GC-MS-based urine metabolic profiling of 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.

Ectopic expression of an Old Yellow Enzyme (OYE3) gene from Saccharomyces cerevisiae increases the tolerance and phytoremediation of 2-nitroaniline in rice

2-nitroaniline (2-NA) is an environmental pollutant and has been extensively used as intermediates in organic synthesis. The presence of 2-NA in the environment is not only harmful for aquatic life but also mutagenic for human beings. In this study, we constructed transgenic rice expressing an Old Yellow Enzyme gene, ScOYE3, from Saccharomyces cerevisiae. The ScOYE3 transgenic plants were comprehensively investigated for their biochemical responses to 2-NA treatment and their 2-NA phytoremediation capabilities. Our results showed that the rice seedlings exposed to 2-NA stress, showed growth inhibition and biomass reduction. However, the transgenic plants exhibited strong tolerance to 2-NA stress compared to wild-type plants. Ectopic expression of ScOYE3 could effectively protect transgenic plants against 2-NA damage, which resulted in less reactive oxygen species accumulation in transgenic plants than that in wild-type plants. Our phytoremediation assay revealed that transgenic plants could eliminate more 2-NA from the medium than wild-type plants. Moreover, omics analysis was performed in order to get a deeper insight into the mechanism of ScOYE3-mediated 2-NA transformation in rice. Altogether, the function of ScOYE3 during 2-NA detoxification was characterized for the first time, which serves as strong theoretical support for the phytoremediation potential of 2-NA by Old Yellow Enzyme genes.

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.

Using urine FTIR spectra to screen autism spectrum disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder caused by multiple factors, lacking clear biomarkers. Diagnosing ASD still relies on behavioural and developmental signs and usually requires lengthy observation periods, all of which are demanding for both clinicians and parents. Although many studies have revealed valuable knowledge in this field, no clearly defined, practical, and widely acceptable diagnostic tool exists. In this study, 26 children with ASD (ASD+), aged 3-5 years, and 26 sex and age-matched controls are studied to investigate the diagnostic potential of the Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy. The urine FTIR spectrum results show a downward trend in the 3000-2600/cm region for ASD+ children when compared to the typically developing (TD) children of the same age. The average area of this region is 25% less in ASD+ level 3 children, 29% less in ASD+ level 2 children, and 16% less in ASD+ level 1 children compared to that of the TD children. Principal component analysis was applied to the two groups using the entire spectrum window and five peaks were identified for further analysis. The correlation between the peaks and natural urine components is validated by artificial urine solutions. Less-than-normal levels of uric acid, phosphate groups, and ammonium ([Formula: see text]) can be listed as probable causes. This study shows that ATR-FTIR can serve as a practical and non-invasive method to screen ASD using the high-frequency region of the urine spectrum.

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.

Association between Plasma Metabolic Profiles of the Antidepressant Escitalopram and Clinical Response in Subjects with Depression

Liquid chromatography/electrospray ionization-mass spectrometry revealed plasma metabolic profiles for the antidepressant drug escitalopram (ECTP) and associated clinical responses in subjects with major depressive disorder (MDD). Metabolic profiles contribute to variations in responses to drug treatment of depression. To assess clinical responses and treatment outcomes, we quantified the levels of metabolites, including those of the parent drug, in plasma samples collected at different time points (days 0, 7, 14, and 42) during treatment of seven patients with MDD. Results showed that mean plasma levels of key metabolites and ECTP at day 7 were significantly associated with the clinical response at 42 days after treatment. Statistical analyses, including principal component analysis, of key metabolites and ECTP samples at different time points clearly distinguished the clinical responders from non-responder subjects. Although further validation with a larger cohort is necessary, our results indicate that early improvement and plasma levels of key metabolites and ECTP are predictive of therapeutic treatment outcomes and thus can be used to guide the use of ECTP.

Investigating the Urinary Metabolome in the First Year of Life and Its Association with Later Diagnosis of Autism Spectrum Disorder or Non-Typical Neurodevelopment in the MARBLES Study

Developmental disabilities are often associated with alterations in metabolism. However, it remains unknown how early these metabolic issues may arise. This study included a subset of children from the Markers of Autism Risks in Babies-Learning Early Signs (MARBLES) prospective cohort study. In this analysis, 109 urine samples collected at 3, 6, and/or 12 months of age from 70 children with a family history of ASD who went on to develop autism spectrum disorder (ASD n = 17), non-typical development (Non-TD n = 11), or typical development (TD n = 42) were investigated by nuclear magnetic resonance (NMR) spectroscopy to measure urinary metabolites. Multivariate principal component analysis and a generalized estimating equation were performed with the objective of exploring the associations between urinary metabolite levels in the first year of life and later adverse neurodevelopment. We found that children who were later diagnosed with ASD tended to have decreased urinary dimethylamine, guanidoacetate, hippurate, and serine, while children who were later diagnosed with Non-TD tended to have elevated urinary ethanolamine and hypoxanthine but lower methionine and homovanillate. Children later diagnosed with ASD or Non-TD both tended to have decreased urinary 3-aminoisobutyrate. Our results suggest subtle alterations in one-carbon metabolism, gut-microbial co-metabolism, and neurotransmitter precursors observed in the first year of life may be associated with later adverse neurodevelopment.

Global metabolic profiles in a non-human primate model of maternal immune activation: implications for neurodevelopmental disorders

Epidemiological evidence implicates severe maternal infections as risk factors for neurodevelopmental disorders, such as ASD and schizophrenia. Accordingly, animal models mimicking infection during pregnancy, including the maternal immune activation (MIA) model, result in offspring with neurobiological, behavioral, and metabolic phenotypes relevant to human neurodevelopmental disorders. Most of these studies have been performed in rodents. We sought to better understand the molecular signatures characterizing the MIA model in an organism more closely related to humans, rhesus monkeys (Macaca mulatta), by evaluating changes in global metabolic profiles in MIA-exposed offspring. Herein, we present the global metabolome in six peripheral tissues (plasma, cerebrospinal fluid, three regions of intestinal mucosa scrapings, and feces) from 13 MIA and 10 control offspring that were confirmed to display atypical neurodevelopment, elevated immune profiles, and neuropathology. Differences in lipid, amino acid, and nucleotide metabolism discriminated these MIA and control samples, with correlations of specific metabolites to behavior scores as well as to cytokine levels in plasma, intestinal, and brain tissues. We also observed modest changes in fecal and intestinal microbial profiles, and identify differential metabolomic profiles within males and females. These findings support a connection between maternal immune activation and the metabolism, microbiota, and behavioral traits of offspring, and may further the translational applications of the MIA model and the advancement of biomarkers for neurodevelopmental disorders such as ASD or schizophrenia.

