1H–13C NMR-based urine metabolic profiling in autism spectrum disorders

Exploring microbiota-gut-brain axis biomarkers linked to autism spectrum disorder in prenatally chlorpyrifos-exposed Fmr1 knock-out and wild-type male rats

Fmr1 (fragile X messenger ribonucleoprotein 1)-knockout (KO) rats, modeling the human Fragile X Syndrome (FXS), are of particular interest for exploring the ASD-like phenotype in preclinical studies. Gestational exposure to chlorpyrifos (CPF) has been associated with ASD diagnosis in humans and ASD-like behaviors in rodents and linked to the microbiota-gut-brain axis. In this study, we have used both Fmr1-KO and wild-type male rats (F2 generation) at postnatal days (PND) 7 and 40 obtained after F1 pregnant females were randomly exposed to 1 mg/kg/mL/day of CPF or vehicle. A nuclear magnetic resonance (NMR) metabolomics approach together with gene expression profiles of these F2 generation rats were employed to analyze different brain regions (such as prefrontal cortex, hippocampus, and cerebellum), whole large intestine (at PND7) and gut content (PND40). The statistical comparison of each matrix spectral profile unveiled tissue-specific metabolic fingerprints. Significant variations in some biomarker levels were detected among brain tissues of different genotypes, including taurine, myo-inositol, and 3-hydroxybutyric acid, and exposure to CPF induced distinct metabolic alterations, particularly in serine and myo-inositol. Additionally, this study provides a set of metabolites associated with gastrointestinal dysfunction in ASD, encompassing several amino acids, choline-derived compounds, bile acids, and sterol molecules. In terms of gene expression, genotype and gestational exposure to CPF had only minimal effects on decarboxylase 2 (gad2) and cholinergic receptor muscarinic 2 (chrm2) genes.

Temporal metabolomics state in pregnant rat: Analysis of amniotic fluid, placenta, and maternal plasma at embryonic and fetal time points

INTRODUCTION

During pregnancy, the dynamic metabolic demands for fetal growth require a continuous supply of essential metabolites. Understanding maternal metabolome changes during gestation is crucial for predicting disease risks in neonates.

METHODS

The study aimed to characterize the placental and amniotic fluid (AF) metabolomes during gestation in rats at gestational days GD-13 and 19 reflecting the end of the embryonic and fetal periods, respectively, and the maternal plasma, using metabolomics (LC-MS) and chemometrics. The objective was to highlight, through univariate and multivariate analyses, the complementarity of the data obtained from these different biological matrices.

RESULTS

The biological matrix had more impact on the metabolome composition than the gestational stage. The placental and AF metabolomes showed specific metabolome evolving over the two gestational stages. Analyzing the three targeted metabolomes revealed evolving pathways in arginine and proline metabolism/glutathione metabolism and phenylalanine metabolism; purine metabolism; and carbohydrate metabolism. Significantly, lipid metabolism in the placenta exhibited substantial changes with higher levels of certain phosphatidylethanolamine and sphingomyelins at GD19 while some cholesteryl esters and some glycosphingolipids levels being in higher levels at GD13.

DISCUSSION

These data highlight the metabolic gradients (mainly in placenta, also in AF, but only a few in plasma) observed through embryonic patterning and organ development during mid-to late gestation.

High-throughput omics technologies in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic, relapsing intestinal disease. Elucidation of the pathogenic mechanisms of IBD requires high-throughput technologies (HTTs) to effectively obtain and analyze large amounts of data. Recently, HTTs have been widely used in IBD, including genomics, transcriptomics, proteomics, microbiomics, metabolomics and single-cell sequencing. When combined with endoscopy, the application of these technologies can provide an in-depth understanding on the alterations of intestinal microbe diversity and abundance, the abnormalities of signaling pathway-mediated immune responses and functionality, and the evaluation of therapeutic effects, improving the accuracy of early diagnosis and treatment of IBD. This review comprehensively summarizes the development and advancement of HTTs, and also highlights the challenges and future directions of these technologies in IBD research. Although HTTs have made striking breakthrough in IBD, more standardized methods and large-scale dataset processing are still needed to achieve the goal of personalized medicine.

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.

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.

