A Metabolomics Approach to Screening for Autism Risk in the Children's Autism Metabolome Project

Disrupted Prenatal Metabolism May Explain the Etiology of Suboptimal Neurodevelopment: A Focus on Phthalates and Micronutrients and their Relationship to Autism Spectrum Disorder

Pregnancy is a time of high metabolic coordination, as maternal metabolism adapts to support the growing fetus. Many of these changes are coordinated by the placenta, a critical fetal endocrine organ and the site of maternal-fetal crosstalk. Dysregulation in maternal and placental metabolism during pregnancy has been linked to adverse outcomes, including altered neurodevelopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder linked to metabolic alterations in both children and their mothers. Prenatal environmental exposures have been linked to risk of ASD through dysregulated maternal, placental, and fetal metabolism. In this review, we focus on recent studies investigating the associations between prenatal metabolism in the maternal-placental-fetal unit and the impact of prenatal environmental exposures to phthalates and micronutrients on ASD risk. By identifying the mechanisms through which phthalates and other ubiquitous endocrine disrupting chemicals influence development, and how nutritional interventions can impact those mechanisms, we can identify promising ways to prevent suboptimal neurodevelopment.

Biomarkers of mitochondrial dysfunction in autism spectrum disorder: A systematic review and meta-analysis

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting 1 in 36 children and is associated with physiological abnormalities, most notably mitochondrial dysfunction, at least in a subset of individuals. This systematic review and meta-analysis discovered 204 relevant articles which evaluated biomarkers of mitochondrial dysfunction in ASD individuals. Significant elevations (all p < 0.01) in the prevalence of lactate (17%), pyruvate (41%), alanine (15%) and creatine kinase (9%) were found in ASD. Individuals with ASD had significant differences (all p < 0.01) with moderate to large effect sizes (Cohen's d' ≥ 0.6) compared to controls in mean pyruvate, lactate-to-pyruvate ratio, ATP, and creatine kinase. Some studies found abnormal TCA cycle metabolites associated with ASD. Thirteen controlled studies reported mitochondrial DNA (mtDNA) deletions or variations in the ASD group in blood, peripheral blood mononuclear cells, lymphocytes, leucocytes, granulocytes, and brain. Meta-analyses discovered significant differences (p < 0.01) in copy number of mtDNA overall and in ND1, ND4 and CytB genes. Four studies linked specific mtDNA haplogroups to ASD. A series of studies found a subgroup of ASD with elevated mitochondrial respiration which was associated with increased sensitivity of the mitochondria to physiological stressors and neurodevelopmental regression. Lactate, pyruvate, lactate-to-pyruvate ratio, carnitine, and acyl-carnitines were associated with clinical features such as delays in language, social interaction, cognition, motor skills, and with repetitive behaviors and gastrointestinal symptoms, although not all studies found an association. Lactate, carnitine, acyl-carnitines, ATP, CoQ10, as well as mtDNA variants, heteroplasmy, haplogroups and copy number were associated with ASD severity. Variability was found across biomarker studies primarily due to differences in collection and processing techniques as well as the intrinsic heterogeneity of the ASD population. Several studies reported alterations in mitochondrial metabolism in mothers of children with ASD and in neonates who develop ASD. Treatments targeting mitochondria, particularly carnitine and ubiquinol, appear beneficial in ASD. The link between mitochondrial dysfunction in ASD and common physiological abnormalities in individuals with ASD including gastrointestinal disorders, oxidative stress, and immune dysfunction is outlined. Several subtypes of mitochondrial dysfunction in ASD are discussed, including one related to neurodevelopmental regression, another related to alterations in microbiome metabolites, and another related to elevations in acyl-carnitines. Mechanisms linking abnormal mitochondrial function with alterations in prenatal brain development and postnatal brain function are outlined. Given the multisystem complexity of some individuals with ASD, this review presents evidence for the mitochondria being central to ASD by contributing to abnormalities in brain development, cognition, and comorbidities such as immune and gastrointestinal dysfunction as well as neurodevelopmental regression. A diagnostic approach to identify mitochondrial dysfunction in ASD is outlined. From this evidence, it is clear that many individuals with ASD have alterations in mitochondrial function which may need to be addressed in order to achieve optimal clinical outcomes. The fact that alterations in mitochondrial metabolism may be found during pregnancy and early in the life of individuals who eventually develop ASD provides promise for early life predictive biomarkers of ASD. Further studies may improve the understanding of the role of the mitochondria in ASD by better defining subgroups and understanding the molecular mechanisms driving some of the unique changes found in mitochondrial function in those with ASD.