Investigation of urine metabolome of BALB/c mouse infected with an avirulent strain of Toxoplasma gondii

Background

The protozoan parasite Toxoplasma gondii is a major concern for human and animal health. Although the metabolic understanding of toxoplasmosis has increased in recent years, the analysis of metabolic alterations through noninvasive methodologies in biofluids remains limited.

Methods

Here, we applied liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics and multivariate statistical analysis to analyze BALB/c mouse urine collected from acutely infected, chronically infected and control subjects.

Results

In total, we identified 2065 and 1409 metabolites in the positive electrospray ionization (ESI +) mode and ESI − mode, respectively. Metabolomic patterns generated from principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) score plots clearly separated T. gondii-infected from uninfected urine samples. Metabolites with altered levels in urine from T. gondii-infected mice revealed changes in pathways related to amino acid metabolism, fatty acid metabolism, and nicotinate and nicotinamide metabolism.

Conclusions

This is the first study to our knowledge on urine metabolic profiling of BALB/c mouse with T. gondii infection. The urine metabolome of infected mouse is distinctive and has value in the understanding of Toxoplasmosis pathogenesis and improvement of treatment.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05408-2.

NMR-Based Metabolomics of Rat Hippocampus, Serum, and Urine in Two Models of Autism

Autism spectrum disorders (ASDs) are increasingly diagnosed as developmental disabilities of unclear etiology related to genetic, epigenetic, or environmental factors. The diagnosis of ASD in children is based on the recognition of typical behavioral symptoms, while no reliable biomarkers are available. Rats in whom ASD-like symptoms are due to maternal administration of the teratogenic drugs valproate or thalidomide on critical day 11 of pregnancy are widely used models in autism research. The present studies, aimed at detecting changes in the levels of hydrophilic and hydrophobic metabolites, were carried out on 1-month-old rats belonging to the abovementioned two ASD models and on a control group. Analysis of both hydrophilic and hydrophobic metabolite levels gives a broader view of possible mechanisms involved in the pathogenesis of autism. Hippocampal proton magnetic resonance (MRS) spectroscopy and ex vivo nuclear magnetic resonance (NMR) analysis of serum and urine samples were used. The results were analyzed using advanced statistical tests. Both the results of our present MRS studies of the hippocampus and of the NMR studies of body fluids in both ASD models, particularly from the THAL model, appeared to be consistent with previously published NMR results of hippocampal homogenates and data from the literature on autistic children. We detected symptoms of disturbances in neurotransmitter metabolism, energy deficit, and oxidative stress, as well as intestinal malfunction, which shed light on the pathogenesis of ASD and could be used for diagnostic purposes. These results confirm the usefulness of the noninvasive techniques used in ASD studies.

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.

Interactions between the intestinal microbiota and epigenome in individuals with autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by variable impairment of cognitive function and interpersonal relationships. Furthermore, some individuals with ASD have gastrointestinal disorders that have been correlated with impairments in intestinal microbiota. Gut microbiota are important not only for intestinal health, but also for many other functions including food digestion, energy production, immune system regulation, and, according to current data, behavior. Disruption of the indigenous microbiota, microbial dysbiosis (imbalance between microorganisms present in the gut), overgrowth of potentially pathogenic microorganisms, a less diverse microbiome, or lower levels of beneficial bacteria in children with ASD can affect behavior. Metabolome analysis in children with ASD has identified perturbations in multiple metabolic pathways that might be associated with cognitive functions. Recent studies have shown that the intestinal microbiome provides environmental signals that can modify host response to stimuli by modifying the host epigenome, which affects DNA methylation, histone modification, and non-coding RNAs. The most studied microbiota-produced epigenetic modifiers are short-chain fatty acids, although other products of intestinal microbiota might also cause epigenetic modifications in the host's DNA. Here we review evidence suggesting that epigenetic alterations caused by modification of gene expression play an important role in understanding ASD.

Serum Metabolomic Analysis of Male Patients with Cannabis or Amphetamine Use Disorder

Studies have demonstrated that chronic consumption of abused drugs induces alterations in several proteins that regulate metabolism. For instance, methamphetamine exposure reduces glucose levels. Fatty and amino acid levels were altered in groups exposed to abused drugs. Therefore, in our study, we investigated the serum metabolomic profile of patients diagnosed with cannabis and/or amphetamine use disorders. Blood was obtained from subjects (control, amphetamine, and cannabis). Detection of serum metabolites was performed using gas chromatography. The ratio peak areas for metabolites were analyzed across the three groups. Both cannabis and amphetamine groups showed higher d-erythrotetrafuranose, octadecanoic acid, hexadecenoic acid, trans-9-octadecanoic acid, lactic acid and methyl thio hydantoin metabolites compared with the control group. Moreover, cannabis patients were found to possess higher glycine, 9,12 octadecanoic acid malonic acid, phosphoric acid and prostaglandin F1a than controls. Our analysis showed that the identified metabolic profile of cannabis or amphetamine use disorder patients was different than control group. Our data indicated that chronic exposure to cannabis or amphetamine dysregulated metabolites in the serum. Future studies are warranted to explore the effects of these abused drugs on the metabolic proteins.

