Altered Urinary Amino Acids in Children With Autism Spectrum Disorders

Human-derived fecal microbiota transplantation alleviates social deficits of the BTBR mouse model of autism through a potential mechanism involving vitamin B6 metabolism

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by social communication deficiencies and stereotypic behaviors influenced by hereditary and/or environmental risk factors. There are currently no approved medications for treating the core symptoms of ASD. Human fecal microbiota transplantation (FMT) has emerged as a potential intervention to improve autistic symptoms, but the underlying mechanisms are not fully understood. In this study, we evaluated the effects of human-derived FMT on behavioral and multi-omics profiles of the BTBR mice, an established model for ASD. FMT effectively alleviated the social deficits in the BTBR mice and normalized their distinct plasma metabolic profile, notably reducing the elevated long-chain acylcarnitines. Integrative analysis linked these phenotypic changes to specific Bacteroides species and vitamin B6 metabolism. Indeed, vitamin B6 supplementation improved the social behaviors in BTBR mice. Collectively, these findings shed new light on the interplay between FMT and vitamin B6 metabolism and revealed a potential mechanism underlying the therapeutic role of FMT in ASD.IMPORTANCEAccumulating evidence supports the beneficial effects of human fecal microbiota transplantation (FMT) on symptoms associated with autism spectrum disorder (ASD). However, the precise mechanism by which FMT induces a shift in the microbiota and leads to symptom improvement remains incompletely understood. This study integrated data from colon-content metagenomics, colon-content metabolomics, and plasma metabolomics to investigate the effects of FMT treatment on the BTBR mouse model for ASD. The analysis linked the amelioration of social deficits following FMT treatment to the restoration of mitochondrial function and the modulation of vitamin B6 metabolism. Bacterial species and compounds with beneficial roles in vitamin B6 metabolism and mitochondrial function may further contribute to improving FMT products and designing novel therapies for ASD treatment.

A Clinical Study of Urine Amino Acids in Children with Autism Spectrum Disorder

Amino acids are organic compounds that enter the protein structure, being involved in the proper functioning of the body. The role of amino acids in the onset of autism spectrum disorder (ASD) is yet to be established. Our aim was to identify correlations between urine amino acids and their derivatives and ASD.

METHODS

We designed a case-control study that consisted of 75 boys and girls, aged between 2 and 12 years. For amino acid profile, we used urine samples that were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS).

RESULTS

Descriptive analysis showed higher values for glutamine, hydroxyproline, tyrosine, aspartic acid, and tryptophan and lower values for serine in the autism group than in the control group. Also, we found that boys with autism had higher values than the boys in the control group for serine, threonine, and aspartic acid. For girls from both groups, we did not find statistically significant values. In terms of age groups, we found significantly higher values for histidine, threonine, valine, methionine, aspartic acid, glutamic acid, alpha amino-adipic acid, sarcosine, alanine, and beta-alanine and significantly lower values for proline for both the autism and control groups under 5 years.

CONCLUSIONS

The findings of this study support the assumption that amino acids may have a role in the expression of ASD.

Infant microbes and metabolites point to childhood neurodevelopmental disorders

This study has followed a birth cohort for over 20 years to find factors associated with neurodevelopmental disorder (ND) diagnosis. Detailed, early-life longitudinal questionnaires captured infection and antibiotic events, stress, prenatal factors, family history, and more. Biomarkers including cord serum metabolome and lipidome, human leukocyte antigen (HLA) genotype, infant microbiota, and stool metabolome were assessed. Among the 16,440 Swedish children followed across time, 1,197 developed an ND. Significant associations emerged for future ND diagnosis in general and for specific ND subtypes, spanning intellectual disability, speech disorder, attention-deficit/hyperactivity disorder, and autism. This investigation revealed microbiome connections to future diagnosis as well as early emerging mood and gastrointestinal problems. The findings suggest links to immunodysregulation and metabolism, compounded by stress, early-life infection, and antibiotics. The convergence of infant biomarkers and risk factors in this prospective, longitudinal study on a large-scale population establishes a foundation for early-life prediction and intervention in neurodevelopment.

Metabolomic analysis of plasma biomarkers in children with autism spectrum disorders

Autism spectrum disorder (ASD) presents a significant risk to human well-being and has emerged as a worldwide public health concern. Twenty-eight children with ASD and 33 healthy children (HC) were selected for the quantitative determination of their plasma metabolites using an ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) platform. A total of 1997 metabolites were detected in the study cohort, from which 116 metabolites were found to be differentially expressed between the ASD and HC groups. Through analytical algorithms such as least absolute shrinkage selection operator (LASSO), support vector machine (SVM), and random forest (RF), three potential metabolic markers were identified as FAHFA (18:1(9Z)/9-O-18:0), DL-2-hydroxystearic acid, and 7(S),17(S)-dihydroxy-8(E),10(Z),13(Z),15(E),19(Z)-docosapentaenoic acid. These metabolites demonstrated superior performance in distinguishing the ASD group from the HC group, as indicated by the area under curves (AUCs) of 0.935, 0.897, and 0.963 for the three candidate biomarkers, respectively. The samples were divided into training and validation sets according to 7:3. Diagnostic models were constructed using logistic regression (LR), SVM, and RF. The constructed three-biomarker diagnostic model also exhibited strong discriminatory efficacy. These findings contribute to advancing our understanding of the underlying mechanisms involved in the occurrence of ASD and provide a valuable reference for clinical diagnosis.

Autism spectrum disorder: pathogenesis, biomarker, and intervention therapy

Autism spectrum disorder (ASD) has become a common neurodevelopmental disorder. The heterogeneity of ASD poses great challenges for its research and clinical translation. On the basis of reviewing the heterogeneity of ASD, this review systematically summarized the current status and progress of pathogenesis, diagnostic markers, and interventions for ASD. We provided an overview of the ASD molecular mechanisms identified by multi-omics studies and convergent mechanism in different genetic backgrounds. The comorbidities, mechanisms associated with important physiological and metabolic abnormalities (i.e., inflammation, immunity, oxidative stress, and mitochondrial dysfunction), and gut microbial disorder in ASD were reviewed. The non-targeted omics and targeting studies of diagnostic markers for ASD were also reviewed. Moreover, we summarized the progress and methods of behavioral and educational interventions, intervention methods related to technological devices, and research on medical interventions and potential drug targets. This review highlighted the application of high-throughput omics methods in ASD research and emphasized the importance of seeking homogeneity from heterogeneity and exploring the convergence of disease mechanisms, biomarkers, and intervention approaches, and proposes that taking into account individuality and commonality may be the key to achieve accurate diagnosis and treatment of ASD.

