidazolone, and oxidative damage marker, DT. The sensitivity, specificity, and receiver operating characteristic area-under-the-curve were 92%, 84%, and 0.94, respectively.

Conclusions

Changes in plasma AGEs were likely indicative of dysfunctional metabolism of dicarbonyl metabolite precursors of AGEs, glyoxal and 3-deoxyglucosone. DT is formed enzymatically by dual oxidase (DUOX); selective increase of DT as an oxidative damage marker implicates increased DUOX activity in ASD possibly linked to impaired gut mucosal immunity. Decreased renal clearance of arginine and CMA in ASD is indicative of increased arginine transporter activity which may be a surrogate marker of disturbance of neuronal availability of amino acids. Data driven combination of these biomarkers perturbed by proteotoxic stress, plasma protein AGEs and DT, gave diagnostic algorithms of high sensitivity and specificity for ASD.

Electronic supplementary material

The online version of this article (10.1186/s13229-017-0183-3) contains supplementary material, which is available to authorized users.

iTRAQ-Based Proteomic Analysis Reveals Protein Profile in Plasma from Children with Autism

PURPOSE

Autism is a childhood neurological disorder with poorly understood etiology and pathology. This study is designed to identify differentially expressed proteins that might serve as potential biomarkers for autism.

EXPERIMENTAL DESIGN

We perform iTRAQ (isobaric tags for relative and absolute quantitation) analysis for normal and autistic children's plasma of the same age group.

RESULTS

The results show that 24 differentially expressed proteins were identified between autistic subjects and controls. For the first time, differential expression of complement C5 (C5) and fermitin family homolog 3 (FERMT3) are related to autism. Five proteins, that is, complement C3 (C3), C5, integrin alpha-IIb (ITGA2B), talin-1 (TLN1), and vitamin D-binding protein (GC) were validated via enzyme-linked immunosorbent assay (ELISA). By ROC (receiver operating characteristic) analysis, combinations of these five proteins C3, C5, GC, ITGA2B, and TLN1 distinguished autistic children from healthy controls with a high AUC (area under the ROC curve) value (0.982, 95% CI, 0.957-1.000, p < 0.000).

CONCLUSION

These above described proteins are found involved in different pathways that have previously been linked to the pathophysiology of autism spectrum disorders (ASDs). The results strongly support that focal adhesions, acting cytoskeleton, cell adhesion, motility and migration, synaptogenesis, and complement system are involved in the pathogenesis of autism, and highlight the important role of platelet function in the pathophysiology of autism.

Serologic Markers of Autism Spectrum Disorder

According to WHO data, about 67 million people worldwide are affected by autism, and this number grows by 14% annually. Among the possible causes of autism are genetic modifications, organic lesions of the central nervous system, metabolic disorders, influence of viral and bacterial infections, chemical influence to the mother's body during pregnancy, etc. The conducted research shows that research papers published until today do not name any potential protein markers that meet the requirements of the basic parameters for evaluating the efficiency of disease diagnostics, in particular high sensitivity, specificity, and accuracy. Conducting proteomic research on a big scale in order to detect serologic markers of protein nature associated with development of autism spectrum disorders seems to be highly relevant.

Serum thyroid-stimulating hormone and interleukin-8 levels in boys with autism spectrum disorder

Background

Autism spectrum disorder (ASD) affects approximately 1 in 68 children in the USA. An ASD blood biomarker may enable early diagnosis and/or identification of new therapeutic targets. Serum samples from ASD and typically developing (TD) boys (n = 30/group) were screened for differences in 110 proteins using a multiplex immunoassay.

Results

Eleven proteins were found that together could confirm ASD with modest accuracy using multiple training and test sets. Two of the 11 proteins identified here were further tested using a different detection platform and with a larger sample of ASD and TD boys. The two proteins, thyroid-stimulating hormone (TSH) and interleukin-8 (IL-8), have been previously identified as putative biomarkers for ASD. TSH levels were significantly lower in ASD boys, whereas IL-8 levels were significantly elevated. The diagnostic accuracy for ASD based upon TSH or IL-8 levels alone varied from 74 to 76%, but using both proteins together, the diagnostic accuracy increased to 82%. In addition, TSH levels were negatively correlated with the Autism Diagnostic Observation Schedule subdomain scores.

Conclusions

These data suggest that a panel of proteins may be useful as a putative blood biomarker for ASD.

