Towards the Development of a Diagnostic Test for Autism Spectrum Disorder: Big Data Meets Metabolomics.

Autism spectrum disorder (ASD) is defined as a neurodevelopmental disorder which results in impairments in social communications and interactions as well as repetitive behaviors. Despite current estimates showing that approximately 2.2% of children are affected in the United States, relatively little about ASD pathophysiology is known in part due to the highly heterogenous presentation of the disorder. Given the limited knowledge into the biological mechanisms governing its etiology, the diagnosis of ASD is performed exclusively based on an individual's behavior assessed by a clinician through psychometric tools. Although there is no readily available biochemical test for ASD diagnosis, multivariate statistical methods show considerable potential for effectively leveraging multiple biochemical measurements for classification and characterization purposes. In this work, markers associated with the folate dependent one-carbon metabolism and transulfuration (FOCM/TS) pathways analyzed via both Fisher Discriminant Analysis and Support Vector Machine showed strong capability to distinguish between ASD and TD cohorts. Furthermore, using Kernel Partial Least Squares regression it was possible to assess some degree of behavioral severity from metabolomic data. While the results presented need to be replicated in independent future studies, they represent a promising avenue for uncovering clinically relevant ASD biomarkers.

Shotgun Metagenomics Study Suggests Alteration in Sulfur Metabolism and Oxidative Stress in Children with Autism and Improvement after Microbiota Transfer Therapy.

Links between gut microbiota and autism spectrum disorder (ASD) have been explored in many studies using 16S rRNA gene amplicon and shotgun sequencing. Based on these links, microbiome therapies have been proposed to improve gastrointestinal (GI) and ASD symptoms in ASD individuals. Previously, our open-label microbiota transfer therapy (MTT) study provided insight into the changes in the gut microbial community of children with ASD after MTT and showed significant and long-term improvement in ASD and GI symptoms. Using samples from the same study, the objective of this work was to perform a deeper taxonomic and functional analysis applying shotgun metagenomic sequencing. Taxonomic analyses revealed that ASD Baseline had many bacteria at lower relative abundances, and their abundance increased after MTT. The relative abundance of fiber consuming and beneficial microbes including Prevotella (P. dentalis, P. enoeca, P. oris, P. meloninogenica), Bifidobacterium bifidum, and a sulfur reducer Desulfovibrio piger increased after MTT-10wks in children with ASD compared to Baseline (consistent at genus level with the previous 16S rRNA gene study). Metabolic pathway analysis at Baseline compared to typically developing (TD) children found an altered abundance of many functional genes but, after MTT, they became similar to TD or donors. Important functional genes that changed included: genes encoding enzymes involved in folate biosynthesis, sulfur metabolism and oxidative stress. These results show that MTT treatment not only changed the relative abundance of important genes involved in metabolic pathways, but also seemed to bring them to a similar level to the TD controls. However, at a two-year follow-up, the microbiota and microbial genes shifted into a new state, distinct from their levels at Baseline and distinct from the TD group. Our current findings suggest that microbes from MTT lead to initial improvement in the metabolic profile of children with ASD, and major additional changes at two years post-treatment. In the future, larger cohort studies, mechanistic in vitro experiments and metatranscriptomics studies are recommended to better understand the role of these specific microbes, functional gene expression, and metabolites relevant to ASD.

A Novel Panel of 80 RNA Biomarkers with Differential Expression in Multiple Human Solid Tumors against Healthy Blood Samples.

The aim of this study was to identify genes with higher expression in solid tumor cells by comparing human tumor biopsies with healthy blood samples using both in silico statistical analysis and experimental validations. This approach resulted in a novel panel of 80 RNA biomarkers with high discrimination power to detect circulating tumor cells in blood samples. To identify the 80 RNA biomarkers, Affymetrix HG-U133 plus 2.0 microarrays datasets were used to compare breast tumor tissue biopsies and breast cancer cell lines with blood samples from patients with conditions other than cancer. A total of 859 samples were analyzed at the discovery stage, consisting of 417 mammary tumors, 41 breast lines, and 401 control samples. To confirm this discovery, external datasets of eight types of tumors were used, and experimental validation studies (NanoString n-counter gene expression assay) were performed, totaling 5028 samples analyzed. In these analyses, the 80 biomarkers showed higher expression in all solid tumors analyzed relative to healthy blood samples. Experimental validation studies using NanoString assay confirmed the results were not dependent of the gene expression platform. A panel of 80 RNA biomarkers was described here, with the potential to detect solid tumor cells present in the blood of multiple tumor types.