Untargeted urine metabolomics and machine learning provide potential metabolic signatures in children with autism spectrum disorder

Background

Complementary to traditional biostatistics, the integration of untargeted urine metabolomic profiling with Machine Learning (ML) has the potential to unveil metabolic profiles crucial for understanding diseases. However, the application of this approach in autism remains underexplored. Our objective was to delve into the metabolic profiles of autism utilizing a comprehensive untargeted metabolomics platform coupled with ML.

Methods

Untargeted metabolomics quantification (UHPLC/Q-TOF-MS) was performed for urine analysis. Feature selection was conducted using Lasso regression, and logistic regression, support vector machine, random forest, and extreme gradient boosting were utilized for significance stratification. Pathway enrichment analysis was performed to identify metabolic pathways associated with autism.

Results

A total of 52 autistic children and 40 typically developing children were enrolled. Lasso regression identified ninety-two urinary metabolites that significantly differed between the two groups. Distinct metabolites, such as prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, were revealed to be associated with autism through the application of four different ML methods (p<0.05). The alterations observed in the phosphatidylinositol and inositol phosphate metabolism pathways were linked to the pathophysiology of autism (p<0.05).

Conclusion

Significant urinary metabolites, including prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, exhibit associations with autism. Additionally, the involvement of the phosphatidylinositol and inositol phosphate pathways suggests their potential role in the pathophysiology of autism.

Metabolomics and transcriptomics analysis of prefrontal cortex in the Pax2 neuron-specific deletion mice

Restricted and repetitive behaviors (RRBs) are one of the characteristics of various neuropsychiatric disorders with complex and diverse molecular mechanisms. Repetitive self-grooming behavior is one of the manifestations of RRBs in humans and rodents. Research on the neural mechanism of repetitive self-grooming behavior is expected to reveal the underlying logic of the occurrence of RRBs. Pax2 is an important member of the paired-box transcription factor family. It is expressed in different regions of the developing central nervous system. Our previous study showed that Pax2 heterozygous gene knockout mice (Pax2+/- KO mice) exhibit significantly increased self-grooming, which suggests that the Pax2 gene is involved in the control of self-grooming behavior, but the molecular mechanism is still unclear. In this study, we further constructed the Pax2 neuron-specific deletion mice (Nestin-Pax2 mice). Targeted metabolomics and transcriptomics techniques was used to analyze. The results showed that there is an excitatory/inhibitory imbalance of the neurotransmitter system and the Arc gene was significantly up-regulated in the prefrontal cortex (PFC) of Nestin-Pax2 mice. This study suggests that the potential regulatory mechanism of the increased repetitive self-grooming behavior in Pax2 gene deletion mice is that the deletion of the Pax2 gene affects the expression of Arc in the PFC, leading to impaired synaptic plasticity and excitatory/inhibitory imbalance, and participating in the occurrence of repetitive self-grooming behavior.

Metabolomics: Perspectives on Clinical Employment in Autism Spectrum Disorder

Precision medicine is imminent, and metabolomics is one of the main actors on stage. We summarize and discuss the current literature on the clinical application of metabolomic techniques as a possible tool to improve early diagnosis of autism spectrum disorder (ASD), to define clinical phenotypes and to identify co-occurring medical conditions. A review of the current literature was carried out after PubMed, Medline and Google Scholar were consulted. A total of 37 articles published in the period 2010-2022 was included. Selected studies involve as a whole 2079 individuals diagnosed with ASD (1625 males, 394 females; mean age of 10, 9 years), 51 with other psychiatric comorbidities (developmental delays), 182 at-risk individuals (siblings, those with genetic conditions) and 1530 healthy controls (TD). Metabolomics, reflecting the interplay between genetics and environment, represents an innovative and promising technique to approach ASD. The metabotype may mirror the clinical heterogeneity of an autistic condition; several metabolites can be expressions of dysregulated metabolic pathways thus liable of leading to clinical profiles. However, the employment of metabolomic analyses in clinical practice is far from being introduced, which means there is a need for further studies for the full transition of metabolomics from clinical research to clinical diagnostic routine.

A paper-based chemical tongue based on the charge transfer complex of ninhydrin with an array of metal-doped carbon dots discriminates natural amino acids and several of their enantiomers

Simultaneous detection of multiple amino acids (AAs) instead of individual AAs is inherently worthwhile for improving diagnostic accuracy in clinical applications. Here, a facile and reliable colorimetric microfluidic paper-based analytical device (μPAD) using carbon dots doped with transition metals (Cr3+, Mn2+, Fe3+, Co2+, Ni2+, Cu2+, and Zn2+) has been provided to detect and discriminate 20 natural amino acids. To make the colourless metal-doped carbon dots suitable for colorimetric assays, they were mixed with ninhydrin to form a charge transfer complex. This optical tongue system, which was constructed by dropping mixtures of ninhydrin with a series of metal-doped carbon dots on a paper substrate in an array format, represented obvious but different colorimetric signatures for every examined amino acid. Since bovine serum albumin was used as a chiral selector reagent for synthesizing the CDs, the sensor device represented excellent selectivity to identify enantiomeric species of AAs. This is the first optical array device that can simultaneously discriminate AAs and several of their enantiomers. We employed various statistical and chemometric methods to analyze the digital data library collected by Image J software, including principal component analysis (PCA), linear discriminant analysis (LDA), and hierarchical cluster analysis (HCA). Twenty AAs could be well distinguished at various concentrations (10.00, 5.00, 2.50, and 1.25 mM). The colorimetric patterns were highly repeatable and were characteristic of individual AAs. Besides qualitative analysis, the designed μPAD-based optical tongue represented quantitative analysis ability, e.g., for lysine in the concentration ranges of 0.005-20.0 mM with a detection limit of 1.0 × 10-6 M and for arginine in the concentration range of 0.12-20.00 mM with a detection limit of 80.0 × 10-6 M. In addition, the binary, ternary, and quaternary mixtures of AAs could also be well recognized with this sensor.