Screening of autism based on task-free fMRI using graph theoretical approach

Studies on autism spectrum disorder (ASD) have indicated several dysfunctions in the structure, and functional organization of the brain. However, findings have not been established as a general diagnostic tool yet. In this regard, current study proposed an automatic screening method for recognition of ASDs from healthy controls (HCs) based on their brain functional abnormalities. In this paradigm, brain functional networks of 60 adolescent and young adult males (29 ASDs and 31 HCs) were estimated from subjects' task-free fMRI data. Then, autism screening was developed based on characteristics of the functional networks using the following steps: A) local and global parameters of the brain functional network were calculated using graph theory. B) network parameters of the ASDs were statistically compared to the HCs. C) significantly altered parameters were used as input features of the screening system. D) performance of the system was verified using various classification techniques. The support vector machine showed superiority to others with an accuracy of 92%. Subsequently, reliability of the results was examined using an independent dataset including 20 ASDs and 20 HCs. Our findings suggest that local parameters of the brain functional network, despite the individual variability, can potentially be used for autism screening.

Complement C3 Expression Is Decreased in Autism Spectrum Disorder Subjects and Contributes to Behavioral Deficits in Rodents

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with hallmark symptoms including social deficits, communication deficits and repetitive behaviors. Accumulating evidence suggests a potential role of the immune system in the pathophysiology of ASD. The complement system represents one of the major effector mechanisms of the innate immune system, and regulates inflammation, and orchestrates defense against pathogens. However, the role of CNS complement system in ASD is not well understood. In the present study, we found a significant increase in C2, C5, and MASP1, but a decrease in C1q, C3, and C4 mRNA levels in the middle frontal gyrus of ASD subjects compared to controls. Significant decreases in the mRNA levels of 2 key proinflammatory cytokines, IL-17 and IL-23 were observed in ASD subjects. Our study further demonstrated a strong association of complement genes with IL-17 and IL-23, suggesting a possible role of the complement system in immune dysregulation in ASD. We observed significant associations between complement components and abnormality of development scores in subjects with ASD. In rodents, C3 knockdown in the prefrontal cortex induced social interaction deficits and repetitive behavior in mice. Together, these studies suggest a potential role of C3 in the pathophysiology of ASD.

[The activity of complement system in children with autistic spectrum disorders]

AIM

To evaluate the state of complement system (CS) activity in children with autistic spectrum disorders (ASD) and children with schizophrenia on the basis of development and implementation of a new method of CS determination.

MATERIAL AND METHODS

A study included 249 patients, aged from 3 to 14 years. The control group consisted of 279 age-matched children. The authors developed a method for integral evaluation of CS activity based on the changes in the death of free swimming ciliata Tetrahymena pyriformis measured with the apparatus BioLat (Moscow, Russia). The integral CS activity (T50) was estimated as the time of death of 50% of ciliata in the blood serum (serum concentration was 5%).

RESULTS AND CONCLUSION

A comparative analysis of CS activity showed statistical differences in median T50 values between patients and controls (p<0.005). Based on CS activity levels, three groups of patients were determined: 1) with the levels lower than the lowest value of the control group (n=112 (39%)); 2) higher than the highest level of the control group (n=103 (36%)); intermittent between low and high values of the control group (n=72 (25%)). Significant differences in T50 between the psychotic autism group and children schizophrenia group were identified (p<0.005). The CS activity was lower in patients with ASD compared to children with schizophrenia.

Dietary glycemic index modulates the behavioral and biochemical abnormalities associated with autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder of unknown etiology, but very likely resulting from both genetic and environmental factors. There is good evidence for immune system dysregulation in individuals with ASD. However, the contribution of insults such as dietary factors that can also activate the immune system have not been explored in the context of ASD. In this paper, we show that the dietary glycemic index has a significant impact on the ASD phenotype. By using BTBR mice, an inbred strain that displays behavioral traits that reflect the diagnostic symptoms of human ASD, we found that the diet modulates plasma metabolites, neuroinflammation and brain markers of neurogenesis in a manner that is highly reflective of ASD in humans. Overall, the manuscript supports the idea that ASD results from gene-environment interactions and that in the presence of a genetic predisposition to ASD, diet can make a large difference in the expression of the condition.

