Mathematical modeling of the methionine cycle and transsulfuration pathway in individuals with autism spectrum disorder

Sulfur amino acid metabolism and related metabotypes of autism spectrum disorder: A review of biochemical evidence for a hypothesis

There are multiple lines of evidence for an impaired sulfur amino acid (SAA) metabolism in autism spectrum disorder (ASD). For instance, the concentrations of methionine, cysteine and S-adenosylmethionine (SAM) in body fluids of individuals with ASD is significantly lower while the concentration of S-adenosylhomocysteine (SAH) is significantly higher as compared to healthy individuals. Reduced methionine and SAM may reflect impaired remethylation pathway whereas increased SAH may reflect reduced S-adenosylhomocysteine hydrolase activity in the catabolic direction. Reduced SAM/SAH ratio reflects an impaired methylation capacity. We hypothesize multiple mechanisms to explain how the interplay of oxidative stress, neuroinflammation, mercury exposure, maternal use of valproate, altered gut microbiome and certain genetic variants may lead to these SAA metabotypes. Furthermore, we also propose a number of mechanisms to explain the metabolic consequences of abnormal SAA metabotypes. For instance in the brain, reduced SAM/SAH ratio will result in melatonin deficiency and hypomethylation of a number of biomolecules such as DNA, RNA and histones. In addition to previously proposed mechanisms, we propose that impaired activity of "radical SAM" enzymes will result in reduced endogenous lipoic acid synthesis, reduced molybdenum cofactor synthesis and impaired porphyrin metabolism leading to mitochondrial dysfunction, porphyrinuria and impaired sulfation capacity. Furthermore depletion of SAM may also lead to the disturbed mTOR signaling pathway in a subgroup of ASD. The proposed "SAM-depletion hypothesis" is an inclusive model to explain the relationship between heterogeneous risk factors and metabotypes observed in a subset of children with ASD.

The influence of choline treatment on behavioral and neurochemical autistic-like phenotype in Mthfr-deficient mice

Imbalanced one carbon metabolism and aberrant autophagy is robustly reported in patients with autism. Polymorphism in the gene methylenetetrahydrofolate reductase (Mthfr), encoding for a key enzyme in this pathway is associated with an increased risk for autistic-spectrum-disorders (ASDs). Autistic-like core and associated behaviors have been described, with contribution of both maternal and offspring Mthfr^+/- genotype to the different domains of behavior. Preconception and prenatal supplementation with methyl donor rich diet to human subjects and mice reduced the risk for developing autism and autistic-like behavior, respectively. Here we tested the potential of choline supplementation to Mthfr-deficient mice at young-adulthood to reduce behavioral and neurochemical changes reminiscent of autism characteristics. We show that offspring of Mthfr^+/- mothers, whether wildtype or heterozygote, exhibit autistic-like behavior, altered brain p62 protein levels and LC3-II/LC3-I levels ratio, both, autophagy markers. Choline supplementation to adult offspring of Mthfr^+/- mothers for 14 days counteracted characteristics related to repetitive behavior and anxiety both in males and in females and improved social behavior solely in male mice. Choline treatment also normalized deviant cortical levels of the autophagy markers measured in male mice. The results demonstrate that choline supplementation even at adulthood, not tested previously, to offspring of Mthfr-deficient mothers, attenuates the autistic-like phenotype. If this proof of concept is replicated it might promote translation of these results to treatment recommendation for children with ASDs bearing similar genetic/metabolic make-up.