Urinary 1H NMR Metabolomic Analysis of Prenatal Maternal Stress Due to a Natural Disaster Reveals Metabolic Risk Factors for Non-Communicable Diseases: The QF2011 Queensland Flood Study

Prenatal stress alters fetal programming, potentially predisposing the ensuing offspring to long-term adverse health outcomes. To gain insight into environmental influences on fetal development, this QF2011 study evaluated the urinary metabolomes of 4-year-old children (n = 89) who were exposed to the 2011 Queensland flood in utero. Proton nuclear magnetic resonance spectroscopy was used to analyze urinary metabolic fingerprints based on maternal levels of objective hardship and subjective distress resulting from the natural disaster. In both males and females, differences were observed between high and low levels of maternal objective hardship and maternal subjective distress groups. Greater prenatal stress exposure was associated with alterations in metabolites associated with protein synthesis, energy metabolism, and carbohydrate metabolism. These alterations suggest profound changes in oxidative and antioxidative pathways that may indicate a higher risk for chronic non-communicable diseases such obesity, insulin resistance, and diabetes, as well as mental illnesses, including depression and schizophrenia. Thus, prenatal stress-associated metabolic biomarkers may provide early predictors of lifetime health trajectories, and potentially serve as prognostic markers for therapeutic strategies in mitigating adverse health outcomes.

Combining TNFR2-Expressing Tregs and IL-6 as Superior Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Masses

We hypothesised that the inclusion of immunosuppressive and inflammatory biomarkers in HGSOC patients would improve the sensitivity and specificity of the preoperative marker prediction of malignancy in patients with ovarian masses. We tested a panel of 29 soluble immune factors by multiplex bead immunoassay and 16 phenotypic T cell markers by flow cytometry in pre-treatment blood samples from 66 patients undergoing surgery for suspected ovarian cancer or ovarian cancer risk reduction. The potential diagnostic utility of all parameters was explored using Volcano plots, principal component analysis (PCA) and receiver operator characteristic (ROC) analysis. We also assessed the effect of culturing PBMCs from 20 healthy donors in the presence of malignant ascites fluid. The combination of TNFR2⁺ Tregs and IL-6 in the pre-treatment blood of patients with advanced HGSOC effectively discriminated patients with benign or malignant ovarian masses. In vitro culturing of the PBMCs of healthy donors in malignant ascites promoted an increase in TNFR2-expressing Tregs, which were decreased following blockade with IL-6 or STAT3 activity. Pre-treatment serum IL-6 and peripheral blood TNFR2⁺ Tregs may be potential clinical biomarkers that can discriminate patients with malignant compared to benign ovarian cancer masses, and the relationship between IL-6 and TNFR2⁺ Treg is likely to be mediated via the STAT3 signalling pathway.

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.

Novelty Detection for Metabolic Dynamics Established On Breast Cancer Tissue Using 2D NMR TOCSY Spectra

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Automatic novelty detection of metabolites of 2D-TOCSY NMR spectra.

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Metabolic profiling of the dynamics changes in Breast cancer tissue sample.

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Accurate and fast automatic multicomponent peak assignment of 2D NMR spectrum.

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One- and multi- novelty detection of metabolites.

Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research.

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.

Curcumin and NCLX inhibitors share anti-tumoral mechanisms in microsatellite-instability-driven colorectal cancer

BACKGROUND AND AIMS

Recent evidences highlight a role of the mitochondria calcium homeostasis in the development of colorectal cancer (CRC). To overcome treatment resistance, we aimed to evaluate the role of the mitochondrial sodium-calcium-lithium exchanger (NCLX) and its targeting in CRC. We also identified curcumin as a new inhibitor of NCLX.

METHODS

We examined whether curcumin and pharmacological compounds induced the inhibition of NCLX-mediated mitochondrial calcium (mtCa2+) extrusion, the role of redox metabolism in this process. We evaluated their anti-tumorigenic activity in vitro and in a xenograft mouse model. We analyzed NCLX expression and associations with survival in The Cancer Genome Atlas (TCGA) dataset and in tissue microarrays from 381 patients with microsatellite instability (MSI)-driven CRC.