Recent developments in proteomic methods and disease biomarkers

Proteomic methodologies for identification and analysis of biomarkers have gained more attention during recent years and have evolved rapidly. Identification and detection of disease biomarkers are important to foresee outbreaks of certain diseases thereby avoiding surgery and other invasive and expensive medical treatments for patients. Thus, more research into discovering new biomarkers and new methods for faster and more accurate detection is needed. It is often difficult to detect and measure biomarkers because of their low concentrations and the complexity of their respective matrices. Therefore it is hard to find and validate methods for accurate screening methods suitable for clinical use. The most recent developments during the last three years and also some historical considerations of proteomic methodologies for identification and validation of disease biomarkers are presented in this review.

Salivary proteomics and biomarkers in neurology and psychiatry

Biomarkers are greatly needed in the fields of neurology and psychiatry, to provide objective and earlier diagnoses of CNS conditions. Proteomics and other omics MS-based technologies are tools currently being utilized in much recent CNS research. Saliva is an interesting alternative biomaterial for the proteomic study of CNS disorders, with several advantages. Collection is noninvasive and saliva has many proteins. It is easier to collect than blood and can be collected by professionals without formal medical training. For psychiatric and neurological patients, supplying a saliva sample is less anxiety-provoking than providing a blood sample, and is less embarrassing than producing a urine specimen. The use of saliva as a biomaterial has been researched for the diagnosis of and greater understanding of several CNS conditions, including neurodegenerative diseases, autism, and depression. Salivary biomarkers could be used to rule out nonpsychiatric conditions that are often mistaken for psychiatric/neurological conditions, such as fibromyalgia, and potentially to assess cognitive ability in individuals with compromised brain function. As MS and omics technology advances, the sensitivity and utility of assessing CNS conditions using distal human biomaterials such as saliva is becoming increasingly possible.

Autism spectrum disorder: an omics perspective

Current directions in autism spectrum disorder (ASD) research may require moving beyond genetic analysis alone, based on the complexity of the disorder, heterogeneity and convergence of genetic alterations at the cellular/functional level. Mass spectrometry (MS) has been increasingly used to study CNS disorders, including ASDs. Proteomic research using MS is directed at understanding endogenous protein changes that occur in ASD. This review focuses on how MS has been used to study ASDs, with particular focus on proteomic analysis. Other neurodevelopmental disorders have been investigated using MS, including fragile X syndrome (FXS) and Smith-Lemli-Opitz Syndrome (SLOS), genetic syndromes highly associated with ASD comorbidity.

Urinary peptidomics identifies potential biomarkers for major depressive disorder

Major depressive disorder (MDD) is a debilitating psychiatric illness with no available objective laboratory-based diagnostic test. In this study, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based peptidomics was applied to identify potential urinary diagnostic biomarkers for MDD. A training set of 42 first-episode drug-naive MDD patients and 28 age- and gender-matched healthy controls (HC) was used to develop a peptide diagnostic pattern. Then, the diagnostic efficacy of this pattern was assessed in an independent blinded test set consisting of 24 MDD patients and 13 age- and gender-matched HC. A combination of five potential biomarkers was identified, yielding a sensitivity of 91.7% and specificity of 84.6% in the test set. Moreover, the protein precursors of four of the five peptides were identified by tandem mass spectrometric analysis: serum albumin, apolipoprotein A-I, protein AMBP, and basement membrane-specific heparan sulfate proteoglycan core protein. Taken together, the peptide pattern may be valuable for establishing an objective laboratory-based diagnostic test for MDD.

Focusing on Cause or Cure?: Priorities and Stakeholder Presence in Childhood Psychiatry Research

BACKGROUND

Biomedical research is influenced by many factors, including the involvement of stakeholder groups invested in research outcomes. Stakeholder involvement in research efforts raise questions of justice as their specific interests and motivations play a role in directing research resources that ultimately produce knowledge shaping how different conditions (and affected individuals) are understood and treated by society. This issue is highly relevant to child psychiatry research where diagnostic criteria and treatment strategies are often controversial. Biological similarities and stakeholder differences between attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) provide an opportunity to explore this issue by comparing research foci and stakeholder involvement in these conditions.

METHODS

A subset of ADHD and ASD research articles published between 1970-2010 were randomly selected from the PubMed database and coded for research focus, funding source(s), and author-reported conflicts of interest (COIs). Chi-square analyses were performed to identify differences between and within ADHD and ASD research across time.

RESULTS

The proportion of ADHD research dedicated to basic, description, and treatment research was roughly similar and remained stable over time, while ASD research showed a significant increase in basic research over the past decade. Government was the primary research funder for both conditions, but for-profit funders were a notable presence in ADHD research, while joint-funding efforts between non-profit and government funders were a notable presence in ASD research. Lastly, COIs were noted more frequently in ADHD than in ASD research.