Blood homocysteine levels in children with autism spectrum disorder: An updated systematic review and meta-analysis

Results of studies on peripheral blood levels of homocysteine (Hcy) in children with autism spectrum disorder (ASD) are inconsistent, and conclusions from two previous meta-analyses on this subject published in 2012 are already outdated. Therefore, we conducted an updated systematic review and meta-analysis to quantitatively summarize the peripheral blood Hcy data in children with ASD compared with healthy controls (HC). We searched PubMed, EMBASE, PsycINFO, PsycARTICLES, Web of Science, and Cochrane Library databases from inception to September 2019 for eligible studies, with no language restriction. Using random-effects model, we computed summary statistics. Thirty-one studies (3304 participants including 1641 cases) were included. The pooled results showed that the peripheral blood Hcy levels were significantly elevated in children with ASD when compared to HC (Hedges's g = 0.56, 95% CI = 0.36 to 0.76, P < 0.001). By sensitivity analyses, we confirmed that our results were quite robust. Additionally, no publication bias was observed in this meta-analysis. In conclusion, our study support the association of increased circulating Hcy levels with ASD in children, and the involvement of Hcy in the occurrence of ASD. However, in view of the significant between-study heterogeneity, the conclusions should be interpreted cautiously and more investigation is required.

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

The Key Role of Purine Metabolism in the Folate-Dependent Phenotype of Autism Spectrum Disorders: An In Silico Analysis

Folate deficiency in the critical developmental period has been repeatedly associated with an increased risk of Autism spectrum disorders (ASD), but the key pathophysiological mechanism has not yet been identified. In this work, we focused on identifying genes whose defect has similar consequences to folate depletion in the metabolic network. Within the Flux Balance Analysis (FBA) framework, we developed a method of blocked metabolites that allowed us to define the metabolic consequences of various gene defects and folate depletion. We identified six genes (GART, PFAS, PPAT, PAICS, ATIC, and ADSL) whose blocking results in nearly the same effect in the metabolic network as folate depletion. All of these genes form the purine biosynthetic pathway. We found that, just like folate depletion, the blockade of any of the six genes mentioned above results in a blockage of purine metabolism. We hypothesize that this can lead to decreased adenosine triphosphate (ATP) and subsequently, an S-adenosyl methionine (SAM) pool in neurons in the case of rapid cell division. Based on our results, we consider the methylation defect to be a potential cause of ASD, due to the depletion of purine, and consequently S-adenosyl methionine (SAM), biosynthesis.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Prenatal Nutritional Intervention Reduces Autistic-Like Behavior Rates Among Mthfr-Deficient Mice

The causes and contributing factors of autism spectrum disorders (ASD) are poorly understood. One gene associated with increased risk for ASD is methylenetetrahydrofolate-reductase (MTHFR), which encodes a key enzyme in one carbon (C1) metabolism. The MTHFR 677C > T polymorphism reduces the efficiency of methyl group production with possible adverse downstream effects on gene expression. In this study, the effects of prenatal and/or postnatal diets enriched in C1 nutrients on ASD-like behavior were evaluated in Mthfr-deficient mice. Differences in intermediate pathways between the mice with and without ASD-like behaviors were tested. The findings indicate that maternal and offspring Mthfr deficiency increased the risk for an ASD-like phenotype in the offspring. The risk of ASD-like behavior was reduced in Mthfr-deficient mice supplemented with C1 nutrients prenatally. Specifically, among offspring of Mthfr+/- dams, prenatal diet supplementation was protective against ASD-like symptomatic behavior compared to the control diet with an odds ratio of 0.18 (CI:0.035, 0.970). Changes in major C1 metabolites, such as the ratios between betaine/choline and SAM/SAH in the cerebral-cortex, were associated with ASD-like behavior. Symptomatic mice presenting ASD-like behavior showed decreased levels of GABA pathway proteins such as GAD65/67 and VGAT and altered ratios of the glutamate receptor subunits GluR1/GluR2 in males and NR2A/NR2B in females. The altered ratios, in turn, favor receptor subunits with higher sensitivity to neuronal activity. Our study suggests that MTHFR deficiency can increase the risk of ASD-like behavior in mice and that prenatal dietary intervention focused on MTHFR genotypes can reduce the risk of ASD-like behavior.