Determination of creatinine in urine and blood serum human samples by CZE-UV using on-column internal standard injection

Creatinine is a well-stablished biomarker for kidney malfunctions and for normalization parameter of urinary quantitative information. Recently, metabolic studies have been discovering other functionalities for creatinine tests in human urine and blood serum. In this work we present an enhanced capillary electrophoresis (CE) based protocol for determination of creatinine. CE is a high-throughput separation technique that have been getting attention through the last decades and might be considered to be adopted as an analytical instrumentation for clinical purposes. In the proposed method, we performed a short injection program with on-column addition of internal standard. Additionally, the method allows a simultaneous screening of non-proteinogenic amino acids that could be considered for metabolomics purposes. We design a pilot study that successfully estimated the creatinine value in 100 urine samples with (2.85 ± 1.78) mg dL^-1 LOD; (8.24 ± 5.93) mg dL^-1 LOQ and 82.4% accuracy. Considering that serum creatinine is also included in the clinical laboratory routines for estimated Glomerular Filtration Rate dosage, the method was complementary applied to 10 blood serum samples, which resulted in a model with (0.4 ± 0.2) mg dL^-1 LOD; (2.0 ± 0.6) mg dL^-1 LOQ and 83.8% of accuracy. All results were in agreement with reference values. The proposed method promotes a great analytical frequency and reproducibility with enhanced specificity compared with the ongoing protocol by Jaffe's reaction, thereby proving to be useful as an alternative for creatinine exams that might help complete a diagnosis of a series of health-related issues.

Comprehensive characterization of amino acids and water-soluble vitamins in a pentylenetetrazole-induced seizures rat model

Epilepsy is a complex neurological disease characterized by spontaneous recurrent seizures that affect around 1% of the global population. Despite the significant progress in the mechanisms of epileptogenesis, there is still about 60% of cases in which the cause is unknown. Thus, revealing the molecular mechanisms of epileptogenesis will greatly improve the development of epilepsy treatment. Since the comprehensive characterization of amino acids and water-soluble vitamins is important in understanding the underlying mechanisms of epilepsy or seizures, we developed two liquid chromatography-tandem mass spectrometry methods to quantify 17 water-soluble vitamins and 46 amino acids and applied them to our pentylenetetrazole-induced kindling rat model. All water-soluble vitamins were detected with a linearity of r > 0.992 and limits of quantitation between 0.1 and 5 ng/ml except for nicotinic acid. For amino acids, the linearities obtained were good with correlation coefficients higher than 0.99, and matrix effects were between 85.3% and 110%. To handle the multidimensional data more effectively, multivariate statistical analysis approaches used in non-targeted metabolomics were creatively exploited in the visualization, interpretation, and exploration of the results.

Inductively coupled plasma mass spectrometry based urine metallome to construct clinical decision models for autism spectrum disorder

BACKGROUND

The global prevalence of autism spectrum disorder (ASD) is on the rise, and high levels of exposure to toxic heavy metals may be associated with this increase. Urine analysis is a noninvasive method for investigating the accumulation and excretion of heavy metals. The aim of this study was to identify ASD-associated urinary metal markers.

METHODS

Overall, 70 children with ASD and 71 children with typical development (TD) were enrolled in this retrospective case-control study. In this metallomics investigation, inductively coupled plasma mass spectrometry was performed to obtain the urine profile of 27 metals.

RESULTS

Children with ASD could be distinguished from children with TD based on the urine metal profile, with ASD children showing an increased urine metal Shannon diversity. A metallome-wide association analysis was used to identify seven ASD-related metals in urine, with cobalt, aluminum, selenium, and lithium significantly higher, and manganese, mercury, and titanium significantly lower in the urine of children with ASD than in children with TD. The least absolute shrinkage and selection operator (LASSO) machine learning method was used to rank the seven urine metals in terms of their effect on ASD. On the basis of these seven urine metals, we constructed a LASSO regression model for ASD classification and found an area under the receiver operating characteristic curve of 0.913. We also constructed a clinical prediction model for ASD based on the seven metals that were different in the urine of children with ASD and found that the model would be useful for the clinical prediction of ASD risk.

CONCLUSIONS

The study findings suggest that altered urine metal concentrations may be an important risk factor for ASD, and we recommend further exploration of the mechanisms and clinical treatment measures for such alterations.

Dysregulation of glutamine/glutamate metabolism in COVID-19 patients: A metabolism study in African population and mini meta-analysis

Coronavirus disease 2019 (COVID-19) remains a serious global threat. The metabolic analysis had been successfully applied in the efforts to uncover the pathological mechanisms and biomarkers of disease severity. Here we performed a quasi-targeted metabolomic analysis on 56 COVID-19 patients from Sierra Leone in western Africa, revealing the metabolomic profiles and the association with disease severity, which was confirmed by the targeted metabolomic analysis of 19 pairs of COVID-19 patients. A meta-analysis was performed on published metabolic data of COVID-19 to verify our findings. Of the 596 identified metabolites, 58 showed significant differences between severe and nonsevere groups. The pathway enrichment of these differential metabolites revealed glutamine and glutamate metabolism as the most significant metabolic pathway (Impact = 0.5; -log10P = 1.959). Further targeted metabolic analysis revealed six metabolites with significant intergroup differences, with glutamine/glutamate ratio significantly associated with severe disease, negatively correlated with 10 clinical parameters and positively correlated with SPO₂ (r_s = 0.442, p = 0.005). Mini meta-analysis indicated elevated glutamate was related to increased risk of COVID-19 infection (pooled odd ratio [OR] = 2.02; 95% confidence interval [CI]: 1.17-3.50) and severe COVID-19 (pooled OR = 2.28; 95% CI: 1.14-4.56). In contrast, elevated glutamine related to decreased risk of infection and severe COVID-19, the pooled OR were 0.30 (95% CI: 0.20-0.44), and 0.44 (95% CI: 0.19-0.98), respectively. Glutamine and glutamate metabolism are associated with COVID-19 severity in multiple populations, which might confer potential therapeutic target of COVID-19, especially for severe patients.

Amino Acids, B Vitamins, and Choline May Independently and Collaboratively Influence the Incidence and Core Symptoms of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a developmental disorder of variable severity, characterized by difficulties in social interaction, communication, and restricted or repetitive patterns of thought and behavior. In 2018, the incidence of ASD was 2.4 times higher than estimated in 2000. Behavior and brain development abnormalities are present in the complex disorder of ASD. Nutritional status plays a key role in the incidence and severity of the core symptoms of ASD. The aim of this study was to review the available peer-reviewed studies that evaluated the relationship between amino acids, choline, B vitamins, and ASD incidence and/or severity of symptoms. Through examining plasma profiles, urine samples, and dietary intake, researchers found that low choline, abnormal amino acid, and low B vitamin levels were present in children with ASD compared to those without ASD. The evidence supports the need for future research that implements simultaneous supplementation of all essential nutrients in individuals with ASD and among prenatal mothers. Future evidence could lead to scientific breakthroughs, ultimately reducing the rates of ASD incidence and severity of symptoms by applying nutritional interventions in at-risk populations.