CONCLUSIONS

Our study shows significant differences in research foci and funding sources between the conditions, and identifies the specific involvement of for-profit and non-profit groups in ADHD and ASD, respectively. Our findings highlight the relationship between stakeholders outside the research community and research trajectories and suggest that examinations of these relationships must be included in broader considerations of biomedical research ethics.

Subclinical inflammatory status in Rett syndrome

Inflammation has been advocated as a possible common central mechanism for developmental cognitive impairment. Rett syndrome (RTT) is a devastating neurodevelopmental disorder, mainly caused by de novo loss-of-function mutations in the gene encoding MeCP2. Here, we investigated plasma acute phase response (APR) in stage II (i.e., “pseudo-autistic”) RTT patients by routine haematology/clinical chemistry and proteomic 2-DE/MALDI-TOF analyses as a function of four major MECP2 gene mutation types (R306C, T158M, R168X, and large deletions). Elevated erythrocyte sedimentation rate values (median 33.0 mm/h versus 8.0 mm/h, P < 0.0001) were detectable in RTT, whereas C-reactive protein levels were unchanged (P = 0.63). The 2-DE analysis identified significant changes for a total of 17 proteins, the majority of which were categorized as APR proteins, either positive (n = 6 spots) or negative (n = 9 spots), and to a lesser extent as proteins involved in the immune system (n = 2 spots), with some proteins having overlapping functions on metabolism (n = 7 spots). The number of protein changes was proportional to the severity of the mutation. Our findings reveal for the first time the presence of a subclinical chronic inflammatory status related to the “pseudo-autistic” phase of RTT, which is related to the severity carried by the MECP2 gene mutation.

Hypermethylation of the enolase gene (ENO2) in autism

It has been hypothesized that dysregulation of brain-expressed genes is the major predisposing underlying mechanism for autism. This dysregulation may be mediated by differential methylation of CpG sites within gene promoters, which could be candidate biomarkers and used for early clinical screening of autism. A total of 131 pairs of age- and sex-matched autistic and control subjects were recruited in this study. Peripheral blood cells were analyzed. The first five pairs were randomly applied to array-based genome-wide methylation studies. A neuron-specific gene, ENO2, was found to be hypermethylated in the autistic samples. This difference was validated by bisulfite sequencing PCR (BSP). The differential expression of ENO2 gene was further analyzed with RT-qPCR and ELISA. The hypermethylation of ENO2 within the promoter region was confirmed by BSP to be present in 14.5 % (19/131) of the total of the autistic samples. The mean ENO2 RNA level in these 19 autistic samples was reduced by about 70 % relative to that in controls. The average level of ENO2 protein expression in the 19 autistic samples (15.18 ± 3.51 μg/l) was about half of that in the controls (33.86 ± 8.16 μg/l).

CONCLUSION

These findings suggest that reduced ENO2 expression may be a biomarker for a subset of autistic children.

Mass spectrometry for the study of autism and neurodevelopmental disorders

Mass spectrometry (MS) has been increasingly used to study central nervous system disorders, including autism spectrum disorders (ASDs). The first studies of ASD using MS focused on the identification of external toxins, but current research is more directed at understanding endogenous protein changes that occur in ASD (ASD proteomics). This chapter focuses on how MS has been used to study ASDs, with particular focus on proteomic analysis. Other neurodevelopmental disorders have been investigated using this technique, including genetic syndromes associated with autism such as fragile X syndrome and Smith-Lemli-Opitz syndrome.

Camel Milk as a Potential Therapy as an Antioxidant in Autism Spectrum Disorder (ASD)

Extensive studies have demonstrated that oxidative stress plays a vital role in the pathology of several neurological diseases, including autism spectrum disorder (ASD); those studies proposed that GSH and antioxidant enzymes have a pathophysiological role in autism. Furthermore, camel milk has emerged to have potential therapeutic effects in autism. The aim of the current study was to evaluate the effect of camel milk consumption on oxidative stress biomarkers in autistic children, by measuring the plasma levels of glutathione, superoxide dismutase, and myeloperoxidase before and 2 weeks after camel milk consumption, using the ELISA technique. All measured parameters exhibited significant increase after camel milk consumption (P < 0.5). These findings suggest that camel milk could play an important role in decreasing oxidative stress by alteration of antioxidant enzymes and nonenzymatic antioxidant molecules levels, as well as the improvement of autistic behaviour as demonstrated by the improved Childhood Autism Rating Scale (CARS).