RESULTS

In vitro, curcumin exerted strong anti-tumoral activity through its action on NCLX with mtCa2+ and reactive oxygen species overload associated with a mitochondrial membrane depolarization, leading to reduced ATP production and apoptosis. NCLX inhibition with pharmacological and molecular approaches reproduced the effects of curcumin. NCLX inhibitors decreased CRC tumor growth in vivo. Both transcriptomic analysis of TCGA dataset and immunohistochemical analysis of tissue microarrays demonstrated that higher NCLX expression was associated with MSI status, and for the first time, NCLX expression was significantly associated with recurrence-free survival.

CONCLUSIONS

Our findings highlight a novel anti-tumoral mechanism of curcumin through its action on NCLX and mitochondria calcium overload that could benefit for therapeutic schedule of patients with MSI CRC.

Senecavirus A- and Non-Infected Cells at Early Stage of Infection: Comparative Metabolomic Profiles

Senecavirus A (SVA), classified into the genus Senecavirus in the family Picornaviridae, causes an infectious disease in pigs. This virus can efficiently replicate in some non-pig-derived cells, such as the BHK cell line and its derivative (BSR-T7/5 cell line). We had recovered a wild-type SVA from its cDNA clone previously, and then uncovered the proteomic profile of SVA-infected BSR-T7/5 cells at 12 h post inoculation (hpi). In order to explore the cellular metabolomics further, the SVA-inoculated BSR-T7/5 cell monolayer was collected at 12 hpi for assay via liquid chromatography-tandem mass spectrometry (LC-MS/MS). The resultant data set was comprehensively analyzed using bioinformatics tools. A total of 451 metabolites were identified using in-house and public databases. Out of these metabolites, sixty-one showed significantly differential values (p value < 0.05). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to analyze metabolic pathways of the significantly differential metabolites. There were eighty-one identified KEGG pathways, out of which twenty-seven showed their p values < 0.05. The pyrimidine metabolism revealed the minimum p value and the maximum number of significantly differential metabolites, implying the pyrimidine played a key role in cellular metabolism after SVA infection. SVA replication must rely on the cellular metabolism. The present study on metabolomics would shed light on impacts of SVA-induced multiple interactions among metabolites on cells or even on natural hosts.

Alteration of the Intestinal Permeability Are Reflected by Changes in the Urine Metabolome of Young Autistic Children: Preliminary Results

Several metabolomics-based studies have provided evidence that autistic subjects might share metabolic abnormalities with gut microbiota dysbiosis and alterations in gut mucosal permeability. Our aims were to explore the most relevant metabolic perturbations in a group of autistic children, compared with their healthy siblings, and to investigate whether the increased intestinal permeability may be mirrored by specific metabolic perturbations. We enrolled 13 autistic children and 14 unaffected siblings aged 2–12 years; the evaluation of the intestinal permeability was estimated by the lactulose:mannitol test. The urine metabolome was investigated by proton nuclear magnetic resonance (1H-NMR) spectroscopy. The lactulose:mannitol test unveiled two autistic children with altered intestinal permeability. Nine metabolites significantly discriminated the urine metabolome of autistic children from that of their unaffected siblings; however, in the autistic children with increased permeability, four additional metabolites—namely, fucose, phenylacetylglycine, nicotinurate, and 1-methyl-nicotinamide, strongly discriminated their urine metabolome from that of the remaining autistic children. Our preliminary data suggest the presence of a specific urine metabolic profile associated with the increase in intestinal permeability.

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.

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.

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.

Maternal Rat Metabolomics: Amniotic Fluid and Placental Metabolic Profiling Workflows