Effect of Supplementation on Levels of Homovanilic and Vanillylmandelic Acids in Children with Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are characterized by numerous comorbidities, including various metabolic and nutritional abnormalities. In many children with ASD, problems with proper nutrition can often lead to inadequate nutrient intake and some disturbances in metabolic profiles, which subsequently correlate with impaired neurobehavioural function. The purpose of this study was to investigate and compare the relationship between supplementation, levels of homovanillic acid (HVA) and vanillylmandelic acid (VMA) and the behaviour of children with ASD using quantitative urinary acid determination and questionnaires provided by parents/caregivers. The study was carried out on 129 children between 3 and 18 years of age. HVA and VMA were extracted and derivatized from urinary samples and simultaneously analyzed by gas chromatography-mass spectrometry (GC-MS). In addition, parents/caregivers of children with ASD were asked to complete questionnaires containing information about their diet and intake/non-intake of supplements. The application of the Mann–Whitney U test showed a statistically significant difference between the level of HVA and vitamin B supplementation (p = 1.64 × 10⁻²) and also omega-6 fatty acids supplementation and the levels of HVA (p = 1.50 × 10⁻³) and VMA (p = 2.50 × 10⁻³). In some children, a reduction in the severity of autistic symptoms (better response to own name or better reaction to change) was also observed. These results suggest that supplementation affects the levels of HVA and VMA and might also affect the children’s behaviour. Further research on these metabolites and the effects of supplementation on their levels, as well as the effects on the behaviour and physical symptoms among children with ASD is needed.

Machine learning approach identifies meconium metabolites as potential biomarkers of neonatal hyperbilirubinemia

Background

The gut microbiota plays an important role in the early stages of human life. Our previous study showed that the abundance of intestinal flora involved in galactose metabolism was altered and correlated with increased serum bilirubin levels in children with jaundice. We conducted the present study to systematically evaluate alterations in the meconium metabolome of neonates with jaundice and search for metabolic markers associated with neonatal jaundice.

Methods

We included 68 neonates with neonatal hyperbilirubinemia, also known as neonatal jaundice (NJ) and 68 matched healthy controls (HC), collected meconium samples from them at birth, and performed metabolomic analysis via liquid chromatography-mass spectrometry.

Results

Gut metabolites enabled clearly distinguishing the neonatal jaundice (NJ) and healthy control (HC) groups. We also identified the compositions of the gut metabolites that differed significantly between the NJ and HC groups; these differentially significant metabolites were enriched in aminyl tRNA biosynthesis; pantothenic acid and coenzyme biosynthesis; and the valine, leucine and isoleucine biosynthesis pathways. Gut branched-chain amino acid (BCAA) levels were positively correlated with serum bilirubin levels, and the area under the receiver operating characteristic curve of the random forest classifier model based on BCAAs, proline, methionine, phenylalanine and total bilirubin reached 96.9%, showing good potential for diagnostic applications. Machine learning-based causal inference analysis revealed the causal effect of BCAAs on serum total bilirubin and NJ.

Conclusions

Altered gut metabolites in neonates with jaundice showed that increased BCAAs and total serum bilirubin were positively correlated. BCAAs proline, methionine, phenylalanine are potential biomarkers of NJ.

Environmental exposures to pesticides, phthalates, phenols and trace elements are associated with neurodevelopment in the CHARGE study

OBJECTIVE

To determine if higher exposures measured in early childhood to environmental phenols, phthalates, pesticides, and/or trace elements, are associated with increased odds of having a diagnosis of Autism Spectrum Disorder (ASD), Developmental Delay (DD), or Other Early Concerns (OEC) compared to typically developing children (TD).

METHODS

This study included 627 children between the ages of 2-5 who participated in the Childhood Autism Risks from Genetics and Environment (CHARGE) study. Urine samples were collected at the same study visit where diagnostic assessments to confirm diagnosis indicated during the recruitment process were performed. Adjusted multinomial regression models of each chemical with diagnosis as the outcome were conducted. Additionally, two methods were used to analyze mixtures: repeated holdout multinomial weighted quantile sum (WQS) regression for each chemical class; and a total urinary mixture effect was assessed with repeated holdout random subset WQS.

RESULTS

Many urinary chemicals were associated with increased odds of ASD, DD or OEC compared to TD; however, most did not remain significant after false discovery rate adjustment. Repeated holdout WQS indices provided evidence for associations of both a phenol/paraben mixture effect and a trace element mixture effect on DD independently. In analyses adjusted for confounders and other exposures, results suggested an association of a pesticide mixture effect with increased risk for ASD. Results also suggested associations of a total urinary mixture with greater odds of both ASD and DD separately.

CONCLUSION

Higher concentrations of urinary biomarkers were associated with ASD, DD, and OEC compared to TD, with consistency of the results comparing single chemical analyses and mixture analyses. Given that the biospecimens used for chemical analysis were generally collected many months after diagnoses were made, the direction of any causal association is unknown. Hence findings may reflect higher exposures among children with non-typical development than TD children due to differences in behaviors, metabolism, or toxicokinetics.

Integration of Urine Proteomic and Metabolomic Profiling Reveals Novel Insights Into Neuroinflammation in Autism Spectrum Disorder

Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders whose etiology and pathogenesis are not fully understood. To gain insight into the molecular basis of ASD, we performed comparative integrated proteomic and metabolomic analyses of urine samples from children diagnosed with ASD and healthy children. All 160 samples underwent proteomics analysis and 60 were analyzed by liquid chromatography-mass spectrometry to obtain metabolite profiles. We identified 77 differentially expressed proteins (DEPs; 21 downregulated and 56 upregulated) and 277 differentially expressed metabolites; 31 of the DEPs including glutathione, leukocyte antigens, glycoproteins, neural adhesion factors, and immunoglobulins, have been implicated in neuroinflammation. The proteomic analysis also revealed 8 signaling pathways that were significantly dysregulated in ASD patients; 3 of these (transendothelial leukocyte migration, antigen processing and presentation, and graft vs. host disease) were associated with the neuroimmune response. The metabolism of tryptophan, which is also related to the neuroimmune response, has been found to play a potential role in ASD. Integrated proteome and metabolome analysis showed that 6 signaling pathways were significantly enriched in ASD patients, 3 of which were correlated with impaired neuroinflammation (glutathione metabolism, metabolism of xenobiotics by cytochrome P450 and transendothelial migration of leukocyte). We also found a correlation between prostaglandin (PG) E2 levels and the inflammatory response in ASD. These results underscore the prominent role of the neuroimmune response in ASD and provide potential biomarkers that can be used for diagnosis or as targets for early intervention.