Impact of Suramin on Key Pathological Features of Sporadic Alzheimer's Disease-Derived Forebrain Neurons.

Background: Alzheimer's disease (AD) is characterized by disrupted proteostasis and macroautophagy (hereafter "autophagy"). The pharmacological agent suramin has known autophagy modulation properties with potential efficacy in mitigating AD neuronal pathology. Objective: In the present work, we investigate the impact of forebrain neuron exposure to suramin on the Akt/mTOR signaling pathway, a major regulator of autophagy, in comparison with rapamycin and chloroquine. We further investigate the effect of suramin on several AD-related biomarkers in sporadic AD (sAD)-derived forebrain neurons. Methods: Neurons differentiated from ReNcell neural progenitors were used to assess the impact of suramin on the Akt/mTOR signaling pathway relative to the autophagy inducer rapamycin and autophagy inhibitor chloroquine. Mature forebrain neurons were differentiated from induced pluripotent stem cells (iPSCs) sourced from a late-onset sAD patient and treated with 100µM suramin for 72 h, followed by assessments for amyloid-ß, phosphorylated tau, oxidative/nitrosative stress, and synaptic puncta density. Results: Suramin treatment of sAD-derived neurons partially ameliorated the increased p-Tau(S199)/Tau ratio, and fully remediated the increased glutathione to oxidized nitric oxide ratio, observed in untreated sAD-derived neurons relative to healthy controls. These positive results may be due in part to the distinct increases in Akt/mTOR pathway mediator p-p70S6K noted with suramin treatment of both ReNcell-derived and iPSC-derived neurons. Longer term neuronal markers, such as synaptic puncta density, were unaffected by suramin treatment. Conclusions: These findings provide initial evidence supporting the potential of suramin to reduce the degree of dysregulation in sAD-derived forebrain neurons in part via the modulation of autophagy.

Multivariate Analysis of Metabolomic and Nutritional Profiles among Children with Autism Spectrum Disorder.

There have been promising results regarding the capability of statistical and machine-learning techniques to offer insight into unique metabolomic patterns observed in ASD. This work re-examines a comparative study contrasting metabolomic and nutrient measurements of children with ASD (n = 55) against their typically developing (TD) peers (n = 44) through a multivariate statistical lens. Hypothesis testing, receiver characteristic curve assessment, and correlation analysis were consistent with prior work and served to underscore prominent areas where metabolomic and nutritional profiles between the groups diverged. Improved univariate analysis revealed 46 nutritional/metabolic differences that were significantly different between ASD and TD groups, with individual areas under the receiver operator curve (AUROC) scores of 0.6-0.9. Many of the significant measurements had correlations with many others, forming two integrated networks of interrelated metabolic differences in ASD. The TD group had 189 significant correlation pairs between metabolites, vs. only 106 for the ASD group, calling attention to underlying differences in metabolic processes. Furthermore, multivariate techniques identified potential biomarker panels with up to six metabolites that were able to attain a predictive accuracy of up to 98% for discriminating between ASD and TD, following cross-validation. Assessing all optimized multivariate models demonstrated concordance with prior physiological pathways identified in the literature, with some of the most important metabolites for discriminating ASD and TD being sulfate, the transsulfuration pathway, uridine (methylation biomarker), and beta-amino isobutyrate (regulator of carbohydrate and lipid metabolism).

Effects of Stromal Cell Conditioned Medium and Antipurinergic Treatment on Macrophage Phenotype.