Studying the metabolome of specific gestational compartments is of growing interest in the context of fetus developmental disorders. However, the metabolomes of the placenta and amniotic fluid (AF) are poorly characterized. Therefore, we present the validation of a fingerprinting methodology. Using pregnant rats, we performed exhaustive and robust extractions of metabolites in the AF and lipids and more polar metabolites in the placenta. For the AF, we compared the extraction capabilities of methanol (MeOH), acetonitrile (ACN), and a mixture of both. For the placenta, we compared (i) the extraction capabilities of dichloromethane, methyl t-butyl ether (MTBE), and butanol, along with (ii) the impact of lyophilization of the placental tissue. Analyses were performed on a C18 and hydrophilic interaction liquid chromatography combined with high-resolution mass spectrometry. The efficiency and the robustness of the extractions were compared based on the number of the features or metabolites (for untargeted or targeted approach, respectively), their mean total intensity, and their coefficient of variation (% CV). The extraction capabilities of MeOH and ACN on the AF metabolome were equivalent. Lyophilization also had no significant impact and usefulness on the placental tissue metabolome profiling. Considering the placental lipidome, MTBE extraction was more informative because it allowed extraction of a slightly higher number of lipids, in higher concentration. This proof-of-concept study assessing the metabolomics and lipidomics of the AF and the placenta revealed changes in both metabolisms, at two different stages of rat gestation, and allowed a detailed prenatal metabolic fingerprinting.

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.

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.

Comparison of the Metabolic Profiles in the Plasma and Urine Samples Between Autistic and Typically Developing Boys: A Preliminary Study

Background: Autism spectrum disorder (ASD) is defined as a pervasive developmental disorder which is caused by genetic and environmental risk factors. Besides the core behavioral symptoms, accumulated results indicate children with ASD also share some metabolic abnormalities. Objectives: To analyze the comprehensive metabolic profiles in both of the first-morning urine and plasma samples collected from the same cohort of autistic boys. Methods: In this study, 30 autistic boys and 30 tightly matched healthy control (HC) boys (age range: 2.4~6.7 years) were recruited. First-morning urine and plasma samples were collected and the liquid chromatography-mass spectrometry (LC-MS) was applied to obtain the untargeted metabolic profiles. The acquired data were processed by multivariate analysis and the screened metabolites were grouped by metabolic pathway. Results: Different discriminating metabolites were found in plasma and urine samples. Notably, taurine and catechol levels were decreased in urine but increased in plasma in the same cohort of ASD children. Enriched pathway analysis revealed that perturbations in taurine and hypotaurine metabolism, phenylalanine metabolism, and arginine and proline metabolism could be found in both of the plasma and urine samples. Conclusion: These preliminary results suggest that a series of common metabolic perturbations exist in children with ASD, and confirmed the importance to have a comprehensive analysis of the metabolites in different biological samples to reveal the full picture of the complex metabolic patterns associated with ASD. Further targeted analyses are needed to validate these results in a larger cohort.

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.

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.

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics

There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.

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.

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.

Metabolic profiling in children with autism spectrum disorder with and without mental regression: preliminary results from a cross-sectional case-control study

INTRODUCTION

It is challenging to establish the mechanisms involved in the variety of well-defined clinical phenotypes in autism spectrum disorder (ASD) and the pathways involved in their pathogeneses.

OBJECTIVES

The aim of the present study was to evaluate the metabolomic profiles of children with ASD subclassified by mental regression (AR) phenotype and with no regression (ANR).

METHODS

The present study was a cross-sectional case-control study. Thirty children aged 2-6 years with ASD were included: 15 with ANR and 15 with AR. In addition, a control group of 30 normally developing children was selected and matched to the ASD group by sex and age. Plasma samples were analyzed with a metabolomics single platform methodology based on liquid chromatography-mass spectrometry. Univariate and multivariate analysis, including orthogonal partial least squares-discriminant analysis modeling and Shared-and-Unique-Structures plots, were performed using MetaboAnalyst 4.0 and SIMCA-P 15. The primary endpoint was the metabolic signature profiling among healthy children and autistic children and their subgroups.

RESULTS

Metabolomic profiles of 30 healthy children, 15 ANR and 15 AR were compared. Several differences between healthy children and children with ASD were detected, involving mainly amino acid, lipid and nicotinamide metabolism. Furthermore, we report subtle differences between the ANR and AR groups.

CONCLUSIONS

In this study, we report, for the first time, the plasmatic metabolomic profiles of children with ASD, including two different phenotypes based on mental regression status. The use of a liquid chromatography-mass spectrometry platform approach for metabolomics in ASD children using plasma appears to be very efficient and adds further support to previous findings in urine. Furthermore, the present study documents several changes related to amino acid, NAD⁺ and lipid metabolism that, in some cases, such as arginine and glutamate pathway alterations, seem to be associated with the AR phenotype. Further targeted analyses are needed in a larger cohort to validate the results presented herein.