Profiles of urine and blood metabolomics in autism spectrum disorders

Early diagnosis and treatment for autism spectrum disorder (ASD) pose challenges. The current diagnostic approach for ASD is mainly clinical assessment of patient behaviors. Biomarkers-based identification of ASD would be useful for pediatricians. Currently, there is no specific treatment for ASD, and evidence for the efficacy of alternative treatments remains inconclusive. The prevalence of ASD is increasing, and it is becoming more urgent to find the pathogenesis of such disorder. Metabolomic studies have been used to deeply investigate the alteration of metabolic pathways, including those associated with ASD. Metabolomics is a promising tool for identifying potential biomarkers and possible pathogenesis of ASD. This review comprehensively summarizes and discusses the abnormal metabolic pathways in ASD children, as indicated by evidence from metabolomic studies in urine and blood. In addition, the targeted interventions that could correct the metabolomic profiles relating to the improvement of autistic behaviors in affected animals and humans have been included. The results revealed that the possible underlying pathophysiology of ASD were alterations of amino acids, reactive oxidative stress, neurotransmitters, and microbiota-gut-brain axis. The potential common pathways shared by animal and human studies related to the improvement of ASD symptoms after pharmacological interventions were mammalian-microbial co-metabolite, purine metabolism, and fatty acid oxidation. The content of this review may contribute to novel biomarkers for the early diagnosis of ASD and possible therapeutic paradigms.

Deletion of Mocos Induces Xanthinuria with Obstructive Nephropathy and Major Metabolic Disorders in Mice

Background

Xanthinuria type II is a rare autosomal purine disorder. This recessive defect of purine metabolism remains an under-recognized disorder.

Methods

Mice with targeted disruption of the molybdenum cofactor sulfurase (Mocos) gene were generated to enable an integrated understanding of purine disorders and evaluate pathophysiologic functions of this gene which is found in a large number of pathways and is known to be associated with autism.

Results

Mocos-deficient mice die with 4 weeks of age due to renal failure of distinct obstructive nephropathy with xanthinuria, xanthine deposits, cystic tubular dilation, Tamm-Horsfall (uromodulin) protein (THP) deposits, tubular cell necrosis with neutrophils, and occasionally hydronephrosis with urolithiasis. Obstructive nephropathy is associated with moderate interstitial inflammatory and fibrotic responses, anemia, reduced detoxification systems, and important alterations of the metabolism of purines, amino acids, and phospholipids. Conversely, heterozygous mice expressing reduced MOCOS protein are healthy with no apparent pathology.

Conclusions

Mocos-deficient mice develop a lethal obstructive nephropathy associated with profound metabolic changes. Studying MOCOS functions may provide important clues about the underlying pathogenesis of xanthinuria and other diseases requiring early diagnosis.

Studying Autism Using Untargeted Metabolomics in Newborn Screening Samples

Main risk factors of autism spectrum disorder (ASD) include both genetic and non-genetic factors, especially prenatal and perinatal events. Newborn screening dried blood spot (DBS) samples have great potential for the study of early biochemical markers of disease. To study DBS strengths and limitations in the context of ASD research, we analyzed the metabolomic profiles of newborns later diagnosed with ASD. We performed LC-MS/MS-based untargeted metabolomics on DBS from 37 case-control pairs randomly selected from the iPSYCH sample. After preprocessing using MZmine 2.41, metabolites were putatively annotated using mzCloud, GNPS feature-based molecular networking, and MolNetEnhancer. A total of 4360 mass spectral features were detected, of which 150 (113 unique) could be putatively annotated at a high confidence level. Chemical structure information at a broad level could be retrieved for 1009 metabolites, covering 31 chemical classes. Although no clear distinction between cases and controls was revealed, our method covered many metabolites previously associated with ASD, suggesting that biochemical markers of ASD are present at birth and may be monitored during newborn screening. Additionally, we observed that gestational age, age at sampling, and month of birth influence the metabolomic profiles of newborn DBS, which informs us on the important confounders to address in future studies.

Gene-Environment Interactions in Developmental Neurotoxicity: a Case Study of Synergy between Chlorpyrifos and CHD8 Knockout in Human BrainSpheres

BACKGROUND

Autism spectrum disorder (ASD) is a major public health concern caused by complex genetic and environmental components. Mechanisms of gene-environment (G × E ) interactions and reliable biomarkers associated with ASD are mostly unknown or controversial. Induced pluripotent stem cells (iPSCs) from patients or with clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9)-introduced mutations in candidate ASD genes provide an opportunity to study (G × E ) interactions.

OBJECTIVES

In this study, we aimed to identify a potential synergy between mutation in the high-risk autism gene encoding chromodomain helicase DNA binding protein 8 (CHD8) and environmental exposure to an organophosphate pesticide (chlorpyrifos; CPF) in an iPSC-derived human three-dimensional (3D) brain model.

METHODS

This study employed human iPSC-derived 3D brain organoids (BrainSpheres) carrying a heterozygote CRISPR/Cas9-introduced inactivating mutation in CHD8 and exposed to CPF or its oxon-metabolite (CPO). Neural differentiation, viability, oxidative stress, and neurite outgrowth were assessed, and levels of main neurotransmitters and selected metabolites were validated against human data on ASD metabolic derangements.

RESULTS

Expression of CHD8 protein was significantly lower in CHD8 heterozygous knockout (C H D 8 + / - ) BrainSpheres compared with C H D 8 + / + ones. Exposure to CPF/CPO treatment further reduced CHD8 protein levels, showing the potential (G × E ) interaction synergy. A novel approach for validation of the model was chosen: from the literature, we identified a panel of metabolic biomarkers in patients and assessed them by targeted metabolomics in vitro. A synergistic effect was observed on the cholinergic system, S-adenosylmethionine, S-adenosylhomocysteine, lactic acid, tryptophan, kynurenic acid, and α -hydroxyglutaric acid levels. Neurite outgrowth was perturbed by CPF/CPO exposure. Heterozygous knockout of CHD8 in BrainSpheres led to an imbalance of excitatory/inhibitory neurotransmitters and lower levels of dopamine.

DISCUSSION

This study pioneered (G × E ) interaction in iPSC-derived organoids. The experimental strategy enables biomonitoring and environmental risk assessment for ASD. Our findings reflected some metabolic perturbations and disruption of neurotransmitter systems involved in ASD. The increased susceptibility of CHD 8 + / - BrainSpheres to chemical insult establishes a possibly broader role of (G × E ) interaction in ASD. https://doi.org/10.1289/EHP8580.