The immunomodulatory capacity of the human mesenchymal stromal cell (MSC) secretome has been a critical driver for the development of cell-free MSC products, such as conditioned medium (CM), for regenerative medicine applications. This is particularly true as cell-free MSC products present several advantages over direct autologous or allogeneic MSC delivery with respect to safety, manufacturability, and defined potency. Recently, significant effort has been placed into creating novel MSC CM formulations with an immunomodulatory capacity tailored for specific regenerative contexts. For instance, the immunoregulatory nature of MSC CM has previously been tuned through a number of cytokine-priming strategies. Herein, we propose an alternate method to tailor the immunomodulatory "phenotype" of cytokine-primed MSC CM through coupling with the pharmacological agent, suramin. Suramin interferes with the signaling of purines including extracellular adenosine triphosphate (ATP), which plays a critical role in the activation of the innate immune system after injury. Toward this end, human THP-1-derived macrophages were activated to a proinflammatory phenotype and treated with (1) unprimed/native MSC CM, (2) interferon-γ/tumor necrosis factor α-primed MSC CM (primed CM), (3) suramin alone, or (4) primed MSC CM and suramin (primed CM/suramin). Markers of key macrophage functions-cytokine secretion, autophagy, oxidative stress modulation, and activation/migration-were assessed. Consistent with previous literature, primed CM elevated macrophage secretion of several proinflammatory and pleiotropic cytokines relative to native CM; whereas addition of suramin imparted consistent shifts in terms of TNFα (↓), interleukin-10 (↓), and hepatocyte growth factor (↑) irrespective of CM. In addition, both primed CM and suramin, individually and combined, increased reactive oxygen species production relative to native CM, and addition of suramin to primed CM shifted levels of CX3CL1, a factor involved in ATP-associated macrophage regulation. Varimax rotation assessment of the secreted cytokine profiles confirmed that primed CM/suramin resulted in a THP-1 phenotypic shift away from the lipopolysaccharide-activated proinflammatory state that was distinct from that of primed CM or native CM alone. This altered primed CM/suramin-associated phenotype may prove beneficial for healing in certain regenerative contexts. These results may inform future work coupling antipurinergic treatments with MSC-derived therapies in regenerative medicine applications.

Urinary Essential Elements of Young Children with Autism Spectrum Disorder and their Mothers.

BACKGROUND: Even though the cause of autism spectrum disorders (ASD) remains unknown, the current understanding points towards complex interactions between environmental and genetic factors. One important environmental factor to consider is intake of toxic and essential elements, and their role in metabolism. Essential elements have received considerably less attention in the literature than the presence of toxins in urine. METHOD: The purpose of this investigation is to comprehensively assess the association between urinary element compositions of 28 mothers who had young children with ASD and 29 mothers who had young typically developing (TD) children, and in a subset of their children (21 with ASD and 26 TD). RESULTS: The results show that there are significant differences between the ASD and TD children cohorts' concentrations for four specific elements (sulfur, phosphorous, molybdenum, and tin). Utilizing multivariate statistical techniques (Fisher's discriminant analysis and support vector machines), it was possible to distinguish the ASD from the TD children groups with an 81% accuracy after cross-validation utilizing the four significantly different elements. However, among the mother cohorts assessed, there were no significant differences between those that had children with ASD and those with TD children. There was a significant correlation of levels of phosphorus and sulfur in the children with ASD (r = 0.63, p = 3.0E-3) and in the TD children (r = 0.47, p = 0.02). CONCLUSIONS: Notable differences were observed between the elemental concentration in urine of children with ASD and their TD peers. Analyzing cellular pathways related to these elements are promising areas of future research.

Multivariate Analysis of Fecal Metabolites from Children with Autism Spectrum Disorder and Gastrointestinal Symptoms before and after Microbiota Transfer Therapy.

Fecal microbiota transplant (FMT) holds significant promise for patients with Autism Spectrum Disorder (ASD) and gastrointestinal (GI) symptoms. Prior work has demonstrated that plasma metabolite profiles of children with ASD become more similar to those of their typically developing (TD) peers following this treatment. This work measures the concentration of 669 biochemical compounds in feces of a cohort of 18 ASD and 20 TD children using ultrahigh performance liquid chromatography-tandem mass spectroscopy. Subsequent measurements were taken from the ASD cohort over the course of 10-week Microbiota Transfer Therapy (MTT) and 8 weeks after completion of this treatment. Univariate and multivariate statistical analysis techniques were used to characterize differences in metabolites before, during, and after treatment. Using Fisher Discriminant Analysis (FDA), it was possible to attain multivariate metabolite models capable of achieving a sensitivity of 94% and a specificity of 95% after cross-validation. Observations made following MTT indicate that the fecal metabolite profiles become more like those of the TD cohort. There was an 82-88% decrease in the median difference of the ASD and TD group for the panel metabolites, and among the top fifty most discriminating individual metabolites, 96% report more comparable values following treatment. Thus, these findings are similar, although less pronounced, as those determined using plasma metabolites.