Metabolomics profiling of valproic acid-induced symptoms resembling autism spectrum disorders using 1H NMR spectral analysis in rat model

Prenatal exposure to valproic acid (VPA) has been implicated in the manifestation of autism spectrum disorder (ASD)-like behavioral and functional changes both in human and rodents including mice and rats. The objective of this study was to determine metabolomics profiling and biomarkers related to VPA-induced symptoms resembling ASD using proton nuclear magnetic resonance (1H-NMR) spectral data. VPA was administered to pregnant rats at gestation day 12.5 and effects measured subsequently in male 4-week-old offspring pups. The sociability of VPA-treated animals was significantly diminished and exhibited ASD-like behavior as evidenced by reduction of social adaptation disorder and lack of social interactions. To find biomarkers related to ASD, the following were collected prefrontal brain cortices, urine bladder and blood samples directly from heart puncture. In all samples, principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) displayed significant clustering pattern differences between control and treated groups. Valine, taurine, myo-inositol, 3-hydroxybutyrate and 1,3-dihydroxyacetone were significantly decreased in brain cortices in treated rats. Serum metabolites of glucose, creatine phosphate, lactate, glutamine and threonine were significantly increased in VPA-administered animals. Urinary metabolites of pimelate, 3-hydroxyisovalerate and valerate were significantly reduced in VPA-treated rat, whereas galactose and galactonate levels were elevated. Various metabolites were associated with mitochondrial dysfunction metabolism and central nervous system disorders. Data demonstrated that VPA-induced alterations in endogenous metabolites of serum, urine, and brain cortex which might prove useful as biomarkers for symptoms resembling ASD as a model of this disorder.

Using random forest to detect multiple inherited metabolic diseases simultaneously based on GC-MS urinary metabolomics

Inborn errors of metabolism, also known as inherited metabolic diseases (IMDs), are related to genetic mutations and cause corresponding biochemical metabolic disorder of newborns and even sudden infant death. Timely detection and diagnosis of IMDs are of great significance for improving survival of newborns. Here we propose a strategy for simultaneously detecting six types of IMDs via combining GC-MS technique with the random forest algorithm (RF). Clinical urine samples from IMD and healthy patients are analyzed using GC-MS for acquiring metabolomics data. Then, the RF model is established as a multi-classification tool for the GC-MS data. Compared with the models built by artificial neural network and support vector machine, the results demonstrated the RF model has superior performance of high specificity, sensitivity, precision, accuracy, and matthews correlation coefficients on identifying all six types of IMDs and normal samples. The proposed strategy can afford a useful method for reliable and effective identification of multiple IMDs in clinical diagnosis.

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.

Plasma metabolomics of autism spectrum disorder and influence of shared components in proband families

Prevalence of autism spectrum disorder (ASD) has been increasing in the United States in the past decades. The exact mechanisms remain enigmatic, and diagnosis of the disease still relies primarily on assessment of behavior. We first used a case-control design (75 idiopathic cases and 29 controls, enrolled at Boston Children's Hospital from 2007-2012) to identify plasma biomarkers of ASD through a metabolome-wide association study approach. Then we leveraged a family-based design (31 families) to investigate the influence of shared genetic and environmental components on the autism-associated features. Using untargeted high-resolution mass spectrometry metabolomics platforms, we detected 19 184 features. Of these, 191 were associated with ASD (false discovery rate < 0.05). We putatively annotated 30 features that had an odds ratio (OR) between <0.01 and 5.84. An identified endogenous metabolite, O-phosphotyrosine, was associated with an extremely low autism odds (OR 0.17; 95% confidence interval 0.06-0.39). We also found that glutathione metabolism was associated with ASD (P = 0.048). Correlations of the significant features between proband and parents were low (median = 0.09). Of the 30 annotated features, the median correlations within families (proband-parents) were -0.15 and 0.24 for the endogenous and exogenous metabolites, respectively. We hypothesize that, without feature identification, family-based correlation analysis of autism-associated features can be an alternative way to assist the prioritization of potentially diagnostic features. A panel of ASD diagnostic metabolic markers with high specificity could be derived upon further studies.

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.

The Impact of Gut Microbiota-Derived Metabolites in Autism Spectrum Disorders

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders characterised by behavioural impairment and deficiencies in social interaction and communication. A recent study estimated that 1 in 89 children have developed some form of ASD in European countries. Moreover, there is no specific treatment and since ASD is not a single clinical entity, the identification of molecular biomarkers for diagnosis remains challenging. Besides behavioural deficiencies, individuals with ASD often develop comorbid medical conditions including intestinal problems, which may reflect aberrations in the bidirectional communication between the brain and the gut. The impact of faecal microbial composition in brain development and behavioural functions has been repeatedly linked to ASD, as well as changes in the metabolic profile of individuals affected by ASD. Since metabolism is one of the major drivers of microbiome-host interactions, this review aims to report emerging literature showing shifts in gut microbiota metabolic function in ASD. Additionally, we discuss how these changes may be involved in and/or perpetuate ASD pathology. These valuable insights can help us to better comprehend ASD pathogenesis and may provide relevant biomarkers for improving diagnosis and identifying new therapeutic targets.

Maternal Plasma Metabolic Profile Demarcates a Role for Neuroinflammation in Non-Typical Development of Children

Maternal and cord plasma metabolomics were used to elucidate biological pathways associated with increased diagnosis risk for autism spectrum disorders (ASD). Metabolome-wide associations were assessed in both maternal and umbilical cord plasma in relation to diagnoses of ASD and other non-typical development (Non-TD) compared to typical development (TD) in the Markers of Autism risk in Babies: Learning Early Signs (MARBLES) cohort study of children born to mothers who already have at least one child with ASD. Analyses were stratified by sample matrix type, machine mode, and annotation confidence level. Dimensionality reduction techniques were used [i.e, principal component analysis (PCA) and random subset weighted quantile sum regression (WQSRS)] to minimize the high multiple comparison burden. With WQSRS, a metabolite mixture obtained from the negative mode of maternal plasma decreased the odds of Non-TD compared to TD. These metabolites, all related to the prostaglandin pathway, underscored the relevance of neuroinflammation status. No other significant findings were observed. Dimensionality reduction strategies provided confirming evidence that a set of maternal plasma metabolites are important in distinguishing Non-TD compared to TD diagnosis. A lower risk for Non-TD was linked to anti-inflammatory elements, thereby linking neuroinflammation to detrimental brain function consistent with studies ranging from neurodevelopment to neurodegeneration.