Metabolomic changes in children with autism

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication and repetitive behaviors. Metabolomic profiling has emerged as a valuable tool for understanding the underlying metabolic dysregulations associated with ASD.

AIM

To comprehensively explore metabolomic changes in children with ASD, integrating findings from various research articles, reviews, systematic reviews, meta-analyses, case reports, editorials, and a book chapter.

METHODS

A systematic search was conducted in electronic databases, including PubMed, PubMed Central, Cochrane Library, Embase, Web of Science, CINAHL, Scopus, LISA, and NLM catalog up until January 2024. Inclusion criteria encompassed research articles (83), review articles (145), meta-analyses (6), systematic reviews (6), case reports (2), editorials (2), and a book chapter (1) related to metabolomic changes in children with ASD. Exclusion criteria were applied to ensure the relevance and quality of included studies.

RESULTS

The systematic review identified specific metabolites and metabolic pathways showing consistent differences in children with ASD compared to typically developing individuals. These metabolic biomarkers may serve as objective measures to support clinical assessments, improve diagnostic accuracy, and inform personalized treatment approaches. Metabolomic profiling also offers insights into the metabolic alterations associated with comorbid conditions commonly observed in individuals with ASD.

CONCLUSION

Integration of metabolomic changes in children with ASD holds promise for enhancing diagnostic accuracy, guiding personalized treatment approaches, monitoring treatment response, and improving outcomes. Further research is needed to validate findings, establish standardized protocols, and overcome technical challenges in metabolomic analysis. By advancing our understanding of metabolic dysregulations in ASD, clinicians can improve the lives of affected individuals and their families.

Metabolic network analysis of pre-ASD newborns and 5-year-old children with autism spectrum disorder

Classical metabolomic and new metabolic network methods were used to study the developmental features of autism spectrum disorder (ASD) in newborns (n = 205) and 5-year-old children (n = 53). Eighty percent of the metabolic impact in ASD was caused by 14 shared biochemical pathways that led to decreased anti-inflammatory and antioxidant defenses, and to increased physiologic stress molecules like lactate, glycerol, cholesterol, and ceramides. CIRCOS plots and a new metabolic network parameter,V ° net, revealed differences in both the kind and degree of network connectivity. Of 50 biochemical pathways and 450 polar and lipid metabolites examined, the developmental regulation of the purine network was most changed. Purine network hub analysis revealed a 17-fold reversal in typically developing children. This purine network reversal did not occur in ASD. These results revealed previously unknown metabolic phenotypes, identified new developmental states of the metabolic correlation network, and underscored the role of mitochondrial functional changes, purine metabolism, and purinergic signaling in autism spectrum disorder.

Etiologic Evaluation of Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is clinically and etiologically heterogeneous. A causal genetic variant can be identified in approximately 20% to 25% of affected individuals with current clinical genetic testing, and all patients with an ASD diagnosis should be offered genetic etiologic evaluation. We suggest that exome sequencing with copy number variant coverage should be the first-line etiologic evaluation for ASD. Neuroimaging, neurophysiologic, metabolic, and other biochemical evaluations can provide insight into the pathophysiology of ASD but should be recommended in the appropriate clinical circumstances.

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.

Effects of Soy Protein Isolate on Fragile X Phenotypes in Mice

Obesity is a pediatric epidemic that is more prevalent in children with developmental disabilities. We hypothesize that soy protein-based diets increase weight gain and alter neurobehavioral outcomes. Our objective herein was to test matched casein- and soy protein-based purified ingredient diets in a mouse model of fragile X syndrome, Fmr1KO mice. The experimental methods included assessment of growth; 24-7 activity levels; motor coordination; learning and memory; blood-based amino acid, phytoestrogen and glucose levels; and organ weights. The primary outcome measure was body weight. We find increased body weight in male Fmr1KO from postnatal day 6 (P6) to P224, male wild type (WT) from P32-P39, female Fmr1KO from P6-P18 and P168-P224, and female Fmr1HET from P9-P18 as a function of soy. Activity at the beginning of the light and dark cycles increased in female Fmr1HET and Fmr1KO mice fed soy. We did not find significant differences in rotarod or passive avoidance behavior as a function of genotype or diet. Several blood-based amino acids and phytoestrogens were significantly altered in response to soy. Liver weight was increased in WT and adipose tissue in Fmr1KO mice fed soy. Activity levels at the beginning of the light cycle and testes weight were greater in Fmr1KO versus WT males irrespective of diet. DEXA analysis at 8-months-old indicated increased fat mass and total body area in Fmr1KO females and lean mass and bone mineral density in Fmr1KO males fed soy. Overall, dietary consumption of soy protein isolate by C57BL/6J mice caused increased growth, which could be attributed to increased lean mass in males and fat mass in females. There were sex-specific differences with more pronounced effects in Fmr1KO versus WT and in males versus females.