Comparison of Three Clinical Trial Treatments for Autism Spectrum Disorder Through Multivariate Analysis of Changes in Metabolic Profiles and Adaptive Behavior

Several studies associate autism spectrum disorder (ASD) pathophysiology with metabolic abnormalities related to DNA methylation and intracellular redox homeostasis. In this regard, three completed clinical trials are reexamined in this work: treatment with (i) methylcobalamin (MeCbl) in combination with low-dose folinic acid (LDFA), (ii) tetrahydrobiopterin, and (iii) high-dose folinic acid (HDFA) for counteracting abnormalities in the folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathways and also for improving ASD-related symptoms and behaviors. Although effects of treatment on individual metabolites and behavioral measures have previously been investigated, this study is the first to consider the effect of interventions on a set of metabolites of the FOCM/TS pathways and to correlate FOCM/TS metabolic changes with behavioral improvements across several studies. To do so, this work uses data from one case–control study and the three clinical trials to develop multivariate models for considering these aspects of treatment. Fisher discriminant analysis (FDA) is first used to establish a model for distinguishing individuals with ASD from typically developing (TD) controls, which is subsequently evaluated on the three treatment data sets, along with one data set for a placebo, to characterize the shift of FOCM/TS metabolism toward that of the TD population. Treatment with MeCbl plus LDFA and, separately, treatment with tetrahydrobiopterin significantly shifted the metabolites toward the values of the control group. Contrary to this, treatment with HDFA had a lesser, though still noticeable, effect whilst the placebo group showed marginal, but not insignificant, variations in metabolites. A second analysis is then performed with non-linear kernel partial least squares (KPLS) regression to predict changes in adaptive behavior, quantified by the Vineland Adaptive Behavior Composite, from changes in FOCM/TS biochemical measurements provided by treatment. Incorporating the 74 samples receiving any treatment, including placebo, into the regression analysis yields an R² of 0.471 after cross-validation when using changes in six metabolic measurements as predictors. These results are suggestive of an ability to effectively improve pathway-wide FOCM/TS metabolic and behavioral abnormalities in ASD with clinical treatment.

Role of lncRNAs as prognostic markers of hepatic cancer and potential therapeutic targeting by S-adenosylmethionine via inhibiting PI3K/Akt signaling pathways

Hepatic cancer (HCC) is a well-identified dilemma throughout the world, and hence, the molecular mechanisms and strategy for preventive protection against this malignancy are critical. S-adenosylmethionine (SAM) is a unique methyl granter in vast reactions, including DNA methylation, and secures the genome against hypomethylation, which is a hallmark of tumors. Consequently, SAM may control the rate of gene expression. The objective of this investigation was to evaluate the expression of long noncoding RNAs (lncRNAs) transcript involved in hepatic tumorigenesis, including additional coding CEBPA (ecCEBPA) and urothelial carcinoma related 1 (UCA1), antioxidant enzymes transcripts, and relevant signaling pathway in diethylnitrosamine (DEN)-prompted HCC along with their conceivable targeting by SAM at different stages of HCC in rats. Our outcomes revealed that SAM particularly when given at the starting phase downregulates ecCEBPA and UCA1 gene transcripts and ameliorate histopathological alterations in DEN-initiated HCC. Interestingly, SAM attenuates DEN-induced upregulation of PI3K/Akt protein expression. However, SAM upregulates the antioxidant enzymes mRNA transcripts and effectively diminishing DNA oxidation. The results of a DNA fragmentation assay further support the capacity of SAM to ameliorate DEN-induced hepatic malignancy. These results revealed the role of ecCEBPA and UCA1 in HCC and suggest that these lncRNAs may be helpful as prognostic and analytical biomarkers of HCC. Curiously, SAM readily targets the studied genes via inhibiting PI3K/Akt signaling pathway, which should make SAM an appealing agent for both chemoprevention and treatment of HCC.