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.

The phenolic interactome and gut microbiota: opportunities and challenges in developing applications for schizophrenia and autism

Schizophrenia and autism spectrum disorder have long been associated with elevated levels of various small phenolic molecules (SPMs). In turn, the gut microbiota (GMB) has been implicated in the kinetics of many of these analytes. Unfortunately, research into the possible relevance of GMB-mediated SPMs to neuropsychiatry continues to be limited by heterogeneous study design, numerous sources of variance and technical challenges. Some SPMs have multiple structural isomers and most have conjugates. Without specialized approaches, SPMs can be incorrectly assigned or inaccurately quantified. In addition, SPM levels can be affected by dietary polyphenol or protein consumption and by various medications and diseases. Nonetheless, heterotypical excretion of various SPMs in association with schizophrenia or autism continues to be reported in independent samples. Recent studies in human cerebrospinal fluid demonstrate the presence of many SPMs A large number of these are bioactive in experimental models. Whether such mechanisms are relevant to the human brain in health or disease is not known. Systematic metabolomic and microbiome studies of well-characterized populations, an appreciation of multiple confounds, and implementation of standardized approaches across platforms and sites are needed to delineate the potential utility of the phenolic interactome in neuropsychiatry.

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.

Proteomics and Metabolomics for AKI Diagnosis

Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.

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.

Is Taurine a Biomarker in Autistic Spectrum Disorder?

Taurine is a sulfur-containing amino acid which is not incorporated into protein. However, taurine has various critical physiological functions including development of the eye and brain, reproduction, osmoregulation, and immune functions including anti-inflammatory as well as anti-oxidant activity. The causes of autistic spectrum disorder (ASD) are not clear but a high heritability implicates an important role for genetic factors. Reports also implicate oxidative stress and inflammation in the etiology of ASD. Thus, taurine, a well-known antioxidant and regulator of inflammation, was investigated here using the sera from both girls and boys with ASD as well as their siblings and parents. Previous reports regarding taurine serum concentrations in ASD from various laboratories have been controversial. To address the potential role of taurine in ASD, we collected sera from 66 children with ASD (males: 45; females: 21, age 1.5-11.5 years, average age 5.2 ± 1.6) as well as their unaffected siblings (brothers: 24; sisters: 32, age 1.5-17 years, average age 7.0 ± 2.0) as controls of the children with ASD along with parents (fathers: 49; mothers: 54, age 28-45 years). The sera from normal adult controls (males: 47; females: 51, age 28-48 years) were used as controls for the parents. Taurine concentrations in all sera samples were measured using high performance liquid chromatography (HPLC) using a phenylisothiocyanate labeling technique. Taurine concentrations from female and male children with ASD were 123.8 ± 15.2 and 145.8 ± 8.1 μM, respectively, and those from their unaffected brothers and sisters were 142.6 ± 10.4 and 150.8 ± 8.4 μM, respectively. There was no significant difference in taurine concentration between autistic children and their unaffected siblings. Taurine concentrations in children with ASD were also not significantly different from their parents (mothers: 139.6 ± 7.7 μM, fathers: 147.4 ± 7.5 μM). No significant difference was observed between adult controls and parents of ASD children (control females: 164.8 ± 4.8 μM, control males: 163.0 ± 7.0 μM). However, 21 out of 66 children with ASD had low taurine concentrations (<106 μM). Since taurine has anti-oxidant activity, children with ASD with low taurine concentrations will be examined for abnormal mitochondrial function. Our data imply that taurine may be a valid biomarker in a subgroup of ASD.

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.