Cord Blood Metabolite Profiles and Their Association with Autistic Traits in Childhood

Autism Spectrum Disorder (ASD) is a diverse neurodevelopmental condition. Gene-environmental interactions in early stages of life might alter metabolic pathways, possibly contributing to ASD pathophysiology. Metabolomics may serve as a tool to identify underlying metabolic mechanisms contributing to ASD phenotype and could help to unravel its complex etiology. In a population-based, prospective cohort study among 783 mother-child pairs, cord blood serum concentrations of amino acids, non-esterified fatty acids, phospholipids, and carnitines were obtained using liquid chromatography coupled with tandem mass spectrometry. Autistic traits were measured at the children's ages of 6 (n = 716) and 13 (n = 648) years using the parent-reported Social Responsiveness Scale. Lower cord blood concentrations of SM.C.39.2 and NEFA16:1/16:0 were associated with higher autistic traits among 6-year-old children, adjusted for sex and age at outcome. After more stringent adjustment for confounders, no significant associations of cord blood metabolites and autistic traits at ages 6 and 13 were detected. Differences in lipid metabolism (SM and NEFA) might be involved in ASD-related pathways and are worth further investigation.

Effects of Walnut and Pumpkin on Selective Neurophenotypes of Autism Spectrum Disorders: A Case Study

Special diets or nutritional supplements are regularly given to treat children with autism spectrum disorder (ASD). The increased consumption of particular foods has been demonstrated in numerous trials to lessen autism-related symptoms and comorbidities. A case study on a boy with moderate autism who significantly improved after three years of following a healthy diet consisting of pumpkin and walnuts was examined in this review in connection to a few different neurophenotypes of ASD. We are able to suggest that a diet high in pumpkin and walnuts was useful in improving the clinical presentation of the ASD case evaluated by reducing oxidative stress, neuroinflammation, glutamate excitotoxicity, mitochondrial dysfunction, and altered gut microbiota, all of which are etiological variables. Using illustrated figures, a full description of the ways by which a diet high in pumpkin and nuts could assist the included case is offered.

Metabolomic biomarkers in autism: identification of complex dysregulations of cellular bioenergetics

Autism Spectrum Disorder (ASD or autism) is a phenotypically and etiologically heterogeneous condition. Identifying biomarkers of clinically significant metabolic subtypes of autism could improve understanding of its underlying pathophysiology and potentially lead to more targeted interventions. We hypothesized that the application of metabolite-based biomarker techniques using decision thresholds derived from quantitative measurements could identify autism-associated subpopulations. Metabolomic profiling was carried out in a case-control study of 499 autistic and 209 typically developing (TYP) children, ages 18-48 months, enrolled in the Children's Autism Metabolome Project (CAMP; ClinicalTrials.gov Identifier: NCT02548442). Fifty-four metabolites, associated with amino acid, organic acid, acylcarnitine and purine metabolism as well as microbiome-associated metabolites, were quantified using liquid chromatography-tandem mass spectrometry. Using quantitative thresholds, the concentrations of 4 metabolites and 149 ratios of metabolites were identified as biomarkers, each identifying subpopulations of 4.5-11% of the CAMP autistic population. A subset of 42 biomarkers could identify CAMP autistic individuals with 72% sensitivity and 90% specificity. Many participants were identified by several metabolic biomarkers. Using hierarchical clustering, 30 clusters of biomarkers were created based on participants' biomarker profiles. Metabolic changes associated with the clusters suggest that altered regulation of cellular metabolism, especially of mitochondrial bioenergetics, were common metabolic phenotypes in this cohort of autistic participants. Autism severity and cognitive and developmental impairment were associated with increased lactate, many lactate containing ratios, and the number of biomarker clusters a participant displayed. These studies provide evidence that metabolic phenotyping is feasible and that defined autistic subgroups can lead to enhanced understanding of the underlying pathophysiology and potentially suggest pathways for targeted metabolic treatments.

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.