Machine learning applied to serum and cerebrospinal fluid metabolomes revealed altered arginine metabolism in neonatal sepsis with meningoencephalitis

Background

Neonatal sepsis with meningoencephalitis is a common complication of sepsis, which is a leading cause of neonatal death and neurological dysfunction. Early identification of neonatal sepsis with meningoencephalitis is particularly important for reducing brain damage. We recruited 70 patients with neonatal sepsis, 42 of which were diagnosed as meningoencephalitis, and collected cerebrospinal fluid (CSF) and serum samples. The purpose of this study was to find neonatal sepsis with meningoencephalitis-related markers using unbiased metabolomics technology and artificial intelligence analysis based on machine learning methods.

Results

We found that the characteristics of neonatal sepsis with meningoencephalitis were manifested mainly as significant decreases in the concentrations of homo-l-arginine, creatinine, and other arginine metabolites in serum and CSF, suggesting possible changes in nitric oxide synthesis. The antioxidants taurine and proline in the serum of the neonatal sepsis with meningoencephalitis increased significantly, suggesting abnormal oxidative stress. Potentially harmful bile salts and aromatic compounds were significantly increased in the serum of the group with meningoencephalitis. We compared different machine learning methods and found that the lasso algorithm performed best. Combining the lasso and XGBoost algorithms was successful in predicting the concentration of homo-l-arginine in CSF per the concentrations of metabolite markers in the serum.

Conclusions

On the basis of machine learning combined with analysis of the serum and CSF metabolomes, we found metabolite markers related to neonatal sepsis with meningoencephalitis. The characteristics of neonatal sepsis with meningoencephalitis were manifested mainly by changes in arginine metabolism and related changes in creatinine metabolism.

Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental condition with hallmark behavioral manifestations including impaired social communication and restricted repetitive behavior. In addition, many affected individuals display metabolic imbalances, immune dysregulation, gastrointestinal dysfunction, and altered gut microbiome compositions.

METHODS

We sought to better understand nonbehavioral features of ASD by determining molecular signatures in peripheral tissues through mass spectrometry methods (ultrahigh performance liquid chromatography-tandem mass spectrometry) with broad panels of identified metabolites. Herein, we compared the global metabolome of 231 plasma and 97 fecal samples from a large cohort of children with ASD and typically developing control children.

RESULTS

Differences in amino acid, lipid, and xenobiotic metabolism distinguished ASD and typically developing samples. Our results implicated oxidative stress and mitochondrial dysfunction, hormone level elevations, lipid profile changes, and altered levels of phenolic microbial metabolites. We also revealed correlations between specific metabolite profiles and clinical behavior scores. Furthermore, a summary of metabolites modestly associated with gastrointestinal dysfunction in ASD is provided, and a pilot study of metabolites that can be transferred via fecal microbial transplant into mice is identified.

CONCLUSIONS

These findings support a connection between metabolism, gastrointestinal physiology, and complex behavioral traits and may advance discovery and development of molecular biomarkers for ASD.

Population epidemiology and concordance for plasma amino acids and precursors in 11-12-year-old children and their parents

Amino acid (AA) concentrations are influenced by both exogenous (e.g. diet, lifestyle) and endogenous factors (e.g. genetic, transcriptomic, epigenetic, and metabolomic). Fasting plasma AA profiles in adulthood are predictive of diabetes risk over periods of up to 12 years. Data on AA profiles in cross-generational cohorts, including individuals from shared gene-environment settings are scarce, but would allow the identification of the contribution of heritable and environmental factors characterising the levels of circulating AAs. This study aimed to investigate parent–child (familial dyad) concordance, absolute differences between generations- (children versus adults), age- (in adults: 28–71 years), and sex-dependent differences in plasma AA concentrations. Plasma AA concentrations were measured by UHPLC/MS–MS in 1166 children [mean (SD) age 11 (0.5) years, 51% female] and 1324 of their parents [44 (5.1) years, 87% female]. AA concentrations were variably concordant between parents and their children (5–41% of variability explained). Most AA concentrations were higher in adults than children, except for the non-essential AAs arginine, aspartic acid, glutamine, hydroxy-proline, proline, and serine. Male adults and children typically had higher AA concentrations than females. The exceptions were alanine, glutamine, glycine, hydroxy-proline, serine, and threonine in girls; and glycine and serine in women. Age, sex, and shared familial factors are important determinants of plasma AA concentrations.

Systematic Review and Bioinformatic Analysis of microRNA Expression in Autism Spectrum Disorder Identifies Pathways Associated With Cancer, Metabolism, Cell Signaling, and Cell Adhesion

Background: Previous studies have identified differentially expressed microRNAs in autism spectrum disorder (ASD), however, results are discrepant. We aimed to systematically review this topic and perform bioinformatic analysis to identify genes and pathways associated with ASD miRNAs. Methods: Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses, we searched the Web of Science, PubMed, Embase, Scopus, and OVID databases to identify all studies comparing microRNA expressions between ASD persons and non-ASD controls on May 11, 2020. We obtained ASD miRNA targets validated by experimental assays from miRTarBase and performed pathway enrichment analysis using Metascape and DIANA-miRPath v3. 0. Results: Thirty-four studies were included in the systematic review. Among 285 altered miRNAs reported in these studies, 15 were consistently upregulated, 14 were consistently downregulated, and 39 were inconsistently dysregulated. The most frequently altered miRNAs including miR-23a-3p, miR-106b-5p, miR-146a-5p, miR-7-5p, miR-27a-3p, miR-181b-5p, miR-486-3p, and miR-451a. Subgroup analysis of tissues showed that miR-146a-5p, miR-155-5p, miR-1277-3p, miR-21-3p, miR-106b-5p, and miR-451a were consistently upregulated in brain tissues, while miR-4742-3p was consistently downregulated; miR-23b-3p, miR-483-5p, and miR-23a-3p were consistently upregulated in blood samples, while miR-15a-5p, miR-193a-5p, miR-20a-5p, miR-574-3p, miR-92a-3p, miR-3135a, and miR-103a-3p were consistently downregulated; miR-7-5p was consistently upregulated in saliva, miR-23a-3p and miR-32-5p were consistently downregulated. The altered ASD miRNAs identified in at least two independent studies were validated to target many autism risk genes. TNRC6B, PTEN, AGO1, SKI, and SMAD4 were the most frequent targets, and miR-92a-3p had the most target autism risk genes. Pathway enrichment analysis showed that ASD miRNAs are significantly involved in pathways associated with cancer, metabolism (notably Steroid biosynthesis, Fatty acid metabolism, Fatty acid biosynthesis, Lysine degradation, Biotin metabolism), cell cycle, cell signaling (especially Hippo, FoxO, TGF-beta, p53, Thyroid hormone, and Estrogen signaling pathway), adherens junction, extracellular matrix-receptor interaction, and Prion diseases. Conclusions: Altered miRNAs in ASD target autism risk genes and are involved in various ASD-related pathways, some of which are understudied and require further investigation.