Metabolism navigates neural cell fate in development, aging and neurodegeneration

An uninterrupted energy supply is critical for the optimal functioning of all our organs, and in this regard the human brain is particularly energy dependent. The study of energy metabolic pathways is a major focus within neuroscience research, which is supported by genetic defects in the oxidative phosphorylation mechanism often contributing towards neurodevelopmental disorders and changes in glucose metabolism presenting as a hallmark feature in age-dependent neurodegenerative disorders. However, as recent studies have illuminated roles of cellular metabolism that span far beyond mere energetics, it would be valuable to first comprehend the physiological involvement of metabolic pathways in neural cell fate and function, and to subsequently reconstruct their impact on diseases of the brain. In this Review, we first discuss recent evidence that implies metabolism as a master regulator of cell identity during neural development. Additionally, we examine the cell type-dependent metabolic states present in the adult brain. As metabolic states have been studied extensively as crucial regulators of malignant transformation in cancer, we reveal how knowledge gained from the field of cancer has aided our understanding in how metabolism likewise controls neural fate determination and stability by directly wiring into the cellular epigenetic landscape. We further summarize research pertaining to the interplay between metabolic alterations and neurodevelopmental and psychiatric disorders, and expose how an improved understanding of metabolic cell fate control might assist in the development of new concepts to combat age-dependent neurodegenerative diseases, particularly Alzheimer's disease.

The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota

BACKGROUND

Autism spectrum disorders (ASD) are associated with dysregulation of the microbiota-gut-brain axis, changes in microbiota composition as well as in the fecal, serum, and urine levels of microbial metabolites. Yet a causal relationship between dysregulation of the microbiota-gut-brain axis and ASD remains to be demonstrated. Here, we hypothesized that the microbial metabolite p-Cresol, which is more abundant in ASD patients compared to neurotypical individuals, could induce ASD-like behavior in mice.

RESULTS

Mice exposed to p-Cresol for 4 weeks in drinking water presented social behavior deficits, stereotypies, and perseverative behaviors, but no changes in anxiety, locomotion, or cognition. Abnormal social behavior induced by p-Cresol was associated with decreased activity of central dopamine neurons involved in the social reward circuit. Further, p-Cresol induced changes in microbiota composition and social behavior deficits could be transferred from p-Cresol-treated mice to control mice by fecal microbiota transplantation (FMT). We also showed that mice transplanted with the microbiota of p-Cresol-treated mice exhibited increased fecal p-Cresol excretion, compared to mice transplanted with the microbiota of control mice. In addition, we identified possible p-Cresol bacterial producers. Lastly, the microbiota of control mice rescued social interactions, dopamine neurons excitability, and fecal p-Cresol levels when transplanted to p-Cresol-treated mice.

CONCLUSIONS

The microbial metabolite p-Cresol induces selectively ASD core behavioral symptoms in mice. Social behavior deficits induced by p-Cresol are dependant on changes in microbiota composition. Our study paves the way for therapeutic interventions targeting the microbiota and p-Cresol production to treat patients with ASD. Video abstract.

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.

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.

Investigating the Role of Diet and Exercise in Gut Microbe-Host Cometabolism

We investigated the individual and combined effects of diet and physical exercise on metabolism and the gut microbiome to establish how these lifestyle factors influence host-microbiome cometabolism. Urinary and fecal samples were collected from athletes and less active controls. Individuals were further classified according to an objective dietary assessment score of adherence to healthy dietary habits according to WHO guidelines, calculated from their proton nuclear magnetic resonance (1H-NMR) urinary profiles. Subsequent models were generated comparing extremes of dietary habits, exercise, and the combined effect of both. Differences in metabolic phenotypes and gut microbiome profiles between the two groups were assessed. Each of the models pertaining to diet healthiness, physical exercise, or a combination of both displayed a metabolic and functional microbial signature, with a significant proportion of the metabolites identified as discriminating between the various pairwise comparisons resulting from gut microbe-host cometabolism. Microbial diversity was associated with a combination of high adherence to healthy dietary habits and exercise and was correlated with a distinct array of microbially derived metabolites, including markers of proteolytic activity. Improved control of dietary confounders, through the use of an objective dietary assessment score, has uncovered further insights into the complex, multifactorial relationship between diet, exercise, the gut microbiome, and metabolism. Furthermore, the observation of higher proteolytic activity associated with higher microbial diversity indicates that increased microbial diversity may confer deleterious as well as beneficial effects on the host.IMPORTANCE Improved control of dietary confounders, through the use of an objective dietary assessment score, has uncovered further insights into the complex, multifactorial relationship between diet, exercise, the gut microbiome, and metabolism. Each of the models pertaining to diet healthiness, physical exercise, or a combination of both, displayed a distinct metabolic and functional microbial signature. A significant proportion of the metabolites identified as discriminating between the various pairwise comparisons result from gut microbe-host cometabolism, and the identified interactions have expanded current knowledge in this area. Furthermore, although increased microbial diversity has previously been linked with health, our observation of higher microbial diversity being associated with increased proteolytic activity indicates that it may confer deleterious as well as beneficial effects on the host.

Urinary Metabonomic Profiling Discriminates Between Children with Autism and Their Healthy Siblings

Background

Autism spectrum disorder (ASD) is a complicated neuropsychiatric disease that displays significant heterogeneity. The diagnosis of ASD is currently primarily dependent upon descriptions of clinical symptoms, and it remains urgent to find biological markers for the detection and diagnosis of autism. The current study applied the urinary metabolic profiling approach to characterize metabolic phenotypes in ASD.

Material/Methods

Urine was obtained from children with ASD and their matched healthy siblings. Samples were analyzed using ¹H NMR-based methods designed to measure a broad range of metabolites. Partial least-square-discriminant analysis (PLS-DA) was used to develop models to identify metabonomic variations that can be used to distinguish between individuals with ASD and their unaffected siblings.

Results

A significant difference was observed between the metabolomic profiles of children with ASD and that of their healthy siblings. An increase in the levels of tryptophan, hippurate, glycine, and creatine, and a decrease in trigonelline, melatonin, pantothenate, serotonin, and taurine were observed compared to the control group. We conclude that several metabolic pathways are affected by autism, which suggests that a gut-brain link may be important in the pathophysiology of ASD.

Conclusions

¹H NMR-based metabonomic analysis of the urine can determine perturbations of specific metabolic pathways related to ASD and help identify a characteristic metabolic fingerprint to better understand the disease and its causes.