Phenotypically driven subgroups of ASD display distinct metabolomic profiles

Autism Spectrum Disorder (ASD) is a heterogeneous condition that includes a broad range of characteristics and associated comorbidities; however, the biology underlying the variability in phenotypes is not well understood. As ASD impacts approximately 1 in 100 children globally, there is an urgent need to better understand the biological mechanisms that contribute to features of ASD. In this study, we leveraged rich phenotypic and diagnostic information related to ASD in 2001 individuals aged 4 to 17 years from the Simons Simplex Collection to derive phenotypically driven subgroups and investigate their respective metabolomes. We performed hierarchical clustering on 40 phenotypes spanning four ASD clinical domains, resulting in three subgroups with distinct phenotype patterns. Using global plasma metabolomic profiling generated by ultrahigh-performance liquid chromatography mass spectrometry, we characterized the metabolome of individuals in each subgroup to interrogate underlying biology related to the subgroups. Subgroup 1 included children with the least maladaptive behavioral traits (N = 862); global decreases in lipid metabolites and concomitant increases in amino acid and nucleotide pathways were observed for children in this subgroup. Subgroup 2 included children with the highest degree of challenges across all phenotype domains (N = 631), and their metabolome profiles demonstrated aberrant metabolism of membrane lipids and increases in lipid oxidation products. Subgroup 3 included children with maladaptive behaviors and co-occurring conditions that showed the highest IQ scores (N = 508); these individuals had increases in sphingolipid metabolites and fatty acid byproducts. Overall, these findings indicated distinct metabolic patterns within ASD subgroups, which may reflect the biological mechanisms giving rise to specific patterns of ASD characteristics. Our results may have important clinical applications relevant to personalized medicine approaches towards managing ASD symptoms.

The Crosstalk between Gut Microbiota and Nervous System: A Bidirectional Interaction between Microorganisms and Metabolome

Several studies have shown that the gut microbiota influences behavior and, in turn, changes in the immune system associated with symptoms of depression or anxiety disorder may be mirrored by corresponding changes in the gut microbiota. Although the composition/function of the intestinal microbiota appears to affect the central nervous system (CNS) activities through multiple mechanisms, accurate epidemiological evidence that clearly explains the connection between the CNS pathology and the intestinal dysbiosis is not yet available. The enteric nervous system (ENS) is a separate branch of the autonomic nervous system (ANS) and the largest part of the peripheral nervous system (PNS). It is composed of a vast and complex network of neurons which communicate via several neuromodulators and neurotransmitters, like those found in the CNS. Interestingly, despite its tight connections to both the PNS and ANS, the ENS is also capable of some independent activities. This concept, together with the suggested role played by intestinal microorganisms and the metabolome in the onset and progression of CNS neurological (neurodegenerative, autoimmune) and psychopathological (depression, anxiety disorders, autism) diseases, explains the large number of investigations exploring the functional role and the physiopathological implications of the gut microbiota/brain axis.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Translating precision medicine for autism spectrum disorder: A pressing need

Autism spectrum disorder (ASD) is a heterogenous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. Currently, ASD is diagnosed according to behavior-based criteria that overlook clinical and genomic heterogeneity, thus repeatedly resulting in failed clinical trials. Here, we summarize the scientific evidence pointing to the pressing need to create a precision medicine framework for ASD and other NDDs. We discuss the role of omics and systems biology to characterize more homogeneous disease subtypes with different underlying pathophysiological mechanisms and to determine corresponding tailored treatments. Finally, we provide recent initiatives towards tackling the complexity in NDDs for precision medicine and cost-effective drug discovery.

Neuroactive Amino Acid Profile in Autism Spectrum Disorder: Results from a Clinical Sample

Biological bases of autism spectrum disorder (ASD) include both genetic and epigenetic causes. Patients with ASD show anomalies in the profile of certain plasma amino acids, including neuroactive amino acids. Monitoring plasma amino acids may be relevant for patient care and interventions. We evaluated the plasma amino acid profile in samples extracted from dry blood spots by electrospray ionization-tandem mass spectrometry. Fourteen amino acids and eleven amino acid ratios were examined in patients with ASD and intellectual disability (ID), and neurotypical control subjects (TD). The amino acid profile in the ASD group showed reduced levels of ornithine (p = 0.008), phenylalanine (p = 0.042) and tyrosine (p = 0.013). The statistically significant amino acid ratios were Leu+Val/Phe+Tyr (p = 0.002), Tyr/Leu (p = 0.007) and Val/Phe (p = 0.028), such differences remaining significant only in the comparison between ASD and TD. Finally, a positive correlation emerged between the score of the restricted and repetitive behavior on ADOS-2 and the citrulline levels in the ASD group (p = 0.0047). To conclude, patients with ASD may show a distinguishable metabolic profile useful for studying their metabolic pathways in order to develop screening tests and targeted therapies.