Investigating Plasma Amino Acids for Differentiating Individuals with Autism Spectrum Disorder and Typically Developing Peers

BACKGROUND

Plasma amino acid measurements have been extensively investigated in individuals with autism spectrum disorder (ASD). Results thus far have been inconclusive as studies generally disagree on which amino acids are different in individuals with ASD versus their typically developing (TD) peers, due in part to methodological limitations of several studies.

METHOD

This paper investigates plasma amino acids in children and adults with ASD using data from Arizona State University's Comprehensive Nutritional and Dietary Intervention Study. Measurements from 64 individuals with ASD and 49 TD controls were analyzed using univariate and multivariate statistical techniques.

RESULTS

Univariate analysis indicated increased median levels of glutamate (+21%, p=0.014) and serine (+8%, p=0.043), and increased mean levels of hydroxyproline (+17%, p=0.018) for the ASD cohort, although these differences were insignificant after correcting for multiple comparisons. A multivariate approach was used to classify study participants into ASD/TD cohorts using Fisher discriminant analysis (FDA) and its nonlinear extension, kernel Fisher discriminant analysis (KFDA). Model fitting with FDA using all available measurements produced Type I and Type II errors of 27.0% and 27.8%, respectively. KFDA was most effective when using hydroxyproline, leucine, and threonine as inputs; however, leave-one-out cross-validation with this nonlinear model only resulted in 70.3% sensitivity and 77.6% specificity.

CONCLUSIONS

The finding of elevated glutamate in ASD is in agreement with several other studies. Overall, however, these results suggest that plasma amino acid measurements are of limited use for purposes of ASD classification, which may explain some of the inconsistencies in results presented in the literature.

Novel biomarkers of metabolic dysfunction is autism spectrum disorder: potential for biological diagnostic markers

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is behaviorally defined by social and communication impairments and restricted interests and repetitive behaviors. There is currently no biomarkers that can help in the diagnosis. Several studies suggest that mitochondrial dysfunction is commonly involved in ASD pathophysiology, but standard mitochondrial biomarkers are thought to be very variable. In the present study we examine a wide variety of plasma biomarkers of mitochondrial metabolism and the related abnormalities of oxidative stress and apoptosis in 41 ASD patients assessed for ASD severity using the Childhood Autism Rating Scales and 41 non-related age and sex matched healthy controls. Our findings confirm previous studies indicating abnormal mitochondrial and related biomarkers in children with ASD including pyruvate, creatine kinase, Complex 1, Glutathione S-Transferase, glutathione and Caspase 7. As a novel finding, we report that lactate dehydrogenase is abnormal in children with ASD. We also identified that only the most severe children demonstrated abnormalities in Complex 1 activity and Glutathione S-Transferase. Additionally, we find that several biomarkers could be candidates for differentiating children with ASD and typically developing children, including Caspase 7, gluthatione and Glutathione S-Transferase by themselves and lactate dehydrogenase and Complex I when added to other biomarkers in combination. Caspase 7 was the most discriminating biomarker between ASD patients and healthy controls suggesting its potential use as diagnostic marker for the early recognition of ASD pathophysiology. This study confirms that several mitochondrial biomarkers are abnormal in children with ASD and suggest that certain mitochondrial biomarkers can differentiate between ASD and typically developing children, making them possibly useful as a tool to diagnosis ASD and identify ASD subgroups.

On the Use of Multivariate Methods for Analysis of Data from Biological Networks

Data analysis used for biomedical research, particularly analysis involving metabolic or signaling pathways, is often based upon univariate statistical analysis. One common approach is to compute means and standard deviations individually for each variable or to determine where each variable falls between upper and lower bounds. Additionally, p-values are often computed to determine if there are differences between data taken from two groups. However, these approaches ignore that the collected data are often correlated in some form, which may be due to these measurements describing quantities that are connected by biological networks. Multivariate analysis approaches are more appropriate in these scenarios, as they can detect differences in datasets that the traditional univariate approaches may miss. This work presents three case studies that involve data from clinical studies of autism spectrum disorder that illustrate the need for and demonstrate the potential impact of multivariate analysis.