Metabolomics analysis of microbiota-gut-brain axis in neurodegenerative and psychiatric diseases

Gut microbiota represents a complex physiological ecosystem that influences the host health. Alterations in the microbiome metabolism affect the body homeostasis and they have been associated with the development of different human neurodegenerative and neuropsychiatric disorders, such as Alzheimer's disease, autism spectrum disorder, bipolar disorder, depression, Huntington's disease, Parkinson's disease, posttraumatic stress disorder and schizophrenia. The development of these complex diseases is influenced by various factors, including genetic predisposition and environmental triggers. Gut microbiota has recently emerged as an important actor in their physiopathology that has been shown to play a role in inflammation, oxidative stress, and gut permeability. Therefore, targeting the metabolites that are produced by or associated with the gut microbiota may help us understand how imbalance in the gut-brain axis affects human health. This review offers a comprehensive overview of the literature on this matter, offering the readers an insight in the state-of-art metabolic measurements of the gut-brain axis in various brain-related diseases.

A Novel UHPLC-MS Method Targeting Urinary Metabolomic Markers for Autism Spectrum Disorder

Autism spectrum disorder is a heterogeneous neurodevelopmental disease. Currently, no biomarker of this disease is known. Diagnosis is performed through observation, standardized behavioral scales, and interviews with parents. In practice, diagnosis is often delayed to the average age of four years or even more which adversely affects a child’s perspective. A laboratory method allowing to detect the disorder at earlier stages is of a great need, as this could help the patients to start with treatment at a younger age, even prior to the clinical diagnosis. Recent evidence indicates that metabolomic markers should be considered as diagnostic markers, also serving for further differentiation and characterization of different subgroups of the autism spectrum. In this study, we developed an ultra-high performance liquid chromatography-tandem triple quadrupole mass spectrometry method for the simultaneous determination of six metabolites in human urine. These metabolites, namely methylguanidine, N-acetyl arginine, inosine, indole-3-acetic acid, indoxyl sulfate and xanthurenic acid were selected as potential biomarkers according to prior metabolomic studies. The analysis was carried out by means of reversed-phase liquid chromatography with gradient elution. Separation of the metabolites was performed on a Phenomenex Luna^® Omega Polar C18 (100 × 1.0 mm, 1.6 µm) column at a flow rate of 0.15 mL/min with acetonitrile/water 0.1% formic acid aqueous as the mobile phase. The analysis was performed on a group of children with autism spectrum disorder and age-matched controls. In school children, we have detected disturbances in the levels of oxidative stress markers connected to arginine and purine metabolism, namely methylguanidine and N-acetylargine. Also, products of gut bacteria metabolism, namely indoxyl sulfate and indole-3-acetic acid, were found to be elevated in the patients’ group. We can conclude that this newly developed method is fast, sensitive, reliable, and well suited for the quantification of proposed markers.

The gut microbiota-brain axis in behaviour and brain disorders

In a striking display of trans-kingdom symbiosis, gut bacteria cooperate with their animal hosts to regulate the development and function of the immune, metabolic and nervous systems through dynamic bidirectional communication along the 'gut-brain axis'. These processes may affect human health, as certain animal behaviours appear to correlate with the composition of gut bacteria, and disruptions in microbial communities have been implicated in several neurological disorders. Most insights about host-microbiota interactions come from animal models, which represent crucial tools for studying the various pathways linking the gut and the brain. However, there are complexities and manifest limitations inherent in translating complex human disease to reductionist animal models. In this Review, we discuss emerging and exciting evidence of intricate and crucial connections between the gut microbiota and the brain involving multiple biological systems, and possible contributions by the gut microbiota to neurological disorders. Continued advances from this frontier of biomedicine may lead to tangible impacts on human health.

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

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.

P-cresol Alters Brain Dopamine Metabolism and Exacerbates Autism-Like Behaviors in the BTBR Mouse

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction/communication, stereotypic behaviors, restricted interests, and abnormal sensory-processing. Several studies have reported significantly elevated urinary and foecal levels of p-cresol in ASD children, an aromatic compound either of environmental origin or produced by specific gut bacterial strains. Methods: Since p-cresol is a known uremic toxin, able to negatively affect multiple brain functions, the present study was undertaken to assess the effects of a single acute injection of low- or high-dose (1 or 10 mg/kg i.v. respectively) of p-cresol in behavioral and neurochemical phenotypes of BTBR mice, a reliable animal model of human ASD. Results: P-cresol significantly increased anxiety-like behaviors and hyperactivity in the open field, in addition to producing stereotypic behaviors and loss of social preference in BTBR mice. Tissue levels of monoaminergic neurotransmitters and their metabolites unveiled significantly activated dopamine turnover in amygdala as well as in dorsal and ventral striatum after p-cresol administration; no effect was recorded in medial-prefrontal cortex and hippocampus. Conclusion: Our study supports a gene x environment interaction model, whereby p-cresol, acting upon a susceptible genetic background, can acutely induce autism-like behaviors and produce abnormal dopamine metabolism in the reward circuitry.

Effect of bound water on the quality of dried Lentinus edodes during storage

BACKGROUND

Water absorption is the dominant factor affecting the quality deterioration of dried Lentinus edodes. We therefore analyzed the effect of moisture content and dynamic water status on physical properties of the mushroom stored at water activity (a_w ), 0.33, 0.43, 0.67, 0.76, and 0.84 for 50 days. Moisture mobility and water status were analyzed using low-field nuclear magnetic resonance, while hardness and microstructure were determined as texture characteristics. Meanwhile, an electronic nose and headspace solid-phase micro-extraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS) were used to analyze the flavor properties of dried L. edodes.

RESULTS

The results showed that bound water was the dominant water status in dried L. edodes. The content and molecular mobility of bound water increased at a_w = 0.67, 0.76, and 0.84. This contributed to discoloration, hardness loss, and microstructure sparsity of dried L. edodes. The increasing content and molecular mobility of bound water aggravated the deterioration of characteristic flavor by reducing acid, aldehyde, and ketone content.

CONCLUSION

Unlike immobilized or free water, bound water had a critical influence on the quality deterioration of dried L. edodes during storage. © 2019 Society of Chemical Industry.