Improving Reproducibility to Enhance Scientific Rigor through Consideration of Mouse Diet

Animal husbandry conditions, including rodent diet, constitute an example highlighting the importance of reporting experimental variables to enhance scientific rigor. In the present study, we examine the effects of three common rodent diets including two chows (Purina 5015 and Teklad 2019) and one purified ingredient diet (AIN-76A) on growth anthropometrics (body weight), behavior (nest building, actigraphy, passive avoidance) and blood biomarkers (ketones, glucose, amino acid profiles) in male and female C57BL/6J mice. We find increased body weight in response to the chows compared to purified ingredient diet albeit selectively in male mice. We did not find significantly altered behavior in female or male wild type C57BL/6J mice. However, amino acid profiles changed as an effect of sex and diet. These data contribute to a growing body of knowledge indicating that rodent diet impacts experimental outcomes and needs to be considered in study design and reporting.

Changes of serum metabolites levels during neoadjuvant chemoradiation and prediction of the pathological response in locally advanced rectal cancer

INTRODUCTION

Previous studies have explored prediction value of serum metabolites in neoadjuvant chemoradiation therapy (NCRT) response for rectal cancer. To date, limited literature is available for serum metabolome changes dynamically through NCRT.

OBJECTIVES

This study aimed to explore temporal change pattern of serum metabolites during NCRT, and potential metabolic biomarkers to predict the pathological response to NCRT in locally advanced rectal cancer (LARC) patients.

METHODS

Based on dynamic UHPLC-QTOF-MS untargeted metabolomics design, this study included 106 LARC patients treated with NCRT. Biological samples of the enrolled patients were collected in five consecutive time-points. Untargeted metabolomics was used to profile serum metabolic signatures from LARC patients. Then, we used fuzzy C-means clustering (FCM) to explore temporal change patterns in metabolites cluster and identify monotonously changing metabolites during NCRT. Repeated measure analysis of variance (RM-ANOVA) and multilevel partial least-squares discriminant analysis (ML-PLS-DA) were performed to select metabolic biomarkers. Finally, a panel of dynamic differential metabolites was used to build logistic regression prediction models.

RESULTS

Metabolite profiles showed a clearly tendency of separation between different follow-up panels. We identified two clusters of 155 serum metabolites with monotonously changing patterns during NCRT (74 decreased metabolites and 81 increased metabolites). Using RM-ANOVA and ML-PLS-DA, 8 metabolites (L-Norleucine, Betaine, Hypoxanthine, Acetylcholine, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, Glycerophosphocholine, Alpha-ketoisovaleric acid, N-Acetyl-L-alanine) were further identified as dynamic differential biomarkers for predicting NCRT sensitivity. The area under the ROC curve (AUC) of prediction model combined with the baseline measurement was 0.54 (95%CI = 0.43 ~ 0.65). By incorporating the variability indexes of 8 dynamic differential metabolites, the prediction model showed better discrimination performance than baseline measurement, with AUC = 0.67 (95%CI 0.57 ~ 0.77), 0.64 (0.53 ~ 0.75), 0.60 (0.50 ~ 0.71), and 0.56 (0.45 ~ 0.67) for the variability index of difference, linear slope, ratio, and standard deviation, respectively.

CONCLUSION

This study identified eight metabolites as dynamic differential biomarkers to discriminate NCRT-sensitive and resistant patients. The changes of metabolite level during NCRT show better performance in predicting NCRT sensitivity. These findings highlight the clinical significance of metabolites variabilities in metabolomics analysis.

Altered gut microbiota correlates with behavioral problems but not gastrointestinal symptoms in individuals with autism

BACKGROUND

Despite inconsistent results across studies, emerging evidence suggests that the microbial micro-environment may be associated with autism spectrum disorder (ASD). Geographical and cultural factors highly impact microbial profiles, and there is a shortage of data from East Asian populations. This study aimed to comprehensively characterize microbial profiles in an East Asian sample and explore whether gut microbiota contributes to clinical symptoms, emotional/behavioral problems, and GI symptoms in ASD.

METHODS

We assessed 82 boys and young men with ASD and 31 typically developing controls (TDC), aged 6-25 years. We analyzed the stool sample of all participants with 16S V3-V4 rRNA sequencing and correlated its profile with GI symptoms, autistic symptoms, and emotional/behavioral problems.

RESULTS

Autistic individuals, compared to TDC, had worse GI symptoms. There were no group differences in alpha diversity of species richness estimates (Shannon-wiener and Simpson diversity indices). Participants with ASD had an increased relative abundance of Fusobacterium, Ruminococcus torques group (at the genus level), and Bacteroides plebeius DSM 17135 (at the species level), while a decreased relative abundance of Ruminococcaceae UCG 013, Ervsipelotrichaceae UCG 003, Parasutterella, Clostridium sensu stricto 1, Turicibacter (at the genus level), and Clostridium spiroforme DSM 1552 and Intestinimonas butyriciproducens (at the species level). Altered taxonomic diversity in ASD significantly correlated with autistic symptoms, thought problems, delinquent behaviors, self dysregulation, and somatic complaints. We did not find an association between gut symptoms and gut microbial dysbiosis.