Post hoc analysis of plasma amino acid profiles: towards a specific pattern in autism spectrum disorder and intellectual disability

Objectives Autism spectrum disorders and intellectual disability present a challenge for therapeutic and dietary management. We performed a re-analysis of plasma amino acid chromatography of children with autism spectrum disorders ( n = 22) or intellectual disability ( n = 29) to search for a metabolic signature that can distinguish individuals with these disorders from controls ( n = 30). Methods We performed univariate and multivariate analyses using different machine learning strategies, from the raw data of the amino acid chromatography. Finally, we analysed the metabolic pathways associated with discriminant biomarkers. Results Multivariate analysis revealed models to discriminate patients with autism spectrum disorders or intellectual disability and controls from plasma amino acid profiles ( P < 0.0003). Univariate analysis showed that autism spectrum disorder and intellectual disability patients shared similar differences relative to controls, including lower glutamate ( P < 0.0001 and P = 0.0002, respectively) and serine ( P = 0.002 for both) concentrations. The multivariate model ( P < 6.12.10^-7) to discriminate between autism spectrum disorders and intellectual disability revealed the involvement of urea, 3-methyl-histidine and histidine metabolism. Biosigner analysis and univariate analysis confirmed the role of 3-methylhistidine ( P = 0.004), histidine ( P = 0.003), urea ( P = 0.0006) and lysine ( P = 0.002). Conclusions We revealed discriminant metabolic patterns between autism spectrum disorders, intellectual disability and controls. Amino acids known to play a role in neurotransmission were discriminant in the models comparing autism spectrum disorders or intellectual disability to controls, and histidine and b-alanine metabolism was specifically highlighted in the model.

The Metabolic Disturbances of Motoneurons Exposed to Glutamate

Glutamate-induced excitotoxicity is considered as one of the major pathophysiological factors of motoneuron death in amyotrophic lateral sclerosis and other motoneuron diseases. In order to expand our knowledge on mechanisms of glutamate-induced excitotoxicity, the present study proposes to determine the metabolic consequences of glutamate and astrocytes in primary enriched motoneuron culture. Using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS), we showed that the presence of astrocytes and glutamate profoundly modified the metabolic profile of motoneurons. Our study highlights for the first time that crosstalk between astrocytes and enriched motoneuron culture induced alterations in phenylalanine, tryptophan, purine, arginine, proline, aspartate, and glutamate metabolism in motoneurons. We observed that astrocytes modulate the sensitivity of motoneurons to glutamate, since metabolites altered by glutamate in motoneurons cultured alone were different (except 5-hydroxylysine) from those altered in co-cultured motoneurons. Our findings provide new insight into the metabolic alterations associated to astrocytes and glutamate in motoneurons and provide opportunities to identify novel therapeutic targets.

Targeting aggression in severe mental illness: The predictive role of genetic, epigenetic, and metabolomic markers

Human aggression is a complex and widespread social behavior that is overrepresented in individuals affected by severe mental illness (SMI), such as schizophrenia (SCZ), bipolar disorder (BD), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD). A substantial proportion of the liability threshold for aggressive behavior is determined by genetic factors, and environmental moderators might precipitate the manifestation of this behavioral phenotype through modification of gene expression via the epigenetic machinery. These specific alterations in the genetic and epigenetic make-up of aggressive individuals might determine distinct biochemical signatures detectable through metabolomics. An additional pathophysiological component playing a role in aggressive behavior might be determined by alterations of gut microbiota. Here, we present a selective review of human data on genetic, epigenetic, and metabolomic markers of aggressive behavior in SMI, discussing also the available evidence on the role of microbiome alterations. Clinical implication of these evidences, as well as future perspectives, will be discussed.

Wildtype motoneurons, ALS-Linked SOD1 mutation and glutamate profoundly modify astrocyte metabolism and lactate shuttling

The selective degeneration of motoneuron that typifies amyotrophic lateral sclerosis (ALS) implicates non-cell-autonomous effects of astrocytes. However, mechanisms underlying astrocyte-mediated neurotoxicity remain largely unknown. According to the determinant role of astrocyte metabolism in supporting neuronal function, we propose to explore the metabolic status of astrocytes exposed to ALS-associated conditions. We found a significant metabolic dysregulation including purine, pyrimidine, lysine, and glycerophospholipid metabolism pathways in astrocytes expressing an ALS-causing mutated superoxide dismutase-1 (SOD1) when co-cultured with motoneurons. SOD1 astrocytes exposed to glutamate revealed a significant modification of the astrocyte metabolic fingerprint. More importantly, we observed that SOD1 mutation and glutamate impact the cellular shuttling of lactate between astrocytes and motoneurons with a decreased in extra- and intra-cellular lactate levels in astrocytes. Based on the emergent strategy of metabolomics, this work provides novel insight for understanding metabolic dysfunction of astrocytes in ALS conditions and opens the perspective of therapeutics targets through focusing on these metabolic pathways. GLIA 2017 GLIA 2017;65:592-605.