Metabolomics analysis of children with autism, idiopathic-developmental delays, and Down syndrome

Although developmental delays affect learning, language, and behavior, some evidence suggests the presence of disturbances in metabolism are associated with psychiatric disorders. Here, the plasma metabolic phenotype of children with autism spectrum disorder (ASD, n = 167), idiopathic-developmental delay (i-DD, n = 51), and Down syndrome (DS, n = 31), as compared to typically developed (TD, n = 193) controls was investigated in a subset of children from the case-control Childhood Autism Risk from Genetics and the Environment (CHARGE) Study. Metabolome profiles were obtained using nuclear magnetic resonance spectroscopy and analyzed in an untargeted manner. Forty-nine metabolites were identified and quantified in each sample that included amino acids, organic acids, sugars, and other compounds. Multiple linear regression analysis revealed significant associations between 11 plasma metabolites and neurodevelopmental outcome. Despite the varied origins of these developmental disabilities, we observed similar perturbation in one-carbon metabolism pathways among DS and ASD cases. Similarities were also observed in the DS and i-DD cases in the energy-related tricarboxylic acid cycle. Other metabolites and pathways were uniquely associated with DS or ASD. By comparing metabolic signatures between these conditions, the current study expands on extant literature demonstrating metabolic alterations associated with developmental disabilities and provides a better understanding of overlapping vs specific biological perturbations associated with these disorders.

Biomarkers in aggression

Aggressive behavior exerts an enormous impact on society remaining among the main causes of worldwide premature death. Effective primary interventions, relying on predictive models of aggression that show adequate sensitivity and specificity are currently lacking. One strategy to increase the accuracy and precision of prediction would be to include biological data in the predictive models. Clearly, to be included in such models, biological markers should be reliably associated with the specific trait under study (i.e., diagnostic biomarkers). Aggression, however, is phenotypically highly heterogeneous, an element that has hindered the identification of reliable biomarkers. However, current research is trying to overcome these challenges by focusing on more homogenous aggression subtypes and/or by studying large sample size of aggressive individuals. Further advance is coming by bioinformatics approaches that are allowing the integration of inter-species biological data as well as the development of predictive algorithms able to discriminate subjects on the basis of the propensity toward aggressive behavior. In this review we first present a brief summary of the available evidence on neuroimaging of aggression. We will then treat extensively the data on genetic determinants, including those from hypothesis-free genome-wide association studies (GWAS) and candidate gene studies. Transcriptomic and neurochemical biomarkers will then be reviewed, and we will dedicate a section on the role of metabolomics in aggression. Finally, we will discuss how biomarkers can inform the development of new pharmacological tools as well as increase the efficacy of preventive strategies.

Untargeted Metabolomics for Autism Spectrum Disorders: Current Status and Future Directions

Autism spectrum disorders (ASDs) are a group of neurodevelopment disorders characterized by childhood onset deficits in social communication and interaction. Although the exact etiology of most cases of ASDs is unknown, a portion has been proposed to be associated with various metabolic abnormalities including mitochondrial dysfunction, disorders of cholesterol metabolism, and folate abnormalities. Targeted biochemical testing like plasma amino acid and acylcarnitine profiles have demonstrated limited utility in helping to diagnose and manage such patients. Untargeted metabolomics has emerged, however, as a promising tool in screening for underlying biochemical abnormalities and managing treatment and as a means of investigating possible novel biomarkers for the disorder. Here, we review the principles and methodology behind untargeted metabolomics, recent pilot studies utilizing this technology, and areas in which it may be integrated into the care of children with this disorder in the future.

Identification of gender-related metabolic disturbances in autism spectrum disorders using urinary metabolomics

Autism spectrum disorders (ASD) are a highly heterogeneous group of neurodevelopmental disorders that are more commonly diagnosed in boys than in girls. The reasons for gender differences in ASD are unknown and no definitive current evidence can explain male predominance. Therefore, in search for laboratory biomarkers responsible for ASD, a comprehensive metabolomics study was performed by metabolic profiling of urine samples in 51 ASD subjects and 51 age- and sex-matched children with typical development. Orthogonal partial least-squares discriminant analysis (OPLS-DA) models with poor quality failed to perform the analysis based on gender in the ASD and control groups. OPLS-DA models based on single-sex samples, especially in female subjects, had better clustering between the ASD and control groups with an increase in the R² and Q² values compared with those in the whole group. Significantly increased levels of adenine, 2-Methylguanosine, creatinine, and 7alpha-hydroxytestololactone and a decrease in creatine were observed in the female ASD subjects. In particular, 7alpha-hydroxytestololactone, which has a structure similar to that of testolactone, was positively correlated with adenine (Pearson correlation coefficient, r = 0.738, p < 0.01), creatinine (r = 0.826, p < 0.01), and 2-Methylguanosine (r = 0.757, p < 0.01) and negatively correlated with creatine (r=-0.413, p < 0.05). A receiver operating characteristic curve analysis using the creatinine:creatine ratio yielded an area under the curve of 0.913 (95% CI: 0.806-1). These metabolites may be sex-related or sex-sensitive to an extent and can be valuable for identification of the molecular pathways involved in the gender bias in manifestation of ASD. The creatinine:creatine ratio has a potential to be a good predictor of ASD in the female subjects.

Metabolome signature of autism in the human prefrontal cortex

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder with yet incompletely uncovered molecular determinants. Alterations in the abundance of low molecular weight compounds (metabolites) in ASD could add to our understanding of the disease. Indeed, such alterations take place in the urine, plasma and cerebellum of ASD individuals. In this work, we investigated mass-spectrometric signal intensities of 1,366 metabolites in the prefrontal cortex grey matter of 32 ASD and 40 control individuals. 15% of these metabolites showed significantly different intensities in ASD and clustered in 16 metabolic pathways. Of them, ten pathways were altered in urine and blood of ASD individuals (Fisher test, p < 0.05), opening an opportunity for the design of new diagnostic instruments. Furthermore, metabolic measurements conducted in 40 chimpanzees and 40 macaques showed an excess of metabolite intensity differences unique to humans, supporting the hypothesized disruption of evolutionary novel cortical mechanisms in ASD.