CONCLUSIONS

Our findings suggest that altered microbiota are associated with behavioral phenotypes but not GI symptoms in ASD. The function of the identified microbial profiles mainly involves the immune pathway, supporting the hypothesis of a complex relationship between altered microbiome, immune dysregulation, and ASD that may advance the discovery of molecular biomarkers for ASD.

Maternal Serum and Placental Metabolomes in Association with Prenatal Phthalate Exposure and Neurodevelopmental Outcomes in the MARBLES Cohort

Prenatal exposure to phthalates, a family of endocrine-disrupting plasticizers, is associated with disruption of maternal metabolism and impaired neurodevelopment. We investigated associations between prenatal phthalate exposure and alterations of both the maternal third trimester serum metabolome and the placental metabolome at birth, and associations of these with child neurodevelopmental outcomes using data and samples from the Markers of Autism Risk in Babies Learning Early Signs (MARBLES) cohort. The third trimester serum (n = 106) and placental (n = 132) metabolomes were investigated using 1H nuclear magnetic resonance spectroscopy. Children were assessed clinically for autism spectrum disorder (ASD) and cognitive development. Although none of the urinary phthalate metabolite concentrations were associated with maternal serum metabolites after adjustment for covariates, mixture analysis using quantile g-computation revealed alterations in placental metabolites with increasing concentrations of phthalate metabolites that included reduced concentrations of 2-hydoxybutyrate, carnitine, O-acetylcarnitine, glucitol, and N-acetylneuraminate. Child neurodevelopmental outcome was not associated with the third trimester serum metabolome, but it was correlated with the placental metabolome in male children only. Maternal phthalate exposure during pregnancy is associated with differences in the placental metabolome at delivery, and the placental metabolome is associated with neurodevelopmental outcomes in males in a cohort with high familial ASD risk.

Proteomic phenotype of cerebral organoids derived from autism spectrum disorder patients reveal disrupted energy metabolism, cellular components, and biological processes

The way in which brain morphology and proteome are remodeled during embryonal development, and how they are linked to the cellular metabolism, could be a key for elucidating the pathological mechanisms of certain neurodevelopmental disorders. Cerebral organoids derived from autism spectrum disorder (ASD) patients were generated to capture critical time-points in the neuronal development, and metabolism and protein expression were investigated. The early stages of development, when neurogenesis commences (day in vitro 39), appeared to be a critical timepoint in pathogenesis. In the first month of development, increased size in ASD-derived organoids were detected in comparison to the controls. The size of the organoids correlates with the number of proliferating cells (Ki-67 positive cells). A significant difference in energy metabolism and proteome phenotype was also observed in ASD organoids at this time point, specifically, prevalence of glycolysis over oxidative phosphorylation, decreased ATP production and mitochondrial respiratory chain activity, differently expressed cell adhesion proteins, cell cycle (spindle formation), cytoskeleton, and several transcription factors. Finally, ASD patients and controls derived organoids were clustered based on a differential expression of ten proteins-heat shock protein 27 (hsp27) phospho Ser 15, Pyk (FAK2), Elk-1, Rac1/cdc42, S6 ribosomal protein phospho Ser 240/Ser 244, Ha-ras, mTOR (FRAP) phospho Ser 2448, PKCα, FoxO3a, Src family phospho Tyr 416-at day 39 which could be defined as potential biomarkers and further investigated for potential drug development.

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.

Association between EEG Paroxysmal Abnormalities and Levels of Plasma Amino Acids and Urinary Organic Acids in Children with Autism Spectrum Disorder

Abnormalities in the plasma amino acid and/or urinary organic acid profile have been reported in autism spectrum disorder (ASD). An imbalance between excitatory and inhibitory neuronal activity has been proposed as a mechanism to explain dysfunctional brain networks in ASD, as also suggested by the increased risk of epilepsy in this disorder. This study explored the possible association between presence of EEG paroxysmal abnormalities and the metabolic profile of plasma amino acids and urinary organic acids in children with ASD. In a sample of 55 children with ASD (81.8% male, mean age 53.67 months), EEGs were recorded, and 24 plasma amino acids and 56 urinary organic acids analyzed. EEG epileptiform discharges were found in 36 (65%) children. A LASSO regression, adjusted by age and sex, was applied to evaluate the association of plasma amino acids and urinary organic acids profiles with the presence of EEG epileptiform discharges. Plasma levels of threonine (THR) (coefficient = -0.02, p = 0.04) and urinary concentration of 3-Hydroxy-3-Methylglutaric acid (HMGA) (coefficient = 0.04, p = 0.02) were found to be associated with the presence of epileptiform discharges. These results suggest that altered redox mechanisms might be linked to epileptiform brain activity in ASD.

