Amino Acid Dysregulation Metabotypes: Potential Biomarkers for Diagnosis and Individualized Treatment for Subtypes of Autism Spectrum Disorder

BackgroundAutism spectrum disorder (ASD) is behaviorally and biologically heterogeneous and likely represents a series of conditions arising from different underlying genetic, metabolic, and environmental factors. There are currently no reliable diagnostic biomarkers for ASD. Based on evidence that dysregulation of branched-chain amino acids (BCAAs) may contribute to the behavioral characteristics of ASD, we tested whether dysregulation of amino acids (AAs) was a pervasive phenomenon in individuals with ASD. This is the first article to report results from the Children's Autism Metabolome Project (CAMP), a large-scale effort to define autism biomarkers based on metabolomic analyses of blood samples from young children.MethodsDysregulation of AA metabolism was identified by comparing plasma metabolites from 516 children with ASD with those from 164 age-matched typically developing children recruited into the CAMP. ASD subjects were stratified into subpopulations based on shared metabolic phenotypes associated with BCAA dysregulation.ResultsWe identified groups of AAs with positive correlations that were, as a group, negatively correlated with BCAA levels in ASD. Imbalances between these two groups of AAs identified three ASD-associated amino acid dysregulation metabotypes. The combination of glutamine, glycine, and ornithine amino acid dysregulation metabotypes identified a dysregulation in AA/BCAA metabolism that is present in 16.7% of the CAMP subjects with ASD and is detectable with a specificity of 96.3% and a positive predictive value of 93.5% within the ASD subject cohort.ConclusionsIdentification and utilization of metabotypes of ASD can lead to actionable metabolic tests that support early diagnosis and stratification for targeted therapeutic interventions

A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing - t4 report

Systemic toxicity testing forms the cornerstone for the safety evaluation of substances. Pressures to move from traditional animal models to novel technologies arise from various concerns, including: the need to evaluate large numbers of previously untested chemicals and new products (such as nanoparticles or cell therapies), the limited predictivity of traditional tests for human health effects, duration and costs of current approaches, and animal welfare considerations. The latter holds especially true in the context of the scheduled 2013 marketing ban on cosmetic ingredients tested for systemic toxicity. Based on a major analysis of the status of alternative methods (Adler et al., 2011) and its independent review (Hartung et al., 2011), the present report proposes a roadmap for how to overcome the acknowledged scientific gaps for the full replacement of systemic toxicity testing using animals. Five whitepapers were commissioned addressing toxicokinetics, skin sensitization, repeated-dose toxicity, carcinogenicity, and reproductive toxicity testing. An expert workshop of 35 participants from Europe and the US discussed and refined these whitepapers, which were subsequently compiled to form the present report. By prioritizing the many options to move the field forward, the expert group hopes to advance regulatory science

Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children

<div><p>Background</p><p>The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age.</p><p>Objectives</p><p>To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment.</p><p>Methods</p><p>Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD.</p><p>Results</p><p>A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set.</p><p>Conclusions</p><p>This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment recommendations.</p></div

Classifier performance metrics based on predictions on the independent 21-sample validation set, showing the feature sets with the highest accuracy.

<p>Feature No. corresponds to the number of the ordered, ranked VIP features that were evaluated. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112445#pone.0112445.s003" target="_blank">Table S3</a> shows the results for all feature sets.</p><p>Classifier performance metrics based on predictions on the independent 21-sample validation set, showing the feature sets with the highest accuracy.</p