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

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

A multiplatform metabolomic approach to characterize fecal signatures of negative postnatal events in chicks: a pilot study

Background

Negative experiences in early life can induce long-lasting effects on the welfare, health, and performance of farm animals. A delayed placement of chicks in rearing houses has negative effects on their performance, and results in fecal-specific odors detectable by rats. Based on this observation, the volatile organic compounds (VOCs) and metabolites from the feces of 12-day-old chickens were screened for early markers of response to negative events using gas-chromatography and liquid-chromatography coupled with mass spectrometry (GC-MS, LC-HRMS).

Results

The low reproducibility of solid-phase micro-extraction of the VOCs followed by GC-MS was not suitable for marker discovery, in contrast to liquid extraction of metabolites from freeze-dried feces followed by GC-MS or LC-HRMS analysis. Therefore, the fecal metabolome from 12-day-old chicks having experienced a normal or delayed placement were recorded by GC-MS and LC-HRMS in two genotypes from two experiments. From both experiments, 25 and 35 metabolites, respectively explaining 81% and 45% of the difference between delayed and control chickens, were identified by orthogonal partial least-squares discriminant analysis from LC-HRMS and GC-MS profiling.

Conclusion

The sets of molecules identified will be useful to better understand the chicks’ response to negative events over time and will contribute to define stress or welfare biomarkers. 

Electronic supplementary material

The online version of this article (10.1186/s40104-019-0335-8) contains supplementary material, which is available to authorized users.

Altered Urinary Amino Acids in Children With Autism Spectrum Disorders

Autism spectrum disorders (ASD) affect 1% of children. Although there is no cure, early diagnosis and behavioral intervention can relieve the symptoms. The clinical heterogeneity of ASD has created a need for improved sensitive and specific laboratory diagnostic methods. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based analysis of the metabolome has shown great potential to uncover biomarkers for complex diseases such as ASD. Here, we used a two-step discovery–validation approach to identify potential novel metabolic biomarkers for ASD. Urine samples from 57 children with ASD and 81 matched children with typical development (TD) were analyzed by LS-MS/MS to assess differences in urinary amino acids and their metabolites (referred to as UAA indicators). A total of 63 UAA indicators were identified, of which 21 were present at significantly different levels in the urine of ASD children compared with TD children. Of these 21, the concentrations of 19 and 10 were higher and lower, respectively, in the urine of ASD children compared with TD children. Using support vector machine modeling and receiver operating characteristic curve analysis, we identified a panel of 7 UAA indicators that discriminated between the samples from ASD and TD children (lysine, 2-aminoisobutyric acid, 5-hydroxytryptamine, proline, aspartate, arginine/ornithine, and 4-hydroxyproline). Among the significantly changed pathways in ASD children were the ornithine/urea cycle (decreased levels of the excitatory amino acid aspartate [p = 2.15 × 10^-10] and increased arginine/ornithine [p = 5.21 × 10^-9]), tryptophan metabolism (increased levels of inhibitory 5-hydroxytryptamine p = 3.62 × 10^-9), the methionine cycle (increased methionine sulfoxide [p = 1.46 × 10^-10] and decreased homocysteine [p = 2.73 × 10^-7]), and lysine metabolism (reduced lysine [p = 7.8 × 10^-9], α-aminoadipic acid [p = 1.16 × 10^-9], and 5-aminovaleric acid [p = 1.05 × 10^-5]). Collectively, the data presented here identify a possible imbalance between excitatory and inhibitory amino acid metabolism in ASD children. The significantly altered UAA indicators could therefore be potential diagnostic biomarkers for ASD.

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.

A Subset of Patients With Autism Spectrum Disorders Show a Distinctive Metabolic Profile by Dried Blood Spot Analyses

Autism spectrum disorder (ASD) is currently diagnosed according to behavioral criteria. Biomarkers that identify children with ASD could lead to more accurate and early diagnosis. ASD is a complex disorder with multifactorial and heterogeneous etiology supporting recognition of biomarkers that identify patient subsets. We investigated an easily testable blood metabolic profile associated with ASD diagnosis using high throughput analyses of samples extracted from dried blood spots (DBS). A targeted panel of 45 ASD analytes including acyl-carnitines and amino acids extracted from DBS was examined in 83 children with ASD (60 males; age 6.06 ± 3.58, range: 2-10 years) and 79 matched, neurotypical (NT) control children (57 males; age 6.8 ± 4.11 years, range 2.5-11 years). Based on their chronological ages, participants were divided in two groups: younger or older than 5 years. Two-sided T-tests were used to identify significant differences in measured metabolite levels between groups. Näive Bayes algorithm trained on the identified metabolites was used to profile children with ASD vs. NT controls. Of the 45 analyzed metabolites, nine (20%) were significantly increased in ASD patients including the amino acid citrulline and acyl-carnitines C2, C4DC/C5OH, C10, C12, C14:2, C16, C16:1, C18:1 (P: < 0.001). Näive Bayes algorithm using acyl-carnitine metabolites which were identified as significantly abnormal showed the highest performances for classifying ASD in children younger than 5 years (n: 42; mean age 3.26 ± 0.89) with 72.3% sensitivity (95% CI: 71.3;73.9), 72.1% specificity (95% CI: 71.2;72.9) and a diagnostic odds ratio 11.25 (95% CI: 9.47;17.7). Re-test analyses as a measure of validity showed an accuracy of 73% in children with ASD aged ≤ 5 years. This easily testable, non-invasive profile in DBS may support recognition of metabolic ASD individuals aged ≤ 5 years and represents a potential complementary tool to improve diagnosis at earlier stages of ASD development.