Distinct metabolic profiles associated with autism spectrum disorder versus cancer in individuals with germline PTEN mutations

PTEN hamartoma tumor syndrome (PHTS), caused by germline PTEN mutations, has been associated with organ-specific cancers and autism spectrum disorder (ASD) and/or developmental delay (DD). Predicting precise clinical phenotypes in any one PHTS individual remains impossible. We conducted an untargeted metabolomics study on an age- and sex-matched series of PHTS individuals with ASD/DD, cancer, or both phenotypes. Using agnostic metabolomic-analyses from patient-derived lymphoblastoid cells and their spent media, we found 52 differentially abundant individual metabolites, 69 cell/media metabolite ratios, and 327 pair-wise metabotype (shared metabolic phenotype) ratios clearly distinguishing PHTS individuals based on phenotype. Network analysis based on significant metabolites pointed to hubs converging on PTEN-related insulin, MAPK, AMPK, and mTOR signaling cascades. Internal cross-validation of significant metabolites showed optimal overall accuracy in distinguishing PHTS individuals with ASD/DD versus those with cancer. Such metabolomic markers may enable more accurate risk predictions and prevention in individual PHTS patients at highest risk.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Altered Metabolic Characteristics in Plasma of Young Boys with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a serious neurodevelopmental disorder with no clinical biomarker. This study used untargeted metabolomic analysis to identify metabolic characteristics in plasma that can distinguish ASD children. 29 boys with ASD (3.02 ± 0.67 years) and 30 typically developing (TD) boys (3.13 ± 0.46 years) were recruited. Developmental and behavioral assessments were conducted in ASD group. Samples of plasma were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The association between metabolite concentration and scale score was assessed by Spearman rank correlation. Altered metabolic characteristics were found in boys with ASD. In Receiver Operating Characteristic (ROC) analysis, ornithine had the highest AUC (Area under ROC) value. Furthermore, the concentration of choline and ornithine was negatively correlated with ABC-language score in ASD group.

Reduced false positives in autism screening via digital biomarkers inferred from deep comorbidity patterns

Here, we develop digital biomarkers for autism spectrum disorder (ASD), computed from patterns of past medical encounters, identifying children at high risk with an area under the receiver operating characteristic exceeding 80% from shortly after 2 years of age for either sex, and across two independent patient databases. We leverage uncharted ASD comorbidities, with no requirement of additional blood work, or procedures, to estimate the autism comorbid risk score (ACoR), during the earliest years when interventions are the most effective. ACoR has superior predictive performance to common questionnaire-based screenings and can reduce their current socioeconomic, ethnic, and demographic biases. In addition, we can condition on current screening scores to either halve the state-of-the-art false-positive rate or boost sensitivity to over 60%, while maintaining specificity above 95%. Thus, ACoR can significantly reduce the median diagnostic age, reducing diagnostic delays and accelerating access to evidence-based interventions.

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.

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.

DNA Methylation of PGC-1α Is Associated With Elevated mtDNA Copy Number and Altered Urinary Metabolites in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex disorder that is underpinned by numerous dysregulated biological pathways, including pathways that affect mitochondrial function. Epigenetic mechanisms contribute to this dysregulation and DNA methylation is an important factor in the etiology of ASD. We measured DNA methylation of peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1α), as well as five genes involved in regulating mitochondrial homeostasis to examine mitochondrial dysfunction in an ASD cohort of South African children. Using targeted Next Generation bisulfite sequencing, we found differential methylation (p < 0.05) at six key genes converging on mitochondrial biogenesis, fission and fusion in ASD, namely PGC-1α, STOML2, MFN2, FIS1, OPA1, and GABPA. PGC-1α, the transcriptional regulator of biogenesis, was significantly hypermethylated at eight CpG sites in the gene promoter, one of which contained a putative binding site for CAMP response binding element 1 (CREB1) (p = 1 × 10^–6). Mitochondrial DNA (mtDNA) copy number, a marker of mitochondrial function, was elevated (p = 0.002) in ASD compared to controls and correlated significantly with DNA methylation at the PGC-1α promoter and there was a positive correlation between methylation at PGC-1α CpG#1 and mtDNA copy number (Spearman’s r = 0.2, n = 49, p = 0.04) in ASD. Furthermore, DNA methylation at PGC-1α CpG#1 and mtDNA copy number correlated significantly (p < 0.05) with levels of urinary organic acids associated with mitochondrial dysfunction, oxidative stress, and neuroendocrinology. Our data show differential methylation in ASD at six key genes converging on PGC-1α-dependent regulation of mitochondrial biogenesis and function. We demonstrate that methylation at the PGC-1α promoter is associated with elevated mtDNA copy number and metabolomic evidence of mitochondrial dysfunction in ASD. This highlights an unexplored role for DNA methylation in regulating specific pathways involved in mitochondrial biogenesis, fission and fusion contributing to mitochondrial dysfunction in ASD.