Differential Metabolites in Chinese Autistic Children: A Multi-Center Study Based on Urinary 1H-NMR Metabolomics Analysis

Background: Autism spectrum disorder (ASD) is a group of early-onset neurodevelopmental disorders. However, there is no valuable biomarker for the early diagnosis of ASD. Our large-scale and multi-center study aims to identify metabolic variations between ASD and healthy children and to investigate differential metabolites and associated pathogenic mechanisms. Methods: One hundred and seventeen autistic children and 119 healthy children were recruited from research centers of 7 cities. Urine samples were assayed by ¹H-NMR metabolomics analysis to detect metabolic variations. Multivariate statistical analysis, including principal component analysis (PCA), and orthogonal projection to latent structure discriminant analysis (OPLS-DA), as well as univariate analysis were used to assess differential metabolites between the ASD and control groups. The differential metabolites were further analyzed by receiver operating characteristics (ROC) curve analysis and metabolic pathways analysis. Results: Compared with the control group, the ASD group showed higher levels of glycine, guanidinoacetic acid, creatine, hydroxyphenylacetylglycine, phenylacetylglycine, and formate and lower levels of 3-aminoisobutanoic acid, alanine, taurine, creatinine, hypoxanthine, and N-methylnicotinamide. ROC curve showed relatively significant diagnostic values for hypoxanthine [area under the curve (AUC) = 0.657, 95% CI 0.588 to 0.726], creatinine (AUC = 0.639, 95% CI 0.569 to 0.709), creatine (AUC = 0.623, 95% CI 0.552 to 0.694), N-methylnicotinamide (AUC = 0.595, 95% CI 0.523 to 0.668), and guanidinoacetic acid (AUC = 0.574, 95% CI 0.501 to 0.647) in the ASD group. Combining the metabolites creatine, creatinine and hypoxanthine, the AUC of the ROC curve reached 0.720 (95% CI 0.659 to 0.777). Significantly altered metabolite pathways associated with differential metabolites were glycine, serine and threonine metabolism, arginine and proline metabolism, and taurine and hypotaurine metabolism. Conclusions: Urinary amino acid metabolites were significantly altered in children with ASD. Amino acid metabolic pathways might play important roles in the pathogenic mechanisms of ASD.

Prospective associations of the infant gut microbiome and microbial function with social behaviors related to autism at age 3 years

The hypothesized link between gut bacteria and autism spectrum disorder (ASD) has been explored through animal models and human studies with microbiome assessment after ASD presentation. We aimed to prospectively characterize the association between the infant/toddler gut microbiome and ASD-related social behaviors at age 3 years. As part of an ongoing birth cohort gut bacterial diversity, structure, taxa, and function at 6 weeks (n = 166), 1 year (n = 158), 2 years (n = 129), and 3 years (n = 140) were quantified with 16S rRNA gene and shotgun metagenomic sequencing (n = 101 six weeks, n = 103 one year). ASD-related social behavior was assessed at age 3 years using Social Responsiveness Scale (SRS-2) T-scores. Covariate-adjusted linear and permutation-based models were implemented. Microbiome structure at 1 year was associated with SRS-2 total T-scores (p = 0.01). Several taxa at 1, 2, and 3 years were associated with SRS-2 performance, including many in the Lachnospiraceae family. Higher relative abundance of Adlercreutzia equolifaciens and Ruminococcus torques at 1 year related to poorer SRS-2 performance. Two functional pathways, L-ornithine and vitamin B6 biosynthesis, were associated with better social skills at 3 years. Our results support potential associations between early-childhood gut microbiome and social behaviors. Future mechanistic studies are warranted to pinpoint sensitive targets for intervention.

Changes in the Gut Microbiota of Children with Autism Spectrum Disorder

Alterations in the gut microbiota may influence gastrointestinal (GI) dysbiosis frequently reported in individuals with autism spectrum disorder (ASD). In this study, we sequenced the bacterial 16S rRNA gene to evaluate changes in fecal microbiota between 48 children with ASD and 48 healthy children in China. At the phylum level, the number of Firmicutes, Proteobacteria, and Verrucomicrobia decreased in children with ASD, while the Bacteroidetes/Firmicutes was significantly higher in autistic children due to enrichment of Bacteroidetes. At the genus level, the amount of Bacteroides, Prevotella, Lachnospiracea_incertae_sedis, and Megamonas increased, while Clostridium XlVa, Eisenbergiella, Clostridium IV, Flavonifractor, Escherichia/Shigella, Haemophilus, Akkermansia, and Dialister decreased in children with ASD relative to the controls. Significant increase was observed in the number of species synthesizing branched-chain amino acids (BCAAs), like Bacteroides vulgatus and Prevotella copri, while the numbers of Bacteroides fragilis and Akkermansia muciniphila decreased in children with ASD compared to the controls. Most importantly, the highest levels of pathogenic bacteria were different for each child with ASD in this cohort. We found that only one functional module, cellular antigens, was enriched in children with ASD, and other pathways like lysine degradation and tryptophan metabolism were significantly decreased in children with ASD. These findings provide further evidence of altered gut microbiota in Chinese ASD children and may contribute to the treatment of patients with ASD. LAY SUMMARY: This study characterized the gut bacteria composition of 48 children with ASD and 48 neurotypical children in China. The metabolic disruptions caused by altered gut microbiota may contribute significantly to the neurological pathophysiology of ASD, including significant increases in the number of species synthesizing BCAAs, and decreases in the number of probiotic species. These findings suggest that a gut microbiome-associated therapeutic intervention may provide a novel strategy for treating GI symptoms frequently seen in individuals with ASD. Autism Res 2020, 13: 1614-1625. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.