In the search for reliable biomarkers for the early diagnosis of autism spectrum disorder: the role of vitamin D

Autism spectrum disorder (ASD) affects about 1% of the world's population. Vitamin D is thought to be essential for normal brain development and modulation of the immune system. Worldwide about 1 billion people are affected by vitamin D deficiency. High-sensitivity C-reactive protein (hs-CRP), cytochrome P450 2E1 (CYP2E1) and 8-hydroxy-2'-deoxyguanosine (8-OH-dG) are biomarkers related to inflammation and oxidative stress. In the present study, these biomarkers were together with serum 25-hydroxyvitamin D (25(OH)D₃) analyzed in 28 (mean age seven years) Saudi male patients with ASD. The study was conducted to determine if there is any relationship between vitamin D levels, the tested biomarkers and the presence and severity of ASD. The hope was to identify if these biomarkers may be useful for early ASD diagnosis. The Childhood Autism Rating Scale (CARS) and the Social Responsiveness Scale (SRS) were used to measure autism severity. The results of the ASD children were compared with 27 age and gender-matched neurotypical controls. The data indicated that Saudi patients with ASD have significantly lower plasma levels of 25(OH)D₃ than neurotypical controls (38 ng/ml compared to 56 ng/ml, respectively; [P = 0.001]). Surprisingly, the levels of CYP2E1 were lower in the children with ASD than the neurotypical controls (0.48 ± 0.08 vs. 69 ± 0.07 ng/ml, respectively; P = 0.001). The ASD children also had significantly higher levels of hs-CRP (0.79 ± 0.09 vs. 0.59 ± 0.09 ng/ml, respectively; P = 0.001) and 8-OH-dG (8.17 ± 1.04 vs. 4.13 ± 1.01 ng/ml, respectively; P = 0.001, compared to neurotypical age and gender-matched controls. The values for hs-CRP and 8-OH-dG did not correlate [P < 0.001] with autism severity. There was found a relationship between autism severity on the CARS scale and the levels of 25(OH)D₃ and CYP1B1. But this was not found for SRS. All four biomarkers seemed to have good sensitivity and specificity, but the sample size of the present study was too small to determine clinical usefulness. The findings also indicate that inadequate levels of vitamin D play a role in the etiology and severity of autism. Furthermore, the results of the present study suggest the possibility of using 25(OH)D₃, CYP1B1, hs-CRP and 8-OH-dG, preferably in combination, as biomarkers for the early diagnosis of ASD. However, further research is needed to evaluate this hypothesis.

Identification of urinary metabolites that correlate with clinical improvements in children with autism treated with sulforaphane from broccoli

Background

Children with autism spectrum disorder (ASD) have urinary metabolites suggesting impairments in several pathways, including oxidative stress, inflammation, mitochondrial dysfunction, and gut microbiome alterations. Sulforaphane, a supplement with indirect antioxidant effects that are derived from broccoli sprouts and seeds, was recently shown to lead to improvements in behavior and social responsiveness in children with ASD. We conducted the current open-label study to determine if we could identify changes in urinary metabolites that were associated with clinical improvements with the goal of identifying a potential mechanism of action.

Methods

Children and young adults enrolled in a school for children with ASD and related neurodevelopmental disorders were recruited to participate in a 12-week, open-label study of sulforaphane. Fasting urinary metabolites and measures of behavior (Aberrant Behavior Checklist—ABC) and social responsiveness (Social Responsiveness Scale—SRS) were measured at baseline and at the end of the study. Pearson’s correlation coefficient was calculated for the pre- to post-intervention change in each of the two clinical scales (ABS and SRS) versus the change in each metabolite.

Results

Fifteen children completed the 12-week study. Mean scores on both symptom measures showed improvements (decreases) over the study period, but only the change in the SRS was significant. The ABC improved − 7.1 points (95% CI − 17.4 to 3.2), and the SRS improved − 9.7 points (95% CI − 18.7 to − 0.8). We identified 77 urinary metabolites that were correlated with changes in symptoms, and they clustered into pathways of oxidative stress, amino acid/gut microbiome, neurotransmitters, hormones, and sphingomyelin metabolism.

Conclusions

Urinary metabolomics analysis is a useful tool to identify pathways that may be involved in the mechanism of action of treatments targeting abnormal physiology in ASD.

Trial registration

This study was prospectively registered at clinicaltrials.gov (NCT02654743) on January 11, 2016.

Advances in the design of nanomaterial-based electrochemical affinity and enzymatic biosensors for metabolic biomarkers: A review

This review (with 340 refs) focuses on methods for specific and sensitive detection of metabolites for diagnostic purposes, with particular emphasis on electrochemical nanomaterial-based sensors. It also covers novel candidate metabolites as potential biomarkers for diseases such as neurodegenerative diseases, autism spectrum disorder and hepatitis. Following an introduction into the field of metabolic biomarkers, a first major section classifies electrochemical biosensors according to the bioreceptor type (enzymatic, immuno, apta and peptide based sensors). A next section covers applications of nanomaterials in electrochemical biosensing (with subsections on the classification of nanomaterials, electrochemical approaches for signal generation and amplification using nanomaterials, and on nanomaterials as tags). A next large sections treats candidate metabolic biomarkers for diagnosis of diseases (in the context with metabolomics), with subsections on biomarkers for neurodegenerative diseases, autism spectrum disorder and hepatitis. The Conclusion addresses current challenges and future perspectives. Graphical abstract This review focuses on the recent developments in electrochemical biosensors based on the use of nanomaterials for the detection of metabolic biomarkers. It covers the critical metabolites for some diseases such as neurodegenerative diseases, autism spectrum disorder and hepatitis.

Liquid chromatography/mass spectrometry-based plasma metabolic profiling study of escitalopram in subjects with major depressive disorder

Liquid chromatography-mass spectrometry (LC-MS) method revealed the plasma metabolite profiles in major depressive disorder patients treated with escitalopram (ECTP) (n = 7). Depression severity was assessed according to the 17-item Hamilton Depression Rating Scale. Metabolic profiles were derived from major depressive disorder subject blood samples collected after ECTP treatment. Blood plasma was separated and processed in order to effectively extract metabolites, which were then analyzed using LC-MS. We identified 19 metabolites and elucidated their structures using LC-tandem MS (LC-MS/MS) combined with elemental compositions derived from accurate mass measurements. We further used online H/D exchange experiments to verify the structural elucidations of each metabolite. Identifying molecular metabolites may provide critical insights into the pharmacological and clinical effects of ECTP treatment and may also provide useful information informing the development of new antidepressant treatments. These detailed plasma metabolite analyses may also be used to identify optimal dose concentrations in psychopharmacotherapeutic treatment through drug monitoring, as well as forming the basis for response predictions in depressed subjects.