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.

Cross-level analysis of molecular and neurobehavioral function in a prospective series of patients with germline heterozygous PTEN mutations with and without autism

Background

PTEN is a well-established risk gene for autism spectrum disorder (ASD). Yet, little is known about how PTEN mutations and associated molecular processes influence neurobehavioral function in mutation carriers with (PTEN-ASD) and without ASD (PTEN no-ASD). The primary aim of the present study was to examine group differences in peripheral blood-derived PTEN pathway protein levels between PTEN-ASD, PTEN no-ASD, and idiopathic macrocephalic ASD patients (macro-ASD). Secondarily, associations between protein levels and neurobehavioral functions were examined in the full cohort.

Methods

Patients were recruited at four tertiary medical centers. Peripheral blood-derived protein levels from canonical PTEN pathways (PI3K/AKT and MAPK/ERK) were analyzed using Western blot analyses blinded to genotype and ASD status. Neurobehavioral measures included standardized assessments of global cognitive ability and multiple neurobehavioral domains. Analysis of variance models examined group differences in demographic, neurobehavioral, and protein measures. Bivariate correlations, structural models, and statistical learning procedures estimated associations between molecular and neurobehavioral variables. To complement patient data, Western blots for downstream proteins were generated to evaluate canonical PTEN pathways in the PTEN-m3m4 mouse model.

Results

Participants included 61 patients (25 PTEN-ASD, 16 PTEN no-ASD, and 20 macro-ASD). Decreased PTEN and S6 were observed in both PTEN mutation groups. Reductions in MnSOD and increases in P-S6 were observed in ASD groups. Elevated neural P-AKT/AKT and P-S6/S6 from PTEN murine models parallel our patient observations. Patient PTEN and AKT levels were independently associated with global cognitive ability, and p27 expression was associated with frontal sub-cortical functions. As a group, molecular measures added significant predictive value to several neurobehavioral domains over and above PTEN mutation status.

Limitations

Sample sizes were small, precluding within-group analyses. Protein and neurobehavioral data were limited to a single evaluation. A small number of patients were excluded with invalid protein data, and cognitively impaired patients had missing data on some assessments.

Conclusions

Several canonical PTEN pathway molecules appear to influence the presence of ASD and modify neurobehavioral function in PTEN mutation patients. Protein assays of the PTEN pathway may be useful for predicting neurobehavioral outcomes in PTEN patients. Future longitudinal analyses are needed to replicate these findings and evaluate within-group relationships between protein and neurobehavioral measures.

Trial registration

ClinicalTrials.gov Identifier NCT02461446

New Strategies for Clinical Trials in Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that poses several challenges in terms of clinical diagnosis and investigation of molecular etiology. The lack of knowledge on the pathogenic mechanisms underlying ASD has hampered the clinical trials that so far have tried to target ASD behavioral symptoms. In order to improve our understanding of the molecular abnormalities associated with ASD, a deeper and more extensive genetic profiling of targeted individuals with ASD was needed.

METHODS

The recent availability of new and more powerful sequencing technologies (third-generation sequencing) has allowed to develop novel strategies for the characterization of comprehensive genetic profiles of individuals with ASD. In particular, this review will describe integrated approaches based on the combination of various omics technologies that will lead to a better stratification of targeted cohorts for the design of clinical trials in ASD.

RESULTS

In order to analyze the big data collected by assays such as the whole genome, epigenome, transcriptome, and proteome, it is critical to develop an efficient computational infrastructure. Machine learning models are instrumental to identify non-linear relationships between the omics technologies and, therefore, establish a functional informative network among the different data sources.

CONCLUSION

The potential advantage provided by these new integrated omics-based strategies is better characterization of the genetic background of ASD cohorts, to identify novel molecular targets for drug development, and ultimately offer a more personalized approach in the design of clinical trials for ASD.