A new opening for the tricky untargeted investigation of natural and modified short peptides

Short peptides are of extreme interest in clinical and food research fields, nevertheless they still represent a crucial analytical issue. The main aim of this paper was the development of an analytical platform for a considerable advancement in short peptides identification. For the first time, short sequences presenting both natural and post-translationally modified amino acids were comprehensively studied thanks to the generation of specific databases. Short peptide databases had a dual purpose. First, they were employed as inclusion lists for a suspect screening mass-spectrometric analysis, overcoming the limits of data dependent acquisition mode and allowing the fragmentation of such low-abundance substances. Moreover, the databases were implemented in Compound Discoverer 3.0, a software dedicated to the analysis of short molecules, for the creation of a data processing workflow specifically dedicated to short peptide tentative identification. For this purpose, a detailed study of short peptide fragmentation pathways was carried out for the first time. The proposed method was applied to the study of short peptide sequences in enriched urine samples and led to the tentative identification more than 200 short natural and modified short peptides, the highest number ever reported.

Targeted metabolomics highlights perturbed metabolism in the brain of autism spectrum disorder sufferers

INTRODUCTION

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by deficiencies in social interactions and communication, combined with restricted and repetitive behavioral issues.

OBJECTIVES

As little is known about the etiopathophysiology of ASD and early diagnosis is relatively subjective, we aim to employ a targeted, fully quantitative metabolomics approach to biochemically profile post-mortem human brain with the overall goal of identifying metabolic pathways that may have been perturbed as a result of the disease while uncovering potential central diagnostic biomarkers.

METHODS

Using a combination of ¹H NMR and DI/LC-MS/MS we quantitatively profiled the metabolome of the posterolateral cerebellum from post-mortem human brain harvested from people who suffered with ASD (n = 11) and compared them with age-matched controls (n = 10).

RESULTS

We accurately identified and quantified 203 metabolites in post-mortem brain extracts and performed a metabolite set enrichment analyses identifying 3 metabolic pathways as significantly perturbed (p < 0.05). These include Pyrimidine, Ubiquinone and Vitamin K metabolism. Further, using a variety of machine-based learning algorithms, we identified a panel of central biomarkers (9-hexadecenoylcarnitine (C16:1) and the phosphatidylcholine PC ae C36:1) capable of discriminating between ASD and controls with an AUC = 0.855 with a sensitivity and specificity equal to 0.80 and 0.818, respectively.

CONCLUSION

For the first time, we report the use of a multi-platform metabolomics approach to biochemically profile brain from people with ASD and report several metabolic pathways which are perturbed in the diseased brain of ASD sufferers. Further, we identified a panel of biomarkers capable of distinguishing ASD from control brains. We believe that these central biomarkers may be useful for diagnosing ASD in more accessible biomatrices.

Composition of Gut Microbiota in Children with Autism Spectrum Disorder: A Systematic Review and Meta-Analysis

BACKGROUND

Autism spectrum disorder (ASD) is a public health problem and has a prevalence of 0.6%-1.7% in children. As well as psychiatric symptoms, dysbiosis and gastrointestinal comorbidities are also frequently reported. The gut-brain microbiota axis suggests that there is a form of communication between microbiota and the brain underlying some neurological disabilities. The aim of this study is to describe and compare the composition of gut microbiota in children with and without ASD.

METHODS

Electronic databases were searched as far as February 2020. Meta-analyses were performed using RevMan5.3 to estimate the overall relative abundance of gut bacteria belonging to 8 phyla and 17 genera in children with ASD and controls.

RESULTS

We included 18 studies assessing a total of 493 ASD children and 404 controls. The microbiota was mainly composed of the phyla Bacteroidetes, Firmicutes, and Actinobacteria, all of which were more abundant in the ASD children than in the controls. Children with ASD showed a significantly higher abundance of the genera Bacteroides, Parabacteroides, Clostridium, Faecalibacterium, and Phascolarctobacterium and a lower percentage of Coprococcus and Bifidobacterium.

DISCUSSION

This meta-analysis suggests that there is a dysbiosis in ASD children which may influence the development and severity of ASD symptomatology. Further studies are required in order to obtain stronger evidence of the effectiveness of pre- or probiotics in reducing autistic behaviors.

Assessments of Amino Acids, Ammonia and Oxidative Stress Among Cohort of Egyptian Autistic Children: Correlations with Electroencephalogram and Disease Severity

OBJECTIVE

The current study aimed to assess the profiles of plasma amino acids, serum ammonia and oxidative stress status among autistic children in terms of electroencephalogram findings and clinical severity among the cohort of autistic Egyptian children.

PATIENTS AND METHODS

The present study included 118 Egyptian children categorized into 54 children with autism who were comparable with 64 healthy controls. Clinical assessments of cases were performed using CARS in addition to EEG records. Plasma amino acids were measured using high-performance liquid chromatography (HPLC), while, serum ammonia and oxidative stress markers were measured using colorimetric methods for all included children.

RESULTS

The overall results revealed that 37.04% of cases had abnormal EEG findings. Amino acid profile in autistic children showed statistically significant lower levels of aspartic acid, glycine, β-alanine, tryptophan, lysine and proline amino acids with significantly higher asparagine amino acid derivative levels among autistic patients versus the control group (p˂0.05). There were significantly higher serum ammonia levels with significantly higher total oxidant status (TOS) and oxidative stress index (OSI) values among the included autistic children vs controls (p˂0.05). There were significantly negative correlations between CARS with aspartic acid (r=-0.269, P=0.049), arginine (r= - 0.286, p= 0.036), and TAS (r= -0.341, p= 0.012), and significantly positive correlations between CARS with TOS (r=0.360, p= 0.007) and OSI (r= 0.338, p= 0.013).

CONCLUSION

Dysregulated amino acid metabolism, high ammonia and oxidative stress were prevalent among autistic children and should be considered in autism management. Still EEG records were inconclusive among autistic children, although may be helpful in assessment autism severity.

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.

Reduced levels of modified nucleosides in the urine of autistic children. Preliminary studies

The aim of this study was to investigate and compare the levels of concentration of modified nucleosides in the urine of autistic and healthy children. The compounds have never been analyzed before. The levels of nucleosides in the urine of both groups were determined by validated high performance liquid chromatography coupled to mass spectrometry (LC-MS/MS) method using multiple reaction monitoring (MRM) mode. Chromatographic separation was achieved with HILIC column and tubercidin was used as the internal standard for the quantification of urinary nucleosides. The within run accuracy and precision ranged from 89 to 106% and from 0.8% to 4.9%, respectively. Lower levels of O-methylguanosine, 7-methylguanosine, 1-methyladenosine, 1-methylguanine, 7-methylguanine and 3-methyladenine in the urine of 22 children with autism, aged 3 to 16 were observed. The differences were not observed in 20 healthy volunteers, in a similar age group. These findings show that modified nucleosides there are metabolic disturbances and nutritional deficiencies in autistic children.