Urinary metabolomics of young Italian autistic children supports abnormal tryptophan and purine metabolism

Background

Autism spectrum disorder (ASD) is still diagnosed through behavioral observation, due to a lack of laboratory biomarkers, which could greatly aid clinicians in providing earlier and more reliable diagnoses. Metabolomics on human biofluids provides a sensitive tool to identify metabolite profiles potentially usable as biomarkers for ASD. Initial metabolomic studies, analyzing urines and plasma of ASD and control individuals, suggested that autistic patients may share some metabolic abnormalities, despite several inconsistencies stemming from differences in technology, ethnicity, age range, and definition of “control” status.

Methods

ASD-specific urinary metabolomic patterns were explored at an early age in 30 ASD children and 30 matched controls (age range 2–7, M:F = 22:8) using hydrophilic interaction chromatography (HILIC)-UHPLC and mass spectrometry, a highly sensitive, accurate, and unbiased approach. Metabolites were then subjected to multivariate statistical analysis and grouped by metabolic pathway.

Results

Urinary metabolites displaying the largest differences between young ASD and control children belonged to the tryptophan and purine metabolic pathways. Also, vitamin B₆, riboflavin, phenylalanine-tyrosine-tryptophan biosynthesis, pantothenate and CoA, and pyrimidine metabolism differed significantly. ASD children preferentially transform tryptophan into xanthurenic acid and quinolinic acid (two catabolites of the kynurenine pathway), at the expense of kynurenic acid and especially of melatonin. Also, the gut microbiome contributes to altered tryptophan metabolism, yielding increased levels of indolyl 3-acetic acid and indolyl lactate.

Conclusions

The metabolic pathways most distinctive of young Italian autistic children largely overlap with those found in rodent models of ASD following maternal immune activation or genetic manipulations. These results are consistent with the proposal of a purine-driven cell danger response, accompanied by overproduction of epileptogenic and excitotoxic quinolinic acid, large reductions in melatonin synthesis, and gut dysbiosis. These metabolic abnormalities could underlie several comorbidities frequently associated to ASD, such as seizures, sleep disorders, and gastrointestinal symptoms, and could contribute to autism severity. Their diagnostic sensitivity, disease-specificity, and interethnic variability will merit further investigation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-016-0109-5) contains supplementary material, which is available to authorized users.

The Lipopolysaccharide-Induced Metabolome Signature in Arabidopsis thaliana Reveals Dynamic Reprogramming of Phytoalexin and Phytoanticipin Pathways

Lipopolysaccharides (LPSs), as MAMP molecules, trigger the activation of signal transduction pathways involved in defence. Currently, plant metabolomics is providing new dimensions into understanding the intracellular adaptive responses to external stimuli. The effect of LPS on the metabolomes of Arabidopsis thaliana cells and leaf tissue was investigated over a 24 h period. Cellular metabolites and those secreted into the medium were extracted with methanol and liquid chromatography coupled to mass spectrometry was used for quantitative and qualitative analyses. Multivariate statistical data analyses were used to extract interpretable information from the generated multidimensional LC-MS data. The results show that LPS perception triggered differential changes in the metabolomes of cells and leaves, leading to variation in the biosynthesis of specialised secondary metabolites. Time-dependent changes in metabolite profiles were observed and biomarkers associated with the LPS-induced response were tentatively identified. These include the phytohormones salicylic acid and jasmonic acid, and also the associated methyl esters and sugar conjugates. The induced defensive state resulted in increases in indole-and other glucosinolates, indole derivatives, camalexin as well as cinnamic acid derivatives and other phenylpropanoids. These annotated metabolites indicate dynamic reprogramming of metabolic pathways that are functionally related towards creating an enhanced defensive capacity. The results reveal new insights into the mode of action of LPS as an activator of plant innate immunity, broadens knowledge about the defence metabolite pathways involved in Arabidopsis responses to LPS, and identifies specialised metabolites of functional importance that can be employed to enhance immunity against